/* Generated by Cython 0.21.1 */ #define PY_SSIZE_T_CLEAN #ifndef CYTHON_USE_PYLONG_INTERNALS #ifdef PYLONG_BITS_IN_DIGIT #define CYTHON_USE_PYLONG_INTERNALS 0 #else #include "pyconfig.h" #ifdef PYLONG_BITS_IN_DIGIT #define CYTHON_USE_PYLONG_INTERNALS 1 #else #define CYTHON_USE_PYLONG_INTERNALS 0 #endif #endif #endif #include "Python.h" #ifndef Py_PYTHON_H #error Python headers needed to compile C extensions, please install development version of Python. #elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03020000) #error Cython requires Python 2.6+ or Python 3.2+. #else #define CYTHON_ABI "0_21_1" #include #ifndef offsetof #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) #endif #if !defined(WIN32) && !defined(MS_WINDOWS) #ifndef __stdcall #define __stdcall #endif #ifndef __cdecl #define __cdecl #endif #ifndef __fastcall #define __fastcall #endif #endif #ifndef DL_IMPORT #define DL_IMPORT(t) t #endif #ifndef DL_EXPORT #define DL_EXPORT(t) t #endif #ifndef PY_LONG_LONG #define PY_LONG_LONG LONG_LONG #endif #ifndef Py_HUGE_VAL #define Py_HUGE_VAL HUGE_VAL #endif #ifdef PYPY_VERSION #define CYTHON_COMPILING_IN_PYPY 1 #define CYTHON_COMPILING_IN_CPYTHON 0 #else #define CYTHON_COMPILING_IN_PYPY 0 #define CYTHON_COMPILING_IN_CPYTHON 1 #endif #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 #define Py_OptimizeFlag 0 #endif #define __PYX_BUILD_PY_SSIZE_T "n" #define CYTHON_FORMAT_SSIZE_T "z" #if PY_MAJOR_VERSION < 3 #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \ PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #define __Pyx_DefaultClassType PyClass_Type #else #define __Pyx_BUILTIN_MODULE_NAME "builtins" #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \ PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #define __Pyx_DefaultClassType PyType_Type #endif #if PY_MAJOR_VERSION >= 3 #define Py_TPFLAGS_CHECKTYPES 0 #define Py_TPFLAGS_HAVE_INDEX 0 #define Py_TPFLAGS_HAVE_NEWBUFFER 0 #endif #if PY_VERSION_HEX < 0x030400a1 && !defined(Py_TPFLAGS_HAVE_FINALIZE) #define Py_TPFLAGS_HAVE_FINALIZE 0 #endif #if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) #define CYTHON_PEP393_ENABLED 1 #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ? \ 0 : _PyUnicode_Ready((PyObject *)(op))) #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) #else #define CYTHON_PEP393_ENABLED 0 #define __Pyx_PyUnicode_READY(op) (0) #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) #endif #if CYTHON_COMPILING_IN_PYPY #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) #define __Pyx_PyFrozenSet_Size(s) PyObject_Size(s) #else #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ? \ PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) #define __Pyx_PyFrozenSet_Size(s) PySet_Size(s) #endif #define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? 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PyMethod_New(func, self) : PyInstanceMethod_New(func)) #else #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) #endif #ifndef CYTHON_INLINE #if defined(__GNUC__) #define CYTHON_INLINE __inline__ #elif defined(_MSC_VER) #define CYTHON_INLINE __inline #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L #define CYTHON_INLINE inline #else #define CYTHON_INLINE #endif #endif #ifndef CYTHON_RESTRICT #if defined(__GNUC__) #define CYTHON_RESTRICT __restrict__ #elif defined(_MSC_VER) && _MSC_VER >= 1400 #define CYTHON_RESTRICT __restrict #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L #define CYTHON_RESTRICT restrict #else #define CYTHON_RESTRICT #endif #endif #ifdef NAN #define __PYX_NAN() ((float) NAN) #else static CYTHON_INLINE float __PYX_NAN() { /* Initialize NaN. The sign is irrelevant, an exponent with all bits 1 and a nonzero mantissa means NaN. If the first bit in the mantissa is 1, it is a quiet NaN. */ float value; memset(&value, 0xFF, sizeof(value)); return value; } #endif #ifdef __cplusplus template void __Pyx_call_destructor(T* x) { x->~T(); } #endif #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) #else #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) #endif #ifndef __PYX_EXTERN_C #ifdef __cplusplus #define __PYX_EXTERN_C extern "C" #else #define __PYX_EXTERN_C extern #endif #endif #if defined(WIN32) || defined(MS_WINDOWS) #define _USE_MATH_DEFINES #endif #include #define __PYX_HAVE__sklearn___tree #define __PYX_HAVE_API__sklearn___tree #include "string.h" #include "stdio.h" #include "stdlib.h" #include "numpy/arrayobject.h" #include "numpy/ufuncobject.h" #include "math.h" #include "pythread.h" #ifdef _OPENMP #include #endif /* _OPENMP */ #ifdef PYREX_WITHOUT_ASSERTIONS #define CYTHON_WITHOUT_ASSERTIONS #endif #ifndef CYTHON_UNUSED # if defined(__GNUC__) # if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) # define CYTHON_UNUSED __attribute__ ((__unused__)) # else # define CYTHON_UNUSED # endif # elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) # define CYTHON_UNUSED __attribute__ ((__unused__)) # else # define CYTHON_UNUSED # endif #endif typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding; const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; #define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 #define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 #define __PYX_DEFAULT_STRING_ENCODING "" #define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString #define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize #define __Pyx_fits_Py_ssize_t(v, type, is_signed) ( \ (sizeof(type) < sizeof(Py_ssize_t)) || \ (sizeof(type) > sizeof(Py_ssize_t) && \ likely(v < (type)PY_SSIZE_T_MAX || \ v == (type)PY_SSIZE_T_MAX) && \ (!is_signed || likely(v > (type)PY_SSIZE_T_MIN || \ v == (type)PY_SSIZE_T_MIN))) || \ (sizeof(type) == sizeof(Py_ssize_t) && \ (is_signed || likely(v < (type)PY_SSIZE_T_MAX || \ v == (type)PY_SSIZE_T_MAX))) ) static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*); static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); #define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) #define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) #define __Pyx_PyBytes_FromString PyBytes_FromString #define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); #if PY_MAJOR_VERSION < 3 #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize #else #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize #endif #define __Pyx_PyObject_AsSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_FromUString(s) __Pyx_PyObject_FromString((const char*)s) #define __Pyx_PyBytes_FromUString(s) __Pyx_PyBytes_FromString((const char*)s) #define __Pyx_PyByteArray_FromUString(s) __Pyx_PyByteArray_FromString((const char*)s) #define __Pyx_PyStr_FromUString(s) __Pyx_PyStr_FromString((const char*)s) #define __Pyx_PyUnicode_FromUString(s) __Pyx_PyUnicode_FromString((const char*)s) #if PY_MAJOR_VERSION < 3 static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) { const Py_UNICODE *u_end = u; while (*u_end++) ; return (size_t)(u_end - u - 1); } #else #define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen #endif #define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) #define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode #define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode #define __Pyx_Owned_Py_None(b) (Py_INCREF(Py_None), Py_None) #define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False)) static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x); static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); #if CYTHON_COMPILING_IN_CPYTHON #define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) #else #define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) #endif #define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII static int __Pyx_sys_getdefaultencoding_not_ascii; static int __Pyx_init_sys_getdefaultencoding_params(void) { PyObject* sys; PyObject* default_encoding = NULL; PyObject* ascii_chars_u = NULL; PyObject* ascii_chars_b = NULL; const char* default_encoding_c; sys = PyImport_ImportModule("sys"); if (!sys) goto bad; default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); Py_DECREF(sys); if (!default_encoding) goto bad; default_encoding_c = PyBytes_AsString(default_encoding); if (!default_encoding_c) goto bad; if (strcmp(default_encoding_c, "ascii") == 0) { __Pyx_sys_getdefaultencoding_not_ascii = 0; } else { char ascii_chars[128]; int c; for (c = 0; c < 128; c++) { ascii_chars[c] = c; } __Pyx_sys_getdefaultencoding_not_ascii = 1; ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); if (!ascii_chars_u) goto bad; ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { PyErr_Format( PyExc_ValueError, "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", default_encoding_c); goto bad; } Py_DECREF(ascii_chars_u); Py_DECREF(ascii_chars_b); } Py_DECREF(default_encoding); return 0; bad: Py_XDECREF(default_encoding); Py_XDECREF(ascii_chars_u); Py_XDECREF(ascii_chars_b); return -1; } #endif #if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) #else #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) #if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT static char* __PYX_DEFAULT_STRING_ENCODING; static int __Pyx_init_sys_getdefaultencoding_params(void) { PyObject* sys; PyObject* default_encoding = NULL; char* default_encoding_c; sys = PyImport_ImportModule("sys"); if (!sys) goto bad; default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); Py_DECREF(sys); if (!default_encoding) goto bad; default_encoding_c = PyBytes_AsString(default_encoding); if (!default_encoding_c) goto bad; __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c)); if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); Py_DECREF(default_encoding); return 0; bad: Py_XDECREF(default_encoding); return -1; } #endif #endif /* Test for GCC > 2.95 */ #if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) #define likely(x) __builtin_expect(!!(x), 1) #define unlikely(x) __builtin_expect(!!(x), 0) #else /* !__GNUC__ or GCC < 2.95 */ #define likely(x) (x) #define unlikely(x) (x) #endif /* __GNUC__ */ static PyObject *__pyx_m; static PyObject *__pyx_d; static PyObject *__pyx_b; static PyObject *__pyx_empty_tuple; static PyObject *__pyx_empty_bytes; static int __pyx_lineno; static int __pyx_clineno = 0; static const char * __pyx_cfilenm= __FILE__; static const char *__pyx_filename; #if !defined(CYTHON_CCOMPLEX) #if defined(__cplusplus) #define CYTHON_CCOMPLEX 1 #elif defined(_Complex_I) #define CYTHON_CCOMPLEX 1 #else #define CYTHON_CCOMPLEX 0 #endif #endif #if CYTHON_CCOMPLEX #ifdef __cplusplus #include #else #include #endif #endif #if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) #undef _Complex_I #define _Complex_I 1.0fj #endif static const char *__pyx_f[] = { "sklearn/_tree.pyx", "sklearn/_tree.pxd", "__init__.pxd", "type.pxd", "bool.pxd", "complex.pxd", }; #define IS_UNSIGNED(type) (((type) -1) > 0) struct __Pyx_StructField_; #define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) typedef struct { const char* name; struct __Pyx_StructField_* fields; size_t size; size_t arraysize[8]; int ndim; char typegroup; char is_unsigned; int flags; } __Pyx_TypeInfo; typedef struct __Pyx_StructField_ { __Pyx_TypeInfo* type; const char* name; size_t offset; } __Pyx_StructField; typedef struct { __Pyx_StructField* field; size_t parent_offset; } __Pyx_BufFmt_StackElem; typedef struct { __Pyx_StructField root; __Pyx_BufFmt_StackElem* head; size_t fmt_offset; size_t new_count, enc_count; size_t struct_alignment; int is_complex; char enc_type; char new_packmode; char enc_packmode; char is_valid_array; } __Pyx_BufFmt_Context; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":723 * # in Cython to enable them only on the right systems. * * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t */ typedef npy_int8 __pyx_t_5numpy_int8_t; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":724 * * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t */ typedef npy_int16 __pyx_t_5numpy_int16_t; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":725 * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< * ctypedef npy_int64 int64_t * #ctypedef npy_int96 int96_t */ typedef npy_int32 __pyx_t_5numpy_int32_t; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":726 * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< * #ctypedef npy_int96 int96_t * #ctypedef npy_int128 int128_t */ typedef npy_int64 __pyx_t_5numpy_int64_t; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":730 * #ctypedef npy_int128 int128_t * * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t */ typedef npy_uint8 __pyx_t_5numpy_uint8_t; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":731 * * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t */ typedef npy_uint16 __pyx_t_5numpy_uint16_t; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":732 * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< * ctypedef npy_uint64 uint64_t * #ctypedef npy_uint96 uint96_t */ typedef npy_uint32 __pyx_t_5numpy_uint32_t; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":733 * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< * #ctypedef npy_uint96 uint96_t * #ctypedef npy_uint128 uint128_t */ typedef npy_uint64 __pyx_t_5numpy_uint64_t; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":737 * #ctypedef npy_uint128 uint128_t * * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< * ctypedef npy_float64 float64_t * #ctypedef npy_float80 float80_t */ typedef npy_float32 __pyx_t_5numpy_float32_t; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":738 * * ctypedef npy_float32 float32_t * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< * #ctypedef npy_float80 float80_t * #ctypedef npy_float128 float128_t */ typedef npy_float64 __pyx_t_5numpy_float64_t; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":747 * # The int types are mapped a bit surprising -- * # numpy.int corresponds to 'l' and numpy.long to 'q' * ctypedef npy_long int_t # <<<<<<<<<<<<<< * ctypedef npy_longlong long_t * ctypedef npy_longlong longlong_t */ typedef npy_long __pyx_t_5numpy_int_t; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":748 * # numpy.int corresponds to 'l' and numpy.long to 'q' * ctypedef npy_long int_t * ctypedef npy_longlong long_t # <<<<<<<<<<<<<< * ctypedef npy_longlong longlong_t * */ typedef npy_longlong __pyx_t_5numpy_long_t; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":749 * ctypedef npy_long int_t * ctypedef npy_longlong long_t * ctypedef npy_longlong longlong_t # <<<<<<<<<<<<<< * * ctypedef npy_ulong uint_t */ typedef npy_longlong __pyx_t_5numpy_longlong_t; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":751 * ctypedef npy_longlong longlong_t * * ctypedef npy_ulong uint_t # <<<<<<<<<<<<<< * ctypedef npy_ulonglong ulong_t * ctypedef npy_ulonglong ulonglong_t */ typedef npy_ulong __pyx_t_5numpy_uint_t; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":752 * * ctypedef npy_ulong uint_t * ctypedef npy_ulonglong ulong_t # <<<<<<<<<<<<<< * ctypedef npy_ulonglong ulonglong_t * */ typedef npy_ulonglong __pyx_t_5numpy_ulong_t; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":753 * ctypedef npy_ulong uint_t * ctypedef npy_ulonglong ulong_t * ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<< * * ctypedef npy_intp intp_t */ typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":755 * ctypedef npy_ulonglong ulonglong_t * * ctypedef npy_intp intp_t # <<<<<<<<<<<<<< * ctypedef npy_uintp uintp_t * */ typedef npy_intp __pyx_t_5numpy_intp_t; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":756 * * ctypedef npy_intp intp_t * ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<< * * ctypedef npy_double float_t */ typedef npy_uintp __pyx_t_5numpy_uintp_t; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":758 * ctypedef npy_uintp uintp_t * * ctypedef npy_double float_t # <<<<<<<<<<<<<< * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t */ typedef npy_double __pyx_t_5numpy_float_t; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":759 * * ctypedef npy_double float_t * ctypedef npy_double double_t # <<<<<<<<<<<<<< * ctypedef npy_longdouble longdouble_t * */ typedef npy_double __pyx_t_5numpy_double_t; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":760 * ctypedef npy_double float_t * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< * * ctypedef npy_cfloat cfloat_t */ typedef npy_longdouble __pyx_t_5numpy_longdouble_t; /* "sklearn/_tree.pxd":14 * cimport numpy as np * * ctypedef np.npy_float32 DTYPE_t # Type of X # <<<<<<<<<<<<<< * ctypedef np.npy_float64 DOUBLE_t # Type of y, sample_weight * ctypedef np.npy_intp SIZE_t # Type for indices and counters */ typedef npy_float32 __pyx_t_7sklearn_5_tree_DTYPE_t; /* "sklearn/_tree.pxd":15 * * ctypedef np.npy_float32 DTYPE_t # Type of X * ctypedef np.npy_float64 DOUBLE_t # Type of y, sample_weight # <<<<<<<<<<<<<< * ctypedef np.npy_intp SIZE_t # Type for indices and counters * ctypedef np.npy_int32 INT32_t # Signed 32 bit integer */ typedef npy_float64 __pyx_t_7sklearn_5_tree_DOUBLE_t; /* "sklearn/_tree.pxd":16 * ctypedef np.npy_float32 DTYPE_t # Type of X * ctypedef np.npy_float64 DOUBLE_t # Type of y, sample_weight * ctypedef np.npy_intp SIZE_t # Type for indices and counters # <<<<<<<<<<<<<< * ctypedef np.npy_int32 INT32_t # Signed 32 bit integer * ctypedef np.npy_uint32 UINT32_t # Unsigned 32 bit integer */ typedef npy_intp __pyx_t_7sklearn_5_tree_SIZE_t; /* "sklearn/_tree.pxd":17 * ctypedef np.npy_float64 DOUBLE_t # Type of y, sample_weight * ctypedef np.npy_intp SIZE_t # Type for indices and counters * ctypedef np.npy_int32 INT32_t # Signed 32 bit integer # <<<<<<<<<<<<<< * ctypedef np.npy_uint32 UINT32_t # Unsigned 32 bit integer * */ typedef npy_int32 __pyx_t_7sklearn_5_tree_INT32_t; /* "sklearn/_tree.pxd":18 * ctypedef np.npy_intp SIZE_t # Type for indices and counters * ctypedef np.npy_int32 INT32_t # Signed 32 bit integer * ctypedef np.npy_uint32 UINT32_t # Unsigned 32 bit integer # <<<<<<<<<<<<<< * * */ typedef npy_uint32 __pyx_t_7sklearn_5_tree_UINT32_t; #if CYTHON_CCOMPLEX #ifdef __cplusplus typedef ::std::complex< float > __pyx_t_float_complex; #else typedef float _Complex __pyx_t_float_complex; #endif #else typedef struct { float real, imag; } __pyx_t_float_complex; #endif #if CYTHON_CCOMPLEX #ifdef __cplusplus typedef ::std::complex< double > __pyx_t_double_complex; #else typedef double _Complex __pyx_t_double_complex; #endif #else typedef struct { double real, imag; } __pyx_t_double_complex; #endif /*--- Type declarations ---*/ struct __pyx_obj_7sklearn_5_tree_Stack; struct __pyx_obj_7sklearn_5_tree_PriorityHeap; struct __pyx_obj_7sklearn_5_tree_Criterion; struct __pyx_obj_7sklearn_5_tree_Splitter; struct __pyx_obj_7sklearn_5_tree_Tree; struct __pyx_obj_7sklearn_5_tree_TreeBuilder; struct __pyx_obj_7sklearn_5_tree_ClassificationCriterion; struct __pyx_obj_7sklearn_5_tree_Entropy; struct __pyx_obj_7sklearn_5_tree_Gini; struct __pyx_obj_7sklearn_5_tree_RegressionCriterion; struct __pyx_obj_7sklearn_5_tree_MSE; struct __pyx_obj_7sklearn_5_tree_FriedmanMSE; struct __pyx_obj_7sklearn_5_tree_BaseDenseSplitter; struct __pyx_obj_7sklearn_5_tree_BestSplitter; struct __pyx_obj_7sklearn_5_tree_RandomSplitter; struct __pyx_obj_7sklearn_5_tree_PresortBestSplitter; struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter; struct __pyx_obj_7sklearn_5_tree_BestSparseSplitter; struct __pyx_obj_7sklearn_5_tree_RandomSparseSplitter; struct __pyx_obj_7sklearn_5_tree_DepthFirstTreeBuilder; struct __pyx_obj_7sklearn_5_tree_BestFirstTreeBuilder; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":762 * ctypedef npy_longdouble longdouble_t * * ctypedef npy_cfloat cfloat_t # <<<<<<<<<<<<<< * ctypedef npy_cdouble cdouble_t * ctypedef npy_clongdouble clongdouble_t */ typedef npy_cfloat __pyx_t_5numpy_cfloat_t; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":763 * * ctypedef npy_cfloat cfloat_t * ctypedef npy_cdouble cdouble_t # <<<<<<<<<<<<<< * ctypedef npy_clongdouble clongdouble_t * */ typedef npy_cdouble __pyx_t_5numpy_cdouble_t; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":764 * ctypedef npy_cfloat cfloat_t * ctypedef npy_cdouble cdouble_t * ctypedef npy_clongdouble clongdouble_t # <<<<<<<<<<<<<< * * ctypedef npy_cdouble complex_t */ typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t; /* "../../../../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":766 * ctypedef npy_clongdouble clongdouble_t * * ctypedef npy_cdouble complex_t # <<<<<<<<<<<<<< * * cdef inline object PyArray_MultiIterNew1(a): */ typedef npy_cdouble __pyx_t_5numpy_complex_t; struct __pyx_t_7sklearn_5_tree_StackRecord; struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord; struct __pyx_t_7sklearn_5_tree_SplitRecord; struct __pyx_t_7sklearn_5_tree_Node; struct __pyx_opt_args_7sklearn_5_tree_4Tree__resize_c; struct __pyx_opt_args_7sklearn_5_tree_4Tree_compute_feature_importances; struct __pyx_opt_args_7sklearn_5_tree_11TreeBuilder_build; struct __pyx_opt_args_7sklearn_5_tree_21DepthFirstTreeBuilder_build; struct __pyx_opt_args_7sklearn_5_tree_20BestFirstTreeBuilder_build; /* "sklearn/_tree.pxd":26 * * # A record on the stack for depth-first tree growing * cdef struct StackRecord: # <<<<<<<<<<<<<< * SIZE_t start * SIZE_t end */ struct __pyx_t_7sklearn_5_tree_StackRecord { __pyx_t_7sklearn_5_tree_SIZE_t start; __pyx_t_7sklearn_5_tree_SIZE_t end; __pyx_t_7sklearn_5_tree_SIZE_t depth; __pyx_t_7sklearn_5_tree_SIZE_t parent; int is_left; double impurity; __pyx_t_7sklearn_5_tree_SIZE_t n_constant_features; }; /* "sklearn/_tree.pxd":52 * * # A record on the frontier for best-first tree growing * cdef struct PriorityHeapRecord: # <<<<<<<<<<<<<< * SIZE_t node_id * SIZE_t start */ struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord { __pyx_t_7sklearn_5_tree_SIZE_t node_id; __pyx_t_7sklearn_5_tree_SIZE_t start; __pyx_t_7sklearn_5_tree_SIZE_t end; __pyx_t_7sklearn_5_tree_SIZE_t pos; __pyx_t_7sklearn_5_tree_SIZE_t depth; int is_leaf; double impurity; double impurity_left; double impurity_right; double improvement; }; /* "sklearn/_tree.pxd":123 * # ============================================================================= * * cdef struct SplitRecord: # <<<<<<<<<<<<<< * # Data to track sample split * SIZE_t feature # Which feature to split on. */ struct __pyx_t_7sklearn_5_tree_SplitRecord { __pyx_t_7sklearn_5_tree_SIZE_t feature; __pyx_t_7sklearn_5_tree_SIZE_t pos; double threshold; double improvement; double impurity_left; double impurity_right; }; /* "sklearn/_tree.pxd":202 * # ============================================================================= * * cdef struct Node: # <<<<<<<<<<<<<< * # Base storage structure for the nodes in a Tree object * */ struct __pyx_t_7sklearn_5_tree_Node { __pyx_t_7sklearn_5_tree_SIZE_t left_child; __pyx_t_7sklearn_5_tree_SIZE_t right_child; __pyx_t_7sklearn_5_tree_SIZE_t feature; __pyx_t_7sklearn_5_tree_DOUBLE_t threshold; __pyx_t_7sklearn_5_tree_DOUBLE_t impurity; __pyx_t_7sklearn_5_tree_SIZE_t n_node_samples; __pyx_t_7sklearn_5_tree_DOUBLE_t weighted_n_node_samples; }; /* "sklearn/_tree.pxd":240 * double weighted_n_samples) nogil * cdef void _resize(self, SIZE_t capacity) except * * cdef int _resize_c(self, SIZE_t capacity=*) nogil # <<<<<<<<<<<<<< * * cdef np.ndarray _get_value_ndarray(self) */ struct __pyx_opt_args_7sklearn_5_tree_4Tree__resize_c { int __pyx_n; __pyx_t_7sklearn_5_tree_SIZE_t capacity; }; /* "sklearn/_tree.pxd":250 * cdef np.ndarray _apply_sparse_csr(self, object X) * * cpdef compute_feature_importances(self, normalize=*) # <<<<<<<<<<<<<< * * */ struct __pyx_opt_args_7sklearn_5_tree_4Tree_compute_feature_importances { int __pyx_n; PyObject *normalize; }; /* "sklearn/_tree.pxd":272 * cdef SIZE_t max_depth # Maximal tree depth * * cpdef build(self, Tree tree, object X, np.ndarray y, # <<<<<<<<<<<<<< * np.ndarray sample_weight=*) * cdef _check_input(self, object X, np.ndarray y, np.ndarray sample_weight) */ struct __pyx_opt_args_7sklearn_5_tree_11TreeBuilder_build { int __pyx_n; PyArrayObject *sample_weight; }; /* "sklearn/_tree.pyx":63 * cdef int IS_NOT_LEFT = 0 * * cdef enum: # <<<<<<<<<<<<<< * # Max value for our rand_r replacement (near the bottom). * # We don't use RAND_MAX because it's different across platforms and */ enum { __pyx_e_7sklearn_5_tree_RAND_R_MAX = 0x7FFFFFFF }; /* "sklearn/_tree.pyx":2816 * self.max_depth = max_depth * * cpdef build(self, Tree tree, object X, np.ndarray y, # <<<<<<<<<<<<<< * np.ndarray sample_weight=None): * """Build a decision tree from the training set (X, y).""" */ struct __pyx_opt_args_7sklearn_5_tree_21DepthFirstTreeBuilder_build { int __pyx_n; PyArrayObject *sample_weight; }; /* "sklearn/_tree.pyx":2970 * self.max_leaf_nodes = max_leaf_nodes * * cpdef build(self, Tree tree, object X, np.ndarray y, # <<<<<<<<<<<<<< * np.ndarray sample_weight=None): * """Build a decision tree from the training set (X, y).""" */ struct __pyx_opt_args_7sklearn_5_tree_20BestFirstTreeBuilder_build { int __pyx_n; PyArrayObject *sample_weight; }; /* "sklearn/_tree.pxd":35 * SIZE_t n_constant_features * * cdef class Stack: # <<<<<<<<<<<<<< * cdef SIZE_t capacity * cdef SIZE_t top */ struct __pyx_obj_7sklearn_5_tree_Stack { PyObject_HEAD struct __pyx_vtabstruct_7sklearn_5_tree_Stack *__pyx_vtab; __pyx_t_7sklearn_5_tree_SIZE_t capacity; __pyx_t_7sklearn_5_tree_SIZE_t top; struct __pyx_t_7sklearn_5_tree_StackRecord *stack_; }; /* "sklearn/_tree.pxd":64 * double improvement * * cdef class PriorityHeap: # <<<<<<<<<<<<<< * cdef SIZE_t capacity * cdef SIZE_t heap_ptr */ struct __pyx_obj_7sklearn_5_tree_PriorityHeap { PyObject_HEAD struct __pyx_vtabstruct_7sklearn_5_tree_PriorityHeap *__pyx_vtab; __pyx_t_7sklearn_5_tree_SIZE_t capacity; __pyx_t_7sklearn_5_tree_SIZE_t heap_ptr; struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord *heap_; }; /* "sklearn/_tree.pxd":81 * # ============================================================================= * * cdef class Criterion: # <<<<<<<<<<<<<< * # The criterion computes the impurity of a node and the reduction of * # impurity of a split on that node. It also computes the output statistics */ struct __pyx_obj_7sklearn_5_tree_Criterion { PyObject_HEAD struct __pyx_vtabstruct_7sklearn_5_tree_Criterion *__pyx_vtab; __pyx_t_7sklearn_5_tree_DOUBLE_t *y; __pyx_t_7sklearn_5_tree_SIZE_t y_stride; __pyx_t_7sklearn_5_tree_DOUBLE_t *sample_weight; __pyx_t_7sklearn_5_tree_SIZE_t *samples; __pyx_t_7sklearn_5_tree_SIZE_t start; __pyx_t_7sklearn_5_tree_SIZE_t pos; __pyx_t_7sklearn_5_tree_SIZE_t end; __pyx_t_7sklearn_5_tree_SIZE_t n_outputs; __pyx_t_7sklearn_5_tree_SIZE_t n_node_samples; double weighted_n_samples; double weighted_n_node_samples; double weighted_n_left; double weighted_n_right; }; /* "sklearn/_tree.pxd":135 * * * cdef class Splitter: # <<<<<<<<<<<<<< * # The splitter searches in the input space for a feature and a threshold * # to split the samples samples[start:end]. */ struct __pyx_obj_7sklearn_5_tree_Splitter { PyObject_HEAD struct __pyx_vtabstruct_7sklearn_5_tree_Splitter *__pyx_vtab; struct __pyx_obj_7sklearn_5_tree_Criterion *criterion; __pyx_t_7sklearn_5_tree_SIZE_t max_features; __pyx_t_7sklearn_5_tree_SIZE_t min_samples_leaf; double min_weight_leaf; PyObject *random_state; __pyx_t_7sklearn_5_tree_UINT32_t rand_r_state; __pyx_t_7sklearn_5_tree_SIZE_t *samples; __pyx_t_7sklearn_5_tree_SIZE_t n_samples; double weighted_n_samples; __pyx_t_7sklearn_5_tree_SIZE_t *features; __pyx_t_7sklearn_5_tree_SIZE_t *constant_features; __pyx_t_7sklearn_5_tree_SIZE_t n_features; __pyx_t_7sklearn_5_tree_DTYPE_t *feature_values; __pyx_t_7sklearn_5_tree_SIZE_t start; __pyx_t_7sklearn_5_tree_SIZE_t end; __pyx_t_7sklearn_5_tree_DOUBLE_t *y; __pyx_t_7sklearn_5_tree_SIZE_t y_stride; __pyx_t_7sklearn_5_tree_DOUBLE_t *sample_weight; }; /* "sklearn/_tree.pxd":214 * * * cdef class Tree: # <<<<<<<<<<<<<< * # The Tree object is a binary tree structure constructed by the * # TreeBuilder. The tree structure is used for predictions and */ struct __pyx_obj_7sklearn_5_tree_Tree { PyObject_HEAD struct __pyx_vtabstruct_7sklearn_5_tree_Tree *__pyx_vtab; __pyx_t_7sklearn_5_tree_SIZE_t n_features; __pyx_t_7sklearn_5_tree_SIZE_t *n_classes; __pyx_t_7sklearn_5_tree_SIZE_t n_outputs; __pyx_t_7sklearn_5_tree_SIZE_t max_n_classes; __pyx_t_7sklearn_5_tree_SIZE_t max_depth; __pyx_t_7sklearn_5_tree_SIZE_t node_count; __pyx_t_7sklearn_5_tree_SIZE_t capacity; struct __pyx_t_7sklearn_5_tree_Node *nodes; double *value; __pyx_t_7sklearn_5_tree_SIZE_t value_stride; }; /* "sklearn/_tree.pxd":257 * # ============================================================================= * * cdef class TreeBuilder: # <<<<<<<<<<<<<< * # The TreeBuilder recursively builds a Tree object from training samples, * # using a Splitter object for splitting internal nodes and assigning */ struct __pyx_obj_7sklearn_5_tree_TreeBuilder { PyObject_HEAD struct __pyx_vtabstruct_7sklearn_5_tree_TreeBuilder *__pyx_vtab; struct __pyx_obj_7sklearn_5_tree_Splitter *splitter; __pyx_t_7sklearn_5_tree_SIZE_t min_samples_split; __pyx_t_7sklearn_5_tree_SIZE_t min_samples_leaf; double min_weight_leaf; __pyx_t_7sklearn_5_tree_SIZE_t max_depth; }; /* "sklearn/_tree.pyx":363 * * * cdef class ClassificationCriterion(Criterion): # <<<<<<<<<<<<<< * """Abstract criterion for classification.""" * cdef SIZE_t* n_classes */ struct __pyx_obj_7sklearn_5_tree_ClassificationCriterion { struct __pyx_obj_7sklearn_5_tree_Criterion __pyx_base; __pyx_t_7sklearn_5_tree_SIZE_t *n_classes; __pyx_t_7sklearn_5_tree_SIZE_t label_count_stride; double *label_count_left; double *label_count_right; double *label_count_total; }; /* "sklearn/_tree.pyx":581 * * * cdef class Entropy(ClassificationCriterion): # <<<<<<<<<<<<<< * """Cross Entropy impurity criteria. * */ struct __pyx_obj_7sklearn_5_tree_Entropy { struct __pyx_obj_7sklearn_5_tree_ClassificationCriterion __pyx_base; }; /* "sklearn/_tree.pyx":672 * * * cdef class Gini(ClassificationCriterion): # <<<<<<<<<<<<<< * """Gini Index impurity criteria. * */ struct __pyx_obj_7sklearn_5_tree_Gini { struct __pyx_obj_7sklearn_5_tree_ClassificationCriterion __pyx_base; }; /* "sklearn/_tree.pyx":766 * * * cdef class RegressionCriterion(Criterion): # <<<<<<<<<<<<<< * """Abstract criterion for regression. * */ struct __pyx_obj_7sklearn_5_tree_RegressionCriterion { struct __pyx_obj_7sklearn_5_tree_Criterion __pyx_base; double *mean_left; double *mean_right; double *mean_total; double *sq_sum_left; double *sq_sum_right; double *sq_sum_total; double *var_left; double *var_right; double *sum_left; double *sum_right; double *sum_total; }; /* "sklearn/_tree.pyx":1049 * * * cdef class MSE(RegressionCriterion): # <<<<<<<<<<<<<< * """Mean squared error impurity criterion. * */ struct __pyx_obj_7sklearn_5_tree_MSE { struct __pyx_obj_7sklearn_5_tree_RegressionCriterion __pyx_base; }; /* "sklearn/_tree.pyx":1090 * * * cdef class FriedmanMSE(MSE): # <<<<<<<<<<<<<< * """Mean squared error impurity criterion with improvement score by Friedman * */ struct __pyx_obj_7sklearn_5_tree_FriedmanMSE { struct __pyx_obj_7sklearn_5_tree_MSE __pyx_base; }; /* "sklearn/_tree.pyx":1244 * * * cdef class BaseDenseSplitter(Splitter): # <<<<<<<<<<<<<< * cdef DTYPE_t* X * cdef SIZE_t X_sample_stride */ struct __pyx_obj_7sklearn_5_tree_BaseDenseSplitter { struct __pyx_obj_7sklearn_5_tree_Splitter __pyx_base; __pyx_t_7sklearn_5_tree_DTYPE_t *X; __pyx_t_7sklearn_5_tree_SIZE_t X_sample_stride; __pyx_t_7sklearn_5_tree_SIZE_t X_fx_stride; }; /* "sklearn/_tree.pyx":1275 * * * cdef class BestSplitter(BaseDenseSplitter): # <<<<<<<<<<<<<< * """Splitter for finding the best split.""" * def __reduce__(self): */ struct __pyx_obj_7sklearn_5_tree_BestSplitter { struct __pyx_obj_7sklearn_5_tree_BaseDenseSplitter __pyx_base; }; /* "sklearn/_tree.pyx":1574 * * * cdef class RandomSplitter(BaseDenseSplitter): # <<<<<<<<<<<<<< * """Splitter for finding the best random split.""" * def __reduce__(self): */ struct __pyx_obj_7sklearn_5_tree_RandomSplitter { struct __pyx_obj_7sklearn_5_tree_BaseDenseSplitter __pyx_base; }; /* "sklearn/_tree.pyx":1776 * * * cdef class PresortBestSplitter(BaseDenseSplitter): # <<<<<<<<<<<<<< * """Splitter for finding the best split, using presorting.""" * cdef DTYPE_t* X_old */ struct __pyx_obj_7sklearn_5_tree_PresortBestSplitter { struct __pyx_obj_7sklearn_5_tree_BaseDenseSplitter __pyx_base; __pyx_t_7sklearn_5_tree_DTYPE_t *X_old; PyArrayObject *X_argsorted; __pyx_t_7sklearn_5_tree_INT32_t *X_argsorted_ptr; __pyx_t_7sklearn_5_tree_SIZE_t X_argsorted_stride; __pyx_t_7sklearn_5_tree_SIZE_t n_total_samples; unsigned char *sample_mask; }; /* "sklearn/_tree.pyx":2024 * * * cdef class BaseSparseSplitter(Splitter): # <<<<<<<<<<<<<< * # The sparse splitter works only with csc sparse matrix format * cdef DTYPE_t* X_data */ struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter { struct __pyx_obj_7sklearn_5_tree_Splitter __pyx_base; __pyx_t_7sklearn_5_tree_DTYPE_t *X_data; __pyx_t_7sklearn_5_tree_INT32_t *X_indices; __pyx_t_7sklearn_5_tree_INT32_t *X_indptr; __pyx_t_7sklearn_5_tree_SIZE_t n_total_samples; __pyx_t_7sklearn_5_tree_SIZE_t *index_to_samples; __pyx_t_7sklearn_5_tree_SIZE_t *sorted_samples; }; /* "sklearn/_tree.pyx":2340 * * * cdef class BestSparseSplitter(BaseSparseSplitter): # <<<<<<<<<<<<<< * """Splitter for finding the best split, using the sparse data.""" * */ struct __pyx_obj_7sklearn_5_tree_BestSparseSplitter { struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter __pyx_base; }; /* "sklearn/_tree.pyx":2558 * * * cdef class RandomSparseSplitter(BaseSparseSplitter): # <<<<<<<<<<<<<< * """Splitter for finding a random split, using the sparse data.""" * */ struct __pyx_obj_7sklearn_5_tree_RandomSparseSplitter { struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter __pyx_base; }; /* "sklearn/_tree.pyx":2804 * # Depth first builder --------------------------------------------------------- * * cdef class DepthFirstTreeBuilder(TreeBuilder): # <<<<<<<<<<<<<< * """Build a decision tree in depth-first fashion.""" * */ struct __pyx_obj_7sklearn_5_tree_DepthFirstTreeBuilder { struct __pyx_obj_7sklearn_5_tree_TreeBuilder __pyx_base; }; /* "sklearn/_tree.pyx":2950 * * * cdef class BestFirstTreeBuilder(TreeBuilder): # <<<<<<<<<<<<<< * """Build a decision tree in best-first fashion. * */ struct __pyx_obj_7sklearn_5_tree_BestFirstTreeBuilder { struct __pyx_obj_7sklearn_5_tree_TreeBuilder __pyx_base; __pyx_t_7sklearn_5_tree_SIZE_t max_leaf_nodes; }; /* "sklearn/_tree.pyx":90 * # ============================================================================= * * cdef class Stack: # <<<<<<<<<<<<<< * """A LIFO data structure. * */ struct __pyx_vtabstruct_7sklearn_5_tree_Stack { int (*is_empty)(struct __pyx_obj_7sklearn_5_tree_Stack *); int (*push)(struct __pyx_obj_7sklearn_5_tree_Stack *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, int, double, __pyx_t_7sklearn_5_tree_SIZE_t); int (*pop)(struct __pyx_obj_7sklearn_5_tree_Stack *, struct __pyx_t_7sklearn_5_tree_StackRecord *); }; static struct __pyx_vtabstruct_7sklearn_5_tree_Stack *__pyx_vtabptr_7sklearn_5_tree_Stack; /* "sklearn/_tree.pyx":208 * * * cdef class PriorityHeap: # <<<<<<<<<<<<<< * """A priority queue implemented as a binary heap. * */ struct __pyx_vtabstruct_7sklearn_5_tree_PriorityHeap { int (*is_empty)(struct __pyx_obj_7sklearn_5_tree_PriorityHeap *); int (*push)(struct __pyx_obj_7sklearn_5_tree_PriorityHeap *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, int, double, double, double, double); int (*pop)(struct __pyx_obj_7sklearn_5_tree_PriorityHeap *, struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord *); }; static struct __pyx_vtabstruct_7sklearn_5_tree_PriorityHeap *__pyx_vtabptr_7sklearn_5_tree_PriorityHeap; /* "sklearn/_tree.pyx":310 * # ============================================================================= * * cdef class Criterion: # <<<<<<<<<<<<<< * """Interface for impurity criteria.""" * */ struct __pyx_vtabstruct_7sklearn_5_tree_Criterion { void (*init)(struct __pyx_obj_7sklearn_5_tree_Criterion *, __pyx_t_7sklearn_5_tree_DOUBLE_t *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_DOUBLE_t *, double, __pyx_t_7sklearn_5_tree_SIZE_t *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t); void (*reset)(struct __pyx_obj_7sklearn_5_tree_Criterion *); void (*update)(struct __pyx_obj_7sklearn_5_tree_Criterion *, __pyx_t_7sklearn_5_tree_SIZE_t); double (*node_impurity)(struct __pyx_obj_7sklearn_5_tree_Criterion *); void (*children_impurity)(struct __pyx_obj_7sklearn_5_tree_Criterion *, double *, double *); void (*node_value)(struct __pyx_obj_7sklearn_5_tree_Criterion *, double *); double (*impurity_improvement)(struct __pyx_obj_7sklearn_5_tree_Criterion *, double); }; static struct __pyx_vtabstruct_7sklearn_5_tree_Criterion *__pyx_vtabptr_7sklearn_5_tree_Criterion; /* "sklearn/_tree.pyx":1135 * * * cdef class Splitter: # <<<<<<<<<<<<<< * def __cinit__(self, Criterion criterion, SIZE_t max_features, * SIZE_t min_samples_leaf, double min_weight_leaf, */ struct __pyx_vtabstruct_7sklearn_5_tree_Splitter { void (*init)(struct __pyx_obj_7sklearn_5_tree_Splitter *, PyObject *, PyArrayObject *, __pyx_t_7sklearn_5_tree_DOUBLE_t *); void (*node_reset)(struct __pyx_obj_7sklearn_5_tree_Splitter *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, double *); void (*node_split)(struct __pyx_obj_7sklearn_5_tree_Splitter *, double, struct __pyx_t_7sklearn_5_tree_SplitRecord *, __pyx_t_7sklearn_5_tree_SIZE_t *); void (*node_value)(struct __pyx_obj_7sklearn_5_tree_Splitter *, double *); double (*node_impurity)(struct __pyx_obj_7sklearn_5_tree_Splitter *); }; static struct __pyx_vtabstruct_7sklearn_5_tree_Splitter *__pyx_vtabptr_7sklearn_5_tree_Splitter; /* "sklearn/_tree.pyx":3154 * # ============================================================================= * * cdef class Tree: # <<<<<<<<<<<<<< * """Array-based representation of a binary decision tree. * */ struct __pyx_vtabstruct_7sklearn_5_tree_Tree { __pyx_t_7sklearn_5_tree_SIZE_t (*_add_node)(struct __pyx_obj_7sklearn_5_tree_Tree *, __pyx_t_7sklearn_5_tree_SIZE_t, int, int, __pyx_t_7sklearn_5_tree_SIZE_t, double, double, __pyx_t_7sklearn_5_tree_SIZE_t, double); void (*_resize)(struct __pyx_obj_7sklearn_5_tree_Tree *, __pyx_t_7sklearn_5_tree_SIZE_t); int (*_resize_c)(struct __pyx_obj_7sklearn_5_tree_Tree *, struct __pyx_opt_args_7sklearn_5_tree_4Tree__resize_c *__pyx_optional_args); PyArrayObject *(*_get_value_ndarray)(struct __pyx_obj_7sklearn_5_tree_Tree *); PyArrayObject *(*_get_node_ndarray)(struct __pyx_obj_7sklearn_5_tree_Tree *); PyArrayObject *(*predict)(struct __pyx_obj_7sklearn_5_tree_Tree *, PyObject *, int __pyx_skip_dispatch); PyArrayObject *(*apply)(struct __pyx_obj_7sklearn_5_tree_Tree *, PyObject *, int __pyx_skip_dispatch); PyArrayObject *(*_apply_dense)(struct __pyx_obj_7sklearn_5_tree_Tree *, PyObject *); PyArrayObject *(*_apply_sparse_csr)(struct __pyx_obj_7sklearn_5_tree_Tree *, PyObject *); PyObject *(*compute_feature_importances)(struct __pyx_obj_7sklearn_5_tree_Tree *, int __pyx_skip_dispatch, struct __pyx_opt_args_7sklearn_5_tree_4Tree_compute_feature_importances *__pyx_optional_args); }; static struct __pyx_vtabstruct_7sklearn_5_tree_Tree *__pyx_vtabptr_7sklearn_5_tree_Tree; static PyArrayObject *__pyx_f_7sklearn_5_tree_4Tree__apply_dense(struct __pyx_obj_7sklearn_5_tree_Tree *, PyObject *); static PyArrayObject *__pyx_f_7sklearn_5_tree_4Tree__apply_sparse_csr(struct __pyx_obj_7sklearn_5_tree_Tree *, PyObject *); /* "sklearn/_tree.pyx":2776 * # Tree builders * # ============================================================================= * cdef class TreeBuilder: # <<<<<<<<<<<<<< * """Interface for different tree building strategies. """ * */ struct __pyx_vtabstruct_7sklearn_5_tree_TreeBuilder { PyObject *(*build)(struct __pyx_obj_7sklearn_5_tree_TreeBuilder *, struct __pyx_obj_7sklearn_5_tree_Tree *, PyObject *, PyArrayObject *, int __pyx_skip_dispatch, struct __pyx_opt_args_7sklearn_5_tree_11TreeBuilder_build *__pyx_optional_args); PyObject *(*_check_input)(struct __pyx_obj_7sklearn_5_tree_TreeBuilder *, PyObject *, PyArrayObject *, PyArrayObject *); }; static struct __pyx_vtabstruct_7sklearn_5_tree_TreeBuilder *__pyx_vtabptr_7sklearn_5_tree_TreeBuilder; static PyObject *__pyx_f_7sklearn_5_tree_11TreeBuilder__check_input(struct __pyx_obj_7sklearn_5_tree_TreeBuilder *, PyObject *, PyArrayObject *, PyArrayObject *); /* "sklearn/_tree.pyx":363 * * * cdef class ClassificationCriterion(Criterion): # <<<<<<<<<<<<<< * """Abstract criterion for classification.""" * cdef SIZE_t* n_classes */ struct __pyx_vtabstruct_7sklearn_5_tree_ClassificationCriterion { struct __pyx_vtabstruct_7sklearn_5_tree_Criterion __pyx_base; }; static struct __pyx_vtabstruct_7sklearn_5_tree_ClassificationCriterion *__pyx_vtabptr_7sklearn_5_tree_ClassificationCriterion; /* "sklearn/_tree.pyx":581 * * * cdef class Entropy(ClassificationCriterion): # <<<<<<<<<<<<<< * """Cross Entropy impurity criteria. * */ struct __pyx_vtabstruct_7sklearn_5_tree_Entropy { struct __pyx_vtabstruct_7sklearn_5_tree_ClassificationCriterion __pyx_base; }; static struct __pyx_vtabstruct_7sklearn_5_tree_Entropy *__pyx_vtabptr_7sklearn_5_tree_Entropy; /* "sklearn/_tree.pyx":672 * * * cdef class Gini(ClassificationCriterion): # <<<<<<<<<<<<<< * """Gini Index impurity criteria. * */ struct __pyx_vtabstruct_7sklearn_5_tree_Gini { struct __pyx_vtabstruct_7sklearn_5_tree_ClassificationCriterion __pyx_base; }; static struct __pyx_vtabstruct_7sklearn_5_tree_Gini *__pyx_vtabptr_7sklearn_5_tree_Gini; /* "sklearn/_tree.pyx":766 * * * cdef class RegressionCriterion(Criterion): # <<<<<<<<<<<<<< * """Abstract criterion for regression. * */ struct __pyx_vtabstruct_7sklearn_5_tree_RegressionCriterion { struct __pyx_vtabstruct_7sklearn_5_tree_Criterion __pyx_base; }; static struct __pyx_vtabstruct_7sklearn_5_tree_RegressionCriterion *__pyx_vtabptr_7sklearn_5_tree_RegressionCriterion; /* "sklearn/_tree.pyx":1049 * * * cdef class MSE(RegressionCriterion): # <<<<<<<<<<<<<< * """Mean squared error impurity criterion. * */ struct __pyx_vtabstruct_7sklearn_5_tree_MSE { struct __pyx_vtabstruct_7sklearn_5_tree_RegressionCriterion __pyx_base; }; static struct __pyx_vtabstruct_7sklearn_5_tree_MSE *__pyx_vtabptr_7sklearn_5_tree_MSE; /* "sklearn/_tree.pyx":1090 * * * cdef class FriedmanMSE(MSE): # <<<<<<<<<<<<<< * """Mean squared error impurity criterion with improvement score by Friedman * */ struct __pyx_vtabstruct_7sklearn_5_tree_FriedmanMSE { struct __pyx_vtabstruct_7sklearn_5_tree_MSE __pyx_base; }; static struct __pyx_vtabstruct_7sklearn_5_tree_FriedmanMSE *__pyx_vtabptr_7sklearn_5_tree_FriedmanMSE; /* "sklearn/_tree.pyx":1244 * * * cdef class BaseDenseSplitter(Splitter): # <<<<<<<<<<<<<< * cdef DTYPE_t* X * cdef SIZE_t X_sample_stride */ struct __pyx_vtabstruct_7sklearn_5_tree_BaseDenseSplitter { struct __pyx_vtabstruct_7sklearn_5_tree_Splitter __pyx_base; }; static struct __pyx_vtabstruct_7sklearn_5_tree_BaseDenseSplitter *__pyx_vtabptr_7sklearn_5_tree_BaseDenseSplitter; /* "sklearn/_tree.pyx":1275 * * * cdef class BestSplitter(BaseDenseSplitter): # <<<<<<<<<<<<<< * """Splitter for finding the best split.""" * def __reduce__(self): */ struct __pyx_vtabstruct_7sklearn_5_tree_BestSplitter { struct __pyx_vtabstruct_7sklearn_5_tree_BaseDenseSplitter __pyx_base; }; static struct __pyx_vtabstruct_7sklearn_5_tree_BestSplitter *__pyx_vtabptr_7sklearn_5_tree_BestSplitter; /* "sklearn/_tree.pyx":1574 * * * cdef class RandomSplitter(BaseDenseSplitter): # <<<<<<<<<<<<<< * """Splitter for finding the best random split.""" * def __reduce__(self): */ struct __pyx_vtabstruct_7sklearn_5_tree_RandomSplitter { struct __pyx_vtabstruct_7sklearn_5_tree_BaseDenseSplitter __pyx_base; }; static struct __pyx_vtabstruct_7sklearn_5_tree_RandomSplitter *__pyx_vtabptr_7sklearn_5_tree_RandomSplitter; /* "sklearn/_tree.pyx":1776 * * * cdef class PresortBestSplitter(BaseDenseSplitter): # <<<<<<<<<<<<<< * """Splitter for finding the best split, using presorting.""" * cdef DTYPE_t* X_old */ struct __pyx_vtabstruct_7sklearn_5_tree_PresortBestSplitter { struct __pyx_vtabstruct_7sklearn_5_tree_BaseDenseSplitter __pyx_base; }; static struct __pyx_vtabstruct_7sklearn_5_tree_PresortBestSplitter *__pyx_vtabptr_7sklearn_5_tree_PresortBestSplitter; /* "sklearn/_tree.pyx":2024 * * * cdef class BaseSparseSplitter(Splitter): # <<<<<<<<<<<<<< * # The sparse splitter works only with csc sparse matrix format * cdef DTYPE_t* X_data */ struct __pyx_vtabstruct_7sklearn_5_tree_BaseSparseSplitter { struct __pyx_vtabstruct_7sklearn_5_tree_Splitter __pyx_base; __pyx_t_7sklearn_5_tree_SIZE_t (*_partition)(struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter *, double, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t); void (*extract_nnz)(struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t *, __pyx_t_7sklearn_5_tree_SIZE_t *, int *); }; static struct __pyx_vtabstruct_7sklearn_5_tree_BaseSparseSplitter *__pyx_vtabptr_7sklearn_5_tree_BaseSparseSplitter; static CYTHON_INLINE __pyx_t_7sklearn_5_tree_SIZE_t __pyx_f_7sklearn_5_tree_18BaseSparseSplitter__partition(struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter *, double, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t); static CYTHON_INLINE void __pyx_f_7sklearn_5_tree_18BaseSparseSplitter_extract_nnz(struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t *, __pyx_t_7sklearn_5_tree_SIZE_t *, int *); /* "sklearn/_tree.pyx":2340 * * * cdef class BestSparseSplitter(BaseSparseSplitter): # <<<<<<<<<<<<<< * """Splitter for finding the best split, using the sparse data.""" * */ struct __pyx_vtabstruct_7sklearn_5_tree_BestSparseSplitter { struct __pyx_vtabstruct_7sklearn_5_tree_BaseSparseSplitter __pyx_base; }; static struct __pyx_vtabstruct_7sklearn_5_tree_BestSparseSplitter *__pyx_vtabptr_7sklearn_5_tree_BestSparseSplitter; /* "sklearn/_tree.pyx":2558 * * * cdef class RandomSparseSplitter(BaseSparseSplitter): # <<<<<<<<<<<<<< * """Splitter for finding a random split, using the sparse data.""" * */ struct __pyx_vtabstruct_7sklearn_5_tree_RandomSparseSplitter { struct __pyx_vtabstruct_7sklearn_5_tree_BaseSparseSplitter __pyx_base; }; static struct __pyx_vtabstruct_7sklearn_5_tree_RandomSparseSplitter *__pyx_vtabptr_7sklearn_5_tree_RandomSparseSplitter; /* "sklearn/_tree.pyx":2804 * # Depth first builder --------------------------------------------------------- * * cdef class DepthFirstTreeBuilder(TreeBuilder): # <<<<<<<<<<<<<< * """Build a decision tree in depth-first fashion.""" * */ struct __pyx_vtabstruct_7sklearn_5_tree_DepthFirstTreeBuilder { struct __pyx_vtabstruct_7sklearn_5_tree_TreeBuilder __pyx_base; }; static struct __pyx_vtabstruct_7sklearn_5_tree_DepthFirstTreeBuilder *__pyx_vtabptr_7sklearn_5_tree_DepthFirstTreeBuilder; /* "sklearn/_tree.pyx":2950 * * * cdef class BestFirstTreeBuilder(TreeBuilder): # <<<<<<<<<<<<<< * """Build a decision tree in best-first fashion. * */ struct __pyx_vtabstruct_7sklearn_5_tree_BestFirstTreeBuilder { struct __pyx_vtabstruct_7sklearn_5_tree_TreeBuilder __pyx_base; int (*_add_split_node)(struct __pyx_obj_7sklearn_5_tree_BestFirstTreeBuilder *, struct __pyx_obj_7sklearn_5_tree_Splitter *, struct __pyx_obj_7sklearn_5_tree_Tree *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, double, int, int, struct __pyx_t_7sklearn_5_tree_Node *, __pyx_t_7sklearn_5_tree_SIZE_t, struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord *); 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static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); static int __pyx_f_7sklearn_5_tree_5Stack_is_empty(struct __pyx_obj_7sklearn_5_tree_Stack *__pyx_v_self); /* proto*/ static int __pyx_f_7sklearn_5_tree_5Stack_push(struct __pyx_obj_7sklearn_5_tree_Stack *__pyx_v_self, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_start, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_end, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_depth, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_parent, int __pyx_v_is_left, double __pyx_v_impurity, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_constant_features); /* proto*/ static int __pyx_f_7sklearn_5_tree_5Stack_pop(struct __pyx_obj_7sklearn_5_tree_Stack *__pyx_v_self, struct __pyx_t_7sklearn_5_tree_StackRecord *__pyx_v_res); /* proto*/ static int __pyx_f_7sklearn_5_tree_12PriorityHeap_is_empty(struct __pyx_obj_7sklearn_5_tree_PriorityHeap *__pyx_v_self); /* proto*/ static int __pyx_f_7sklearn_5_tree_12PriorityHeap_push(struct __pyx_obj_7sklearn_5_tree_PriorityHeap *__pyx_v_self, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_node_id, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_start, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_end, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_pos, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_depth, int __pyx_v_is_leaf, double __pyx_v_improvement, double __pyx_v_impurity, double __pyx_v_impurity_left, double __pyx_v_impurity_right); /* proto*/ static int __pyx_f_7sklearn_5_tree_12PriorityHeap_pop(struct __pyx_obj_7sklearn_5_tree_PriorityHeap *__pyx_v_self, struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord *__pyx_v_res); /* proto*/ static void __pyx_f_7sklearn_5_tree_9Criterion_init(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_Criterion *__pyx_v_self, CYTHON_UNUSED __pyx_t_7sklearn_5_tree_DOUBLE_t *__pyx_v_y, CYTHON_UNUSED __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_y_stride, CYTHON_UNUSED __pyx_t_7sklearn_5_tree_DOUBLE_t *__pyx_v_sample_weight, CYTHON_UNUSED double __pyx_v_weighted_n_samples, CYTHON_UNUSED __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_samples, CYTHON_UNUSED __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_start, CYTHON_UNUSED __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_end); /* proto*/ static void __pyx_f_7sklearn_5_tree_9Criterion_reset(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_Criterion *__pyx_v_self); /* proto*/ static void __pyx_f_7sklearn_5_tree_9Criterion_update(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_Criterion *__pyx_v_self, CYTHON_UNUSED __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_new_pos); /* proto*/ static double __pyx_f_7sklearn_5_tree_9Criterion_node_impurity(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_Criterion *__pyx_v_self); /* proto*/ static void __pyx_f_7sklearn_5_tree_9Criterion_children_impurity(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_Criterion *__pyx_v_self, CYTHON_UNUSED double *__pyx_v_impurity_left, CYTHON_UNUSED double *__pyx_v_impurity_right); /* proto*/ static void __pyx_f_7sklearn_5_tree_9Criterion_node_value(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_Criterion *__pyx_v_self, CYTHON_UNUSED double *__pyx_v_dest); /* proto*/ static double __pyx_f_7sklearn_5_tree_9Criterion_impurity_improvement(struct __pyx_obj_7sklearn_5_tree_Criterion *__pyx_v_self, double __pyx_v_impurity); /* proto*/ static void __pyx_f_7sklearn_5_tree_23ClassificationCriterion_init(struct __pyx_obj_7sklearn_5_tree_ClassificationCriterion *__pyx_v_self, __pyx_t_7sklearn_5_tree_DOUBLE_t *__pyx_v_y, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_y_stride, __pyx_t_7sklearn_5_tree_DOUBLE_t *__pyx_v_sample_weight, double __pyx_v_weighted_n_samples, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_start, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_end); /* proto*/ static void __pyx_f_7sklearn_5_tree_23ClassificationCriterion_reset(struct __pyx_obj_7sklearn_5_tree_ClassificationCriterion *__pyx_v_self); /* proto*/ static void __pyx_f_7sklearn_5_tree_23ClassificationCriterion_update(struct __pyx_obj_7sklearn_5_tree_ClassificationCriterion *__pyx_v_self, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_new_pos); /* proto*/ static double __pyx_f_7sklearn_5_tree_23ClassificationCriterion_node_impurity(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_ClassificationCriterion *__pyx_v_self); /* proto*/ static void __pyx_f_7sklearn_5_tree_23ClassificationCriterion_children_impurity(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_ClassificationCriterion *__pyx_v_self, CYTHON_UNUSED double *__pyx_v_impurity_left, CYTHON_UNUSED double *__pyx_v_impurity_right); /* proto*/ static void __pyx_f_7sklearn_5_tree_23ClassificationCriterion_node_value(struct __pyx_obj_7sklearn_5_tree_ClassificationCriterion *__pyx_v_self, double *__pyx_v_dest); /* proto*/ static double __pyx_f_7sklearn_5_tree_7Entropy_node_impurity(struct __pyx_obj_7sklearn_5_tree_Entropy *__pyx_v_self); /* proto*/ static void __pyx_f_7sklearn_5_tree_7Entropy_children_impurity(struct __pyx_obj_7sklearn_5_tree_Entropy *__pyx_v_self, double *__pyx_v_impurity_left, double *__pyx_v_impurity_right); /* proto*/ static double __pyx_f_7sklearn_5_tree_4Gini_node_impurity(struct __pyx_obj_7sklearn_5_tree_Gini *__pyx_v_self); /* proto*/ static void __pyx_f_7sklearn_5_tree_4Gini_children_impurity(struct __pyx_obj_7sklearn_5_tree_Gini *__pyx_v_self, double *__pyx_v_impurity_left, double *__pyx_v_impurity_right); /* proto*/ static void __pyx_f_7sklearn_5_tree_19RegressionCriterion_init(struct __pyx_obj_7sklearn_5_tree_RegressionCriterion *__pyx_v_self, __pyx_t_7sklearn_5_tree_DOUBLE_t *__pyx_v_y, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_y_stride, __pyx_t_7sklearn_5_tree_DOUBLE_t *__pyx_v_sample_weight, double __pyx_v_weighted_n_samples, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_start, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_end); /* proto*/ static void __pyx_f_7sklearn_5_tree_19RegressionCriterion_reset(struct __pyx_obj_7sklearn_5_tree_RegressionCriterion *__pyx_v_self); /* proto*/ static void __pyx_f_7sklearn_5_tree_19RegressionCriterion_update(struct __pyx_obj_7sklearn_5_tree_RegressionCriterion *__pyx_v_self, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_new_pos); /* proto*/ static double __pyx_f_7sklearn_5_tree_19RegressionCriterion_node_impurity(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_RegressionCriterion *__pyx_v_self); /* proto*/ static void __pyx_f_7sklearn_5_tree_19RegressionCriterion_children_impurity(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_RegressionCriterion *__pyx_v_self, CYTHON_UNUSED double *__pyx_v_impurity_left, CYTHON_UNUSED double *__pyx_v_impurity_right); /* proto*/ static void __pyx_f_7sklearn_5_tree_19RegressionCriterion_node_value(struct __pyx_obj_7sklearn_5_tree_RegressionCriterion *__pyx_v_self, double *__pyx_v_dest); /* proto*/ static double __pyx_f_7sklearn_5_tree_3MSE_node_impurity(struct __pyx_obj_7sklearn_5_tree_MSE *__pyx_v_self); /* proto*/ static void __pyx_f_7sklearn_5_tree_3MSE_children_impurity(struct __pyx_obj_7sklearn_5_tree_MSE *__pyx_v_self, double *__pyx_v_impurity_left, double *__pyx_v_impurity_right); /* proto*/ static double __pyx_f_7sklearn_5_tree_11FriedmanMSE_impurity_improvement(struct __pyx_obj_7sklearn_5_tree_FriedmanMSE *__pyx_v_self, CYTHON_UNUSED double __pyx_v_impurity); /* proto*/ static void __pyx_f_7sklearn_5_tree_8Splitter_init(struct __pyx_obj_7sklearn_5_tree_Splitter *__pyx_v_self, PyObject *__pyx_v_X, PyArrayObject *__pyx_v_y, __pyx_t_7sklearn_5_tree_DOUBLE_t *__pyx_v_sample_weight); /* proto*/ static void __pyx_f_7sklearn_5_tree_8Splitter_node_reset(struct __pyx_obj_7sklearn_5_tree_Splitter *__pyx_v_self, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_start, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_end, double *__pyx_v_weighted_n_node_samples); /* proto*/ static void __pyx_f_7sklearn_5_tree_8Splitter_node_split(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_Splitter *__pyx_v_self, CYTHON_UNUSED double __pyx_v_impurity, CYTHON_UNUSED struct __pyx_t_7sklearn_5_tree_SplitRecord *__pyx_v_split, CYTHON_UNUSED __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_n_constant_features); /* proto*/ static void __pyx_f_7sklearn_5_tree_8Splitter_node_value(struct __pyx_obj_7sklearn_5_tree_Splitter *__pyx_v_self, double *__pyx_v_dest); /* proto*/ static double __pyx_f_7sklearn_5_tree_8Splitter_node_impurity(struct __pyx_obj_7sklearn_5_tree_Splitter *__pyx_v_self); /* proto*/ static void __pyx_f_7sklearn_5_tree_17BaseDenseSplitter_init(struct __pyx_obj_7sklearn_5_tree_BaseDenseSplitter *__pyx_v_self, PyObject *__pyx_v_X, PyArrayObject *__pyx_v_y, __pyx_t_7sklearn_5_tree_DOUBLE_t *__pyx_v_sample_weight); /* proto*/ static void __pyx_f_7sklearn_5_tree_12BestSplitter_node_split(struct __pyx_obj_7sklearn_5_tree_BestSplitter *__pyx_v_self, double __pyx_v_impurity, struct __pyx_t_7sklearn_5_tree_SplitRecord *__pyx_v_split, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_n_constant_features); /* proto*/ static void __pyx_f_7sklearn_5_tree_14RandomSplitter_node_split(struct __pyx_obj_7sklearn_5_tree_RandomSplitter *__pyx_v_self, double __pyx_v_impurity, struct __pyx_t_7sklearn_5_tree_SplitRecord *__pyx_v_split, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_n_constant_features); /* proto*/ static void __pyx_f_7sklearn_5_tree_19PresortBestSplitter_init(struct __pyx_obj_7sklearn_5_tree_PresortBestSplitter *__pyx_v_self, PyObject *__pyx_v_X, PyArrayObject *__pyx_v_y, __pyx_t_7sklearn_5_tree_DOUBLE_t *__pyx_v_sample_weight); /* proto*/ static void __pyx_f_7sklearn_5_tree_19PresortBestSplitter_node_split(struct __pyx_obj_7sklearn_5_tree_PresortBestSplitter *__pyx_v_self, double __pyx_v_impurity, struct __pyx_t_7sklearn_5_tree_SplitRecord *__pyx_v_split, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_n_constant_features); /* proto*/ static void __pyx_f_7sklearn_5_tree_18BaseSparseSplitter_init(struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter *__pyx_v_self, PyObject *__pyx_v_X, PyArrayObject *__pyx_v_y, __pyx_t_7sklearn_5_tree_DOUBLE_t *__pyx_v_sample_weight); /* proto*/ static CYTHON_INLINE __pyx_t_7sklearn_5_tree_SIZE_t __pyx_f_7sklearn_5_tree_18BaseSparseSplitter__partition(struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter *__pyx_v_self, double __pyx_v_threshold, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_end_negative, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_start_positive, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_zero_pos); /* proto*/ static CYTHON_INLINE void __pyx_f_7sklearn_5_tree_18BaseSparseSplitter_extract_nnz(struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter *__pyx_v_self, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_feature, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_end_negative, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_start_positive, int *__pyx_v_is_samples_sorted); /* proto*/ static void __pyx_f_7sklearn_5_tree_18BestSparseSplitter_node_split(struct __pyx_obj_7sklearn_5_tree_BestSparseSplitter *__pyx_v_self, double __pyx_v_impurity, struct __pyx_t_7sklearn_5_tree_SplitRecord *__pyx_v_split, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_n_constant_features); /* proto*/ static void __pyx_f_7sklearn_5_tree_20RandomSparseSplitter_node_split(struct __pyx_obj_7sklearn_5_tree_RandomSparseSplitter *__pyx_v_self, double __pyx_v_impurity, struct __pyx_t_7sklearn_5_tree_SplitRecord *__pyx_v_split, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_n_constant_features); /* proto*/ static PyObject *__pyx_f_7sklearn_5_tree_11TreeBuilder_build(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_TreeBuilder *__pyx_v_self, CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_tree, CYTHON_UNUSED PyObject *__pyx_v_X, CYTHON_UNUSED PyArrayObject *__pyx_v_y, int __pyx_skip_dispatch, struct __pyx_opt_args_7sklearn_5_tree_11TreeBuilder_build *__pyx_optional_args); /* proto*/ static PyObject *__pyx_f_7sklearn_5_tree_11TreeBuilder__check_input(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_TreeBuilder *__pyx_v_self, PyObject *__pyx_v_X, PyArrayObject *__pyx_v_y, PyArrayObject *__pyx_v_sample_weight); /* proto*/ static PyObject *__pyx_f_7sklearn_5_tree_21DepthFirstTreeBuilder_build(struct __pyx_obj_7sklearn_5_tree_DepthFirstTreeBuilder *__pyx_v_self, struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_tree, PyObject *__pyx_v_X, PyArrayObject *__pyx_v_y, int __pyx_skip_dispatch, struct __pyx_opt_args_7sklearn_5_tree_21DepthFirstTreeBuilder_build *__pyx_optional_args); /* proto*/ static PyObject *__pyx_f_7sklearn_5_tree_20BestFirstTreeBuilder_build(struct __pyx_obj_7sklearn_5_tree_BestFirstTreeBuilder *__pyx_v_self, struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_tree, PyObject *__pyx_v_X, PyArrayObject *__pyx_v_y, int __pyx_skip_dispatch, struct __pyx_opt_args_7sklearn_5_tree_20BestFirstTreeBuilder_build *__pyx_optional_args); /* proto*/ static CYTHON_INLINE int __pyx_f_7sklearn_5_tree_20BestFirstTreeBuilder__add_split_node(struct __pyx_obj_7sklearn_5_tree_BestFirstTreeBuilder *__pyx_v_self, struct __pyx_obj_7sklearn_5_tree_Splitter *__pyx_v_splitter, struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_tree, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_start, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_end, double __pyx_v_impurity, int __pyx_v_is_first, int __pyx_v_is_left, struct __pyx_t_7sklearn_5_tree_Node *__pyx_v_parent, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_depth, struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord *__pyx_v_res); /* proto*/ static void __pyx_f_7sklearn_5_tree_4Tree__resize(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_capacity); /* proto*/ static int __pyx_f_7sklearn_5_tree_4Tree__resize_c(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self, struct __pyx_opt_args_7sklearn_5_tree_4Tree__resize_c *__pyx_optional_args); /* proto*/ static __pyx_t_7sklearn_5_tree_SIZE_t __pyx_f_7sklearn_5_tree_4Tree__add_node(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_parent, int __pyx_v_is_left, int __pyx_v_is_leaf, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_feature, double __pyx_v_threshold, double __pyx_v_impurity, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_node_samples, double __pyx_v_weighted_n_node_samples); /* proto*/ static PyArrayObject *__pyx_f_7sklearn_5_tree_4Tree_predict(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_X, int __pyx_skip_dispatch); /* proto*/ static PyArrayObject *__pyx_f_7sklearn_5_tree_4Tree_apply(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_X, int __pyx_skip_dispatch); /* proto*/ static PyArrayObject *__pyx_f_7sklearn_5_tree_4Tree__apply_dense(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_X); /* proto*/ static PyArrayObject *__pyx_f_7sklearn_5_tree_4Tree__apply_sparse_csr(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_X); /* proto*/ static PyObject *__pyx_f_7sklearn_5_tree_4Tree_compute_feature_importances(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self, int __pyx_skip_dispatch, struct __pyx_opt_args_7sklearn_5_tree_4Tree_compute_feature_importances *__pyx_optional_args); /* proto*/ static PyArrayObject *__pyx_f_7sklearn_5_tree_4Tree__get_value_ndarray(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self); /* proto*/ static PyArrayObject *__pyx_f_7sklearn_5_tree_4Tree__get_node_ndarray(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self); /* proto*/ /* Module declarations from 'cpython.buffer' */ /* Module declarations from 'cpython.ref' */ /* Module declarations from 'libc.string' */ /* Module declarations from 'libc.stdio' */ /* Module declarations from 'cpython.object' */ /* Module declarations from '__builtin__' */ /* Module declarations from 'cpython.type' */ static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; /* Module declarations from 'libc.stdlib' */ /* Module declarations from 'numpy' */ /* Module declarations from 'numpy' */ static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/ /* Module declarations from 'libc.math' */ /* Module declarations from 'cpython.version' */ /* Module declarations from 'cpython.exc' */ /* Module declarations from 'cpython.module' */ /* Module declarations from 'cpython.mem' */ /* Module declarations from 'cpython.tuple' */ /* Module declarations from 'cpython.list' */ /* Module declarations from 'cpython.sequence' */ /* Module declarations from 'cpython.mapping' */ /* Module declarations from 'cpython.iterator' */ /* Module declarations from 'cpython.number' */ /* Module declarations from 'cpython.int' */ /* Module declarations from '__builtin__' */ /* Module declarations from 'cpython.bool' */ static PyTypeObject *__pyx_ptype_7cpython_4bool_bool = 0; /* Module declarations from 'cpython.long' */ /* Module declarations from 'cpython.float' */ /* Module declarations from '__builtin__' */ /* Module declarations from 'cpython.complex' */ static PyTypeObject *__pyx_ptype_7cpython_7complex_complex = 0; /* Module declarations from 'cpython.string' */ /* Module declarations from 'cpython.unicode' */ /* Module declarations from 'cpython.dict' */ /* Module declarations from 'cpython.instance' */ /* Module declarations from 'cpython.function' */ /* Module declarations from 'cpython.method' */ /* Module declarations from 'cpython.weakref' */ /* Module declarations from 'cpython.getargs' */ /* Module declarations from 'cpython.pythread' */ /* Module declarations from 'cpython.pystate' */ /* Module declarations from 'cpython.cobject' */ /* Module declarations from 'cpython.oldbuffer' */ /* Module declarations from 'cpython.set' */ /* Module declarations from 'cpython.bytes' */ /* Module declarations from 'cpython.pycapsule' */ /* Module declarations from 'cpython' */ /* Module declarations from 'sklearn._tree' */ static PyTypeObject *__pyx_ptype_7sklearn_5_tree_Stack = 0; static PyTypeObject *__pyx_ptype_7sklearn_5_tree_PriorityHeap = 0; static PyTypeObject *__pyx_ptype_7sklearn_5_tree_Criterion = 0; static PyTypeObject *__pyx_ptype_7sklearn_5_tree_Splitter = 0; static PyTypeObject *__pyx_ptype_7sklearn_5_tree_Tree = 0; static PyTypeObject *__pyx_ptype_7sklearn_5_tree_TreeBuilder = 0; static PyTypeObject *__pyx_ptype_7sklearn_5_tree_ClassificationCriterion = 0; static PyTypeObject *__pyx_ptype_7sklearn_5_tree_Entropy = 0; static PyTypeObject *__pyx_ptype_7sklearn_5_tree_Gini = 0; static PyTypeObject *__pyx_ptype_7sklearn_5_tree_RegressionCriterion = 0; static PyTypeObject *__pyx_ptype_7sklearn_5_tree_MSE = 0; static PyTypeObject *__pyx_ptype_7sklearn_5_tree_FriedmanMSE = 0; static PyTypeObject *__pyx_ptype_7sklearn_5_tree_BaseDenseSplitter = 0; static PyTypeObject *__pyx_ptype_7sklearn_5_tree_BestSplitter = 0; static PyTypeObject *__pyx_ptype_7sklearn_5_tree_RandomSplitter = 0; static PyTypeObject *__pyx_ptype_7sklearn_5_tree_PresortBestSplitter = 0; static PyTypeObject *__pyx_ptype_7sklearn_5_tree_BaseSparseSplitter = 0; static PyTypeObject *__pyx_ptype_7sklearn_5_tree_BestSparseSplitter = 0; static PyTypeObject *__pyx_ptype_7sklearn_5_tree_RandomSparseSplitter = 0; static PyTypeObject *__pyx_ptype_7sklearn_5_tree_DepthFirstTreeBuilder = 0; static PyTypeObject *__pyx_ptype_7sklearn_5_tree_BestFirstTreeBuilder = 0; static double __pyx_v_7sklearn_5_tree_INFINITY; static __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_7sklearn_5_tree__TREE_LEAF; static __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_7sklearn_5_tree__TREE_UNDEFINED; static __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_7sklearn_5_tree_INITIAL_STACK_SIZE; static __pyx_t_7sklearn_5_tree_DTYPE_t __pyx_v_7sklearn_5_tree_MIN_IMPURITY_SPLIT; static __pyx_t_7sklearn_5_tree_DTYPE_t __pyx_v_7sklearn_5_tree_FEATURE_THRESHOLD; static __pyx_t_7sklearn_5_tree_DTYPE_t __pyx_v_7sklearn_5_tree_EXTRACT_NNZ_SWITCH; static int __pyx_v_7sklearn_5_tree_IS_FIRST; static int __pyx_v_7sklearn_5_tree_IS_NOT_FIRST; static int __pyx_v_7sklearn_5_tree_IS_LEFT; static int __pyx_v_7sklearn_5_tree_IS_NOT_LEFT; static void __pyx_f_7sklearn_5_tree_heapify_up(struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord *, __pyx_t_7sklearn_5_tree_SIZE_t); /*proto*/ static void __pyx_f_7sklearn_5_tree_heapify_down(struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t); /*proto*/ static CYTHON_INLINE void __pyx_f_7sklearn_5_tree__init_split(struct __pyx_t_7sklearn_5_tree_SplitRecord *, __pyx_t_7sklearn_5_tree_SIZE_t); /*proto*/ static CYTHON_INLINE void __pyx_f_7sklearn_5_tree_sort(__pyx_t_7sklearn_5_tree_DTYPE_t *, __pyx_t_7sklearn_5_tree_SIZE_t *, __pyx_t_7sklearn_5_tree_SIZE_t); /*proto*/ static CYTHON_INLINE void __pyx_f_7sklearn_5_tree_swap(__pyx_t_7sklearn_5_tree_DTYPE_t *, __pyx_t_7sklearn_5_tree_SIZE_t *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t); /*proto*/ static CYTHON_INLINE __pyx_t_7sklearn_5_tree_DTYPE_t __pyx_f_7sklearn_5_tree_median3(__pyx_t_7sklearn_5_tree_DTYPE_t *, __pyx_t_7sklearn_5_tree_SIZE_t); /*proto*/ static void __pyx_f_7sklearn_5_tree_introsort(__pyx_t_7sklearn_5_tree_DTYPE_t *, __pyx_t_7sklearn_5_tree_SIZE_t *, __pyx_t_7sklearn_5_tree_SIZE_t, int); /*proto*/ static CYTHON_INLINE void __pyx_f_7sklearn_5_tree_sift_down(__pyx_t_7sklearn_5_tree_DTYPE_t *, __pyx_t_7sklearn_5_tree_SIZE_t *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t); /*proto*/ static void __pyx_f_7sklearn_5_tree_heapsort(__pyx_t_7sklearn_5_tree_DTYPE_t *, __pyx_t_7sklearn_5_tree_SIZE_t *, __pyx_t_7sklearn_5_tree_SIZE_t); /*proto*/ static int __pyx_f_7sklearn_5_tree_compare_SIZE_t(void const *, void const *); /*proto*/ static CYTHON_INLINE void __pyx_f_7sklearn_5_tree_binary_search(__pyx_t_7sklearn_5_tree_INT32_t *, __pyx_t_7sklearn_5_tree_INT32_t, __pyx_t_7sklearn_5_tree_INT32_t, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t *, __pyx_t_7sklearn_5_tree_INT32_t *); /*proto*/ static CYTHON_INLINE void __pyx_f_7sklearn_5_tree_extract_nnz_index_to_samples(__pyx_t_7sklearn_5_tree_INT32_t *, __pyx_t_7sklearn_5_tree_DTYPE_t *, __pyx_t_7sklearn_5_tree_INT32_t, __pyx_t_7sklearn_5_tree_INT32_t, __pyx_t_7sklearn_5_tree_SIZE_t *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t *, __pyx_t_7sklearn_5_tree_DTYPE_t *, __pyx_t_7sklearn_5_tree_SIZE_t *, __pyx_t_7sklearn_5_tree_SIZE_t *); /*proto*/ static CYTHON_INLINE void __pyx_f_7sklearn_5_tree_extract_nnz_binary_search(__pyx_t_7sklearn_5_tree_INT32_t *, __pyx_t_7sklearn_5_tree_DTYPE_t *, __pyx_t_7sklearn_5_tree_INT32_t, __pyx_t_7sklearn_5_tree_INT32_t, __pyx_t_7sklearn_5_tree_SIZE_t *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t *, __pyx_t_7sklearn_5_tree_DTYPE_t *, __pyx_t_7sklearn_5_tree_SIZE_t *, __pyx_t_7sklearn_5_tree_SIZE_t *, __pyx_t_7sklearn_5_tree_SIZE_t *, int *); /*proto*/ static CYTHON_INLINE void __pyx_f_7sklearn_5_tree_sparse_swap(__pyx_t_7sklearn_5_tree_SIZE_t *, __pyx_t_7sklearn_5_tree_SIZE_t *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t); /*proto*/ static CYTHON_INLINE int __pyx_f_7sklearn_5_tree__add_to_frontier(struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord *, struct __pyx_obj_7sklearn_5_tree_PriorityHeap *); /*proto*/ static CYTHON_INLINE __pyx_t_7sklearn_5_tree_UINT32_t __pyx_f_7sklearn_5_tree_our_rand_r(__pyx_t_7sklearn_5_tree_UINT32_t *); /*proto*/ static CYTHON_INLINE PyArrayObject *__pyx_f_7sklearn_5_tree_sizet_ptr_to_ndarray(__pyx_t_7sklearn_5_tree_SIZE_t *, __pyx_t_7sklearn_5_tree_SIZE_t); /*proto*/ static CYTHON_INLINE __pyx_t_7sklearn_5_tree_SIZE_t __pyx_f_7sklearn_5_tree_rand_int(__pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_UINT32_t *); /*proto*/ static CYTHON_INLINE double __pyx_f_7sklearn_5_tree_rand_uniform(double, double, __pyx_t_7sklearn_5_tree_UINT32_t *); /*proto*/ static CYTHON_INLINE double __pyx_f_7sklearn_5_tree_log(double); /*proto*/ static __pyx_t_7sklearn_5_tree_DTYPE_t *__pyx_fuse_0__pyx_f_7sklearn_5_tree_safe_realloc(__pyx_t_7sklearn_5_tree_DTYPE_t **, size_t); /*proto*/ static __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_fuse_1__pyx_f_7sklearn_5_tree_safe_realloc(__pyx_t_7sklearn_5_tree_SIZE_t **, size_t); /*proto*/ static unsigned char *__pyx_fuse_2__pyx_f_7sklearn_5_tree_safe_realloc(unsigned char **, size_t); /*proto*/ static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_7sklearn_5_tree_SIZE_t = { "SIZE_t", NULL, sizeof(__pyx_t_7sklearn_5_tree_SIZE_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_7sklearn_5_tree_SIZE_t) ? 'U' : 'I', IS_UNSIGNED(__pyx_t_7sklearn_5_tree_SIZE_t), 0 }; static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_7sklearn_5_tree_DOUBLE_t = { "DOUBLE_t", NULL, sizeof(__pyx_t_7sklearn_5_tree_DOUBLE_t), { 0 }, 0, 'R', 0, 0 }; static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_7sklearn_5_tree_DTYPE_t = { "DTYPE_t", NULL, sizeof(__pyx_t_7sklearn_5_tree_DTYPE_t), { 0 }, 0, 'R', 0, 0 }; static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_7sklearn_5_tree_INT32_t = { "INT32_t", NULL, sizeof(__pyx_t_7sklearn_5_tree_INT32_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_7sklearn_5_tree_INT32_t) ? 'U' : 'I', IS_UNSIGNED(__pyx_t_7sklearn_5_tree_INT32_t), 0 }; static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_float64_t = { "float64_t", NULL, sizeof(__pyx_t_5numpy_float64_t), { 0 }, 0, 'R', 0, 0 }; #define __Pyx_MODULE_NAME "sklearn._tree" int __pyx_module_is_main_sklearn___tree = 0; /* Implementation of 'sklearn._tree' */ static PyObject *__pyx_builtin_MemoryError; static PyObject *__pyx_builtin_range; static PyObject *__pyx_builtin_ValueError; static PyObject *__pyx_builtin_RuntimeError; static int __pyx_pf_7sklearn_5_tree_5Stack___cinit__(struct __pyx_obj_7sklearn_5_tree_Stack *__pyx_v_self, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_capacity); /* proto */ static void __pyx_pf_7sklearn_5_tree_5Stack_2__dealloc__(struct __pyx_obj_7sklearn_5_tree_Stack *__pyx_v_self); /* proto */ static int __pyx_pf_7sklearn_5_tree_12PriorityHeap___cinit__(struct __pyx_obj_7sklearn_5_tree_PriorityHeap *__pyx_v_self, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_capacity); /* proto */ static void __pyx_pf_7sklearn_5_tree_12PriorityHeap_2__dealloc__(struct __pyx_obj_7sklearn_5_tree_PriorityHeap *__pyx_v_self); /* proto */ static int __pyx_pf_7sklearn_5_tree_23ClassificationCriterion___cinit__(struct __pyx_obj_7sklearn_5_tree_ClassificationCriterion *__pyx_v_self, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_outputs, PyArrayObject *__pyx_v_n_classes); /* proto */ static void __pyx_pf_7sklearn_5_tree_23ClassificationCriterion_2__dealloc__(struct __pyx_obj_7sklearn_5_tree_ClassificationCriterion *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_23ClassificationCriterion_4__reduce__(struct __pyx_obj_7sklearn_5_tree_ClassificationCriterion *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_23ClassificationCriterion_6__getstate__(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_ClassificationCriterion *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_23ClassificationCriterion_8__setstate__(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_ClassificationCriterion *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_d); /* proto */ static int __pyx_pf_7sklearn_5_tree_19RegressionCriterion___cinit__(struct __pyx_obj_7sklearn_5_tree_RegressionCriterion *__pyx_v_self, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_outputs); /* proto */ static void __pyx_pf_7sklearn_5_tree_19RegressionCriterion_2__dealloc__(struct __pyx_obj_7sklearn_5_tree_RegressionCriterion *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_19RegressionCriterion_4__reduce__(struct __pyx_obj_7sklearn_5_tree_RegressionCriterion *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_19RegressionCriterion_6__getstate__(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_RegressionCriterion *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_19RegressionCriterion_8__setstate__(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_RegressionCriterion *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_d); /* proto */ static int __pyx_pf_7sklearn_5_tree_8Splitter___cinit__(struct __pyx_obj_7sklearn_5_tree_Splitter *__pyx_v_self, struct __pyx_obj_7sklearn_5_tree_Criterion *__pyx_v_criterion, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_max_features, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_min_samples_leaf, double __pyx_v_min_weight_leaf, PyObject *__pyx_v_random_state); /* proto */ static void __pyx_pf_7sklearn_5_tree_8Splitter_2__dealloc__(struct __pyx_obj_7sklearn_5_tree_Splitter *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_8Splitter_4__getstate__(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_Splitter *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_8Splitter_6__setstate__(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_Splitter *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_d); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_8Splitter_9criterion___get__(struct __pyx_obj_7sklearn_5_tree_Splitter *__pyx_v_self); /* proto */ static int __pyx_pf_7sklearn_5_tree_8Splitter_9criterion_2__set__(struct __pyx_obj_7sklearn_5_tree_Splitter *__pyx_v_self, PyObject *__pyx_v_value); /* proto */ static int __pyx_pf_7sklearn_5_tree_8Splitter_9criterion_4__del__(struct __pyx_obj_7sklearn_5_tree_Splitter *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_8Splitter_12max_features___get__(struct __pyx_obj_7sklearn_5_tree_Splitter *__pyx_v_self); /* proto */ static int __pyx_pf_7sklearn_5_tree_8Splitter_12max_features_2__set__(struct __pyx_obj_7sklearn_5_tree_Splitter *__pyx_v_self, PyObject *__pyx_v_value); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_8Splitter_16min_samples_leaf___get__(struct __pyx_obj_7sklearn_5_tree_Splitter *__pyx_v_self); /* proto */ static int __pyx_pf_7sklearn_5_tree_8Splitter_16min_samples_leaf_2__set__(struct __pyx_obj_7sklearn_5_tree_Splitter *__pyx_v_self, PyObject *__pyx_v_value); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_8Splitter_15min_weight_leaf___get__(struct __pyx_obj_7sklearn_5_tree_Splitter *__pyx_v_self); /* proto */ static int __pyx_pf_7sklearn_5_tree_8Splitter_15min_weight_leaf_2__set__(struct __pyx_obj_7sklearn_5_tree_Splitter *__pyx_v_self, PyObject *__pyx_v_value); /* proto */ static int __pyx_pf_7sklearn_5_tree_17BaseDenseSplitter___cinit__(struct __pyx_obj_7sklearn_5_tree_BaseDenseSplitter *__pyx_v_self, CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_Criterion *__pyx_v_criterion, CYTHON_UNUSED __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_max_features, CYTHON_UNUSED __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_min_samples_leaf, CYTHON_UNUSED double __pyx_v_min_weight_leaf, CYTHON_UNUSED PyObject *__pyx_v_random_state); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_12BestSplitter___reduce__(struct __pyx_obj_7sklearn_5_tree_BestSplitter *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_14RandomSplitter___reduce__(struct __pyx_obj_7sklearn_5_tree_RandomSplitter *__pyx_v_self); /* proto */ static int __pyx_pf_7sklearn_5_tree_19PresortBestSplitter___cinit__(struct __pyx_obj_7sklearn_5_tree_PresortBestSplitter *__pyx_v_self, CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_Criterion *__pyx_v_criterion, CYTHON_UNUSED __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_max_features, CYTHON_UNUSED __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_min_samples_leaf, CYTHON_UNUSED double __pyx_v_min_weight_leaf, CYTHON_UNUSED PyObject *__pyx_v_random_state); /* proto */ static void __pyx_pf_7sklearn_5_tree_19PresortBestSplitter_2__dealloc__(struct __pyx_obj_7sklearn_5_tree_PresortBestSplitter *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_19PresortBestSplitter_4__reduce__(struct __pyx_obj_7sklearn_5_tree_PresortBestSplitter *__pyx_v_self); /* proto */ static int __pyx_pf_7sklearn_5_tree_18BaseSparseSplitter___cinit__(struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter *__pyx_v_self, CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_Criterion *__pyx_v_criterion, CYTHON_UNUSED __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_max_features, CYTHON_UNUSED __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_min_samples_leaf, CYTHON_UNUSED double __pyx_v_min_weight_leaf, CYTHON_UNUSED PyObject *__pyx_v_random_state); /* proto */ static void __pyx_pf_7sklearn_5_tree_18BaseSparseSplitter_2__dealloc__(struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_18BestSparseSplitter___reduce__(struct __pyx_obj_7sklearn_5_tree_BestSparseSplitter *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_20RandomSparseSplitter___reduce__(struct __pyx_obj_7sklearn_5_tree_RandomSparseSplitter *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_11TreeBuilder_build(struct __pyx_obj_7sklearn_5_tree_TreeBuilder *__pyx_v_self, struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_tree, PyObject *__pyx_v_X, PyArrayObject *__pyx_v_y, PyArrayObject *__pyx_v_sample_weight); /* proto */ static int __pyx_pf_7sklearn_5_tree_21DepthFirstTreeBuilder___cinit__(struct __pyx_obj_7sklearn_5_tree_DepthFirstTreeBuilder *__pyx_v_self, struct __pyx_obj_7sklearn_5_tree_Splitter *__pyx_v_splitter, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_min_samples_split, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_min_samples_leaf, double __pyx_v_min_weight_leaf, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_max_depth); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_21DepthFirstTreeBuilder_2build(struct __pyx_obj_7sklearn_5_tree_DepthFirstTreeBuilder *__pyx_v_self, struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_tree, PyObject *__pyx_v_X, PyArrayObject *__pyx_v_y, PyArrayObject *__pyx_v_sample_weight); /* proto */ static int __pyx_pf_7sklearn_5_tree_20BestFirstTreeBuilder___cinit__(struct __pyx_obj_7sklearn_5_tree_BestFirstTreeBuilder *__pyx_v_self, struct __pyx_obj_7sklearn_5_tree_Splitter *__pyx_v_splitter, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_min_samples_split, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_min_samples_leaf, PyObject *__pyx_v_min_weight_leaf, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_max_depth, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_max_leaf_nodes); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_20BestFirstTreeBuilder_2build(struct __pyx_obj_7sklearn_5_tree_BestFirstTreeBuilder *__pyx_v_self, struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_tree, PyObject *__pyx_v_X, PyArrayObject *__pyx_v_y, PyArrayObject *__pyx_v_sample_weight); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_4Tree_9n_classes___get__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_4Tree_13children_left___get__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_4Tree_14children_right___get__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_4Tree_7feature___get__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_4Tree_9threshold___get__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_4Tree_8impurity___get__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_4Tree_14n_node_samples___get__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_4Tree_23weighted_n_node_samples___get__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_4Tree_5value___get__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self); /* proto */ static int __pyx_pf_7sklearn_5_tree_4Tree___cinit__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self, int __pyx_v_n_features, PyArrayObject *__pyx_v_n_classes, int __pyx_v_n_outputs); /* proto */ static void __pyx_pf_7sklearn_5_tree_4Tree_2__dealloc__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_4Tree_4__reduce__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_4Tree_6__getstate__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_4Tree_8__setstate__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_d); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_4Tree_10predict(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_X); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_4Tree_12apply(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_X); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_4Tree_14compute_feature_importances(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_normalize); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_4Tree_10n_features___get__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self); /* proto */ static int __pyx_pf_7sklearn_5_tree_4Tree_10n_features_2__set__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_value); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_4Tree_9n_outputs___get__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self); /* proto */ static int __pyx_pf_7sklearn_5_tree_4Tree_9n_outputs_2__set__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_value); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_4Tree_13max_n_classes___get__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self); /* proto */ static int __pyx_pf_7sklearn_5_tree_4Tree_13max_n_classes_2__set__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_value); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_4Tree_9max_depth___get__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self); /* proto */ static int __pyx_pf_7sklearn_5_tree_4Tree_9max_depth_2__set__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_value); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_4Tree_10node_count___get__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self); /* proto */ static int __pyx_pf_7sklearn_5_tree_4Tree_10node_count_2__set__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_value); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree_4Tree_8capacity___get__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self); /* proto */ static int __pyx_pf_7sklearn_5_tree_4Tree_8capacity_2__set__(struct __pyx_obj_7sklearn_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_value); /* proto */ static PyObject *__pyx_pf_7sklearn_5_tree__realloc_test(CYTHON_UNUSED PyObject *__pyx_self); /* proto */ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ static void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info); /* proto */ static PyObject *__pyx_tp_new_7sklearn_5_tree_Stack(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new_7sklearn_5_tree_PriorityHeap(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new_7sklearn_5_tree_Criterion(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new_7sklearn_5_tree_Splitter(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new_7sklearn_5_tree_Tree(PyTypeObject *t, PyObject *a, PyObject *k); 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/* "sklearn/_tree.pyx":117 * * cdef bint is_empty(self) nogil: * return self.top <= 0 # <<<<<<<<<<<<<< * * cdef int push(self, SIZE_t start, SIZE_t end, SIZE_t depth, SIZE_t parent, */ __pyx_r = (__pyx_v_self->top <= 0); goto __pyx_L0; /* "sklearn/_tree.pyx":116 * free(self.stack_) * * cdef bint is_empty(self) nogil: # <<<<<<<<<<<<<< * return self.top <= 0 * */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "sklearn/_tree.pyx":119 * return self.top <= 0 * * cdef int push(self, SIZE_t start, SIZE_t end, SIZE_t depth, SIZE_t parent, # <<<<<<<<<<<<<< * bint is_left, double impurity, * SIZE_t n_constant_features) nogil: */ static int __pyx_f_7sklearn_5_tree_5Stack_push(struct __pyx_obj_7sklearn_5_tree_Stack *__pyx_v_self, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_start, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_end, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_depth, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_parent, int __pyx_v_is_left, double __pyx_v_impurity, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_constant_features) { __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_top; struct __pyx_t_7sklearn_5_tree_StackRecord *__pyx_v_stack; int __pyx_r; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_1; int __pyx_t_2; struct __pyx_t_7sklearn_5_tree_StackRecord *__pyx_t_3; /* "sklearn/_tree.pyx":126 * Returns 0 if successful; -1 on out of memory error. * """ * cdef SIZE_t top = self.top # <<<<<<<<<<<<<< * cdef StackRecord* stack = NULL * */ __pyx_t_1 = __pyx_v_self->top; __pyx_v_top = __pyx_t_1; /* "sklearn/_tree.pyx":127 * """ * cdef SIZE_t top = self.top * cdef StackRecord* stack = NULL # <<<<<<<<<<<<<< * * # Resize if capacity not sufficient */ __pyx_v_stack = NULL; /* "sklearn/_tree.pyx":130 * * # Resize if capacity not sufficient * if top >= self.capacity: # <<<<<<<<<<<<<< * self.capacity *= 2 * stack = realloc(self.stack_, */ __pyx_t_2 = ((__pyx_v_top >= __pyx_v_self->capacity) != 0); if (__pyx_t_2) { /* "sklearn/_tree.pyx":131 * # Resize if capacity not sufficient * if top >= self.capacity: * self.capacity *= 2 # <<<<<<<<<<<<<< * stack = realloc(self.stack_, * self.capacity * sizeof(StackRecord)) */ __pyx_v_self->capacity = (__pyx_v_self->capacity * 2); /* "sklearn/_tree.pyx":132 * if top >= self.capacity: * self.capacity *= 2 * stack = realloc(self.stack_, # <<<<<<<<<<<<<< * self.capacity * sizeof(StackRecord)) * if stack == NULL: */ __pyx_v_stack = ((struct __pyx_t_7sklearn_5_tree_StackRecord *)realloc(__pyx_v_self->stack_, (__pyx_v_self->capacity * (sizeof(struct __pyx_t_7sklearn_5_tree_StackRecord))))); /* "sklearn/_tree.pyx":134 * stack = realloc(self.stack_, * self.capacity * sizeof(StackRecord)) * if stack == NULL: # <<<<<<<<<<<<<< * # no free; __dealloc__ handles that * return -1 */ __pyx_t_2 = ((__pyx_v_stack == NULL) != 0); if (__pyx_t_2) { /* "sklearn/_tree.pyx":136 * if stack == NULL: * # no free; __dealloc__ handles that * return -1 # <<<<<<<<<<<<<< * self.stack_ = stack * */ __pyx_r = -1; goto __pyx_L0; } /* "sklearn/_tree.pyx":137 * # no free; __dealloc__ handles that * return -1 * self.stack_ = stack # <<<<<<<<<<<<<< * * stack = self.stack_ */ __pyx_v_self->stack_ = __pyx_v_stack; goto __pyx_L3; } __pyx_L3:; /* "sklearn/_tree.pyx":139 * self.stack_ = stack * * stack = self.stack_ # <<<<<<<<<<<<<< * stack[top].start = start * stack[top].end = end */ __pyx_t_3 = __pyx_v_self->stack_; __pyx_v_stack = __pyx_t_3; /* "sklearn/_tree.pyx":140 * * stack = self.stack_ * stack[top].start = start # <<<<<<<<<<<<<< * stack[top].end = end * stack[top].depth = depth */ (__pyx_v_stack[__pyx_v_top]).start = __pyx_v_start; /* "sklearn/_tree.pyx":141 * stack = self.stack_ * stack[top].start = start * stack[top].end = end # <<<<<<<<<<<<<< * stack[top].depth = depth * stack[top].parent = parent */ (__pyx_v_stack[__pyx_v_top]).end = __pyx_v_end; /* "sklearn/_tree.pyx":142 * stack[top].start = start * stack[top].end = end * stack[top].depth = depth # <<<<<<<<<<<<<< * stack[top].parent = parent * stack[top].is_left = is_left */ (__pyx_v_stack[__pyx_v_top]).depth = __pyx_v_depth; /* "sklearn/_tree.pyx":143 * stack[top].end = end * stack[top].depth = depth * stack[top].parent = parent # <<<<<<<<<<<<<< * stack[top].is_left = is_left * stack[top].impurity = impurity */ (__pyx_v_stack[__pyx_v_top]).parent = __pyx_v_parent; /* "sklearn/_tree.pyx":144 * stack[top].depth = depth * stack[top].parent = parent * stack[top].is_left = is_left # <<<<<<<<<<<<<< * stack[top].impurity = impurity * stack[top].n_constant_features = n_constant_features */ (__pyx_v_stack[__pyx_v_top]).is_left = __pyx_v_is_left; /* "sklearn/_tree.pyx":145 * stack[top].parent = parent * stack[top].is_left = is_left * stack[top].impurity = impurity # <<<<<<<<<<<<<< * stack[top].n_constant_features = n_constant_features * */ (__pyx_v_stack[__pyx_v_top]).impurity = __pyx_v_impurity; /* "sklearn/_tree.pyx":146 * stack[top].is_left = is_left * stack[top].impurity = impurity * stack[top].n_constant_features = n_constant_features # <<<<<<<<<<<<<< * * # Increment stack pointer */ (__pyx_v_stack[__pyx_v_top]).n_constant_features = __pyx_v_n_constant_features; /* "sklearn/_tree.pyx":149 * * # Increment stack pointer * self.top = top + 1 # <<<<<<<<<<<<<< * return 0 * */ __pyx_v_self->top = (__pyx_v_top + 1); /* "sklearn/_tree.pyx":150 * # Increment stack pointer * self.top = top + 1 * return 0 # <<<<<<<<<<<<<< * * cdef int pop(self, StackRecord* res) nogil: */ __pyx_r = 0; goto __pyx_L0; /* "sklearn/_tree.pyx":119 * return self.top <= 0 * * cdef int push(self, SIZE_t start, SIZE_t end, SIZE_t depth, SIZE_t parent, # <<<<<<<<<<<<<< * bint is_left, double impurity, * SIZE_t n_constant_features) nogil: */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "sklearn/_tree.pyx":152 * return 0 * * cdef int pop(self, StackRecord* res) nogil: # <<<<<<<<<<<<<< * """Remove the top element from the stack and copy to ``res``. * */ static int __pyx_f_7sklearn_5_tree_5Stack_pop(struct __pyx_obj_7sklearn_5_tree_Stack *__pyx_v_self, struct __pyx_t_7sklearn_5_tree_StackRecord *__pyx_v_res) { __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_top; struct __pyx_t_7sklearn_5_tree_StackRecord *__pyx_v_stack; int __pyx_r; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_1; struct __pyx_t_7sklearn_5_tree_StackRecord *__pyx_t_2; int __pyx_t_3; /* "sklearn/_tree.pyx":158 * otherwise. * """ * cdef SIZE_t top = self.top # <<<<<<<<<<<<<< * cdef StackRecord* stack = self.stack_ * */ __pyx_t_1 = __pyx_v_self->top; __pyx_v_top = __pyx_t_1; /* "sklearn/_tree.pyx":159 * """ * cdef SIZE_t top = self.top * cdef StackRecord* stack = self.stack_ # <<<<<<<<<<<<<< * * if top <= 0: */ __pyx_t_2 = __pyx_v_self->stack_; __pyx_v_stack = __pyx_t_2; /* "sklearn/_tree.pyx":161 * cdef StackRecord* stack = self.stack_ * * if top <= 0: # <<<<<<<<<<<<<< * return -1 * */ __pyx_t_3 = ((__pyx_v_top <= 0) != 0); if (__pyx_t_3) { /* "sklearn/_tree.pyx":162 * * if top <= 0: * return -1 # <<<<<<<<<<<<<< * * res[0] = stack[top - 1] */ __pyx_r = -1; goto __pyx_L0; } /* "sklearn/_tree.pyx":164 * return -1 * * res[0] = stack[top - 1] # <<<<<<<<<<<<<< * self.top = top - 1 * */ (__pyx_v_res[0]) = (__pyx_v_stack[(__pyx_v_top - 1)]); /* "sklearn/_tree.pyx":165 * * res[0] = stack[top - 1] * self.top = top - 1 # <<<<<<<<<<<<<< * * return 0 */ __pyx_v_self->top = (__pyx_v_top - 1); /* "sklearn/_tree.pyx":167 * self.top = top - 1 * * return 0 # <<<<<<<<<<<<<< * * */ __pyx_r = 0; goto __pyx_L0; /* "sklearn/_tree.pyx":152 * return 0 * * cdef int pop(self, StackRecord* res) nogil: # <<<<<<<<<<<<<< * """Remove the top element from the stack and copy to ``res``. * */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "sklearn/_tree.pyx":174 * # ============================================================================= * * cdef void heapify_up(PriorityHeapRecord* heap, SIZE_t pos) nogil: # <<<<<<<<<<<<<< * """Restore heap invariant parent.improvement > child.improvement from * ``pos`` upwards. """ */ static void __pyx_f_7sklearn_5_tree_heapify_up(struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord *__pyx_v_heap, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_pos) { __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_parent_pos; int __pyx_t_1; struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord __pyx_t_2; struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord __pyx_t_3; /* "sklearn/_tree.pyx":177 * """Restore heap invariant parent.improvement > child.improvement from * ``pos`` upwards. """ * if pos == 0: # <<<<<<<<<<<<<< * return * */ __pyx_t_1 = ((__pyx_v_pos == 0) != 0); if (__pyx_t_1) { /* "sklearn/_tree.pyx":178 * ``pos`` upwards. """ * if pos == 0: * return # <<<<<<<<<<<<<< * * cdef SIZE_t parent_pos = (pos - 1) / 2 */ goto __pyx_L0; } /* "sklearn/_tree.pyx":180 * return * * cdef SIZE_t parent_pos = (pos - 1) / 2 # <<<<<<<<<<<<<< * * if heap[parent_pos].improvement < heap[pos].improvement: */ __pyx_v_parent_pos = ((__pyx_v_pos - 1) / 2); /* "sklearn/_tree.pyx":182 * cdef SIZE_t parent_pos = (pos - 1) / 2 * * if heap[parent_pos].improvement < heap[pos].improvement: # <<<<<<<<<<<<<< * heap[parent_pos], heap[pos] = heap[pos], heap[parent_pos] * heapify_up(heap, parent_pos) */ __pyx_t_1 = (((__pyx_v_heap[__pyx_v_parent_pos]).improvement < (__pyx_v_heap[__pyx_v_pos]).improvement) != 0); if (__pyx_t_1) { /* "sklearn/_tree.pyx":183 * * if heap[parent_pos].improvement < heap[pos].improvement: * heap[parent_pos], heap[pos] = heap[pos], heap[parent_pos] # <<<<<<<<<<<<<< * heapify_up(heap, parent_pos) * */ __pyx_t_2 = (__pyx_v_heap[__pyx_v_pos]); __pyx_t_3 = (__pyx_v_heap[__pyx_v_parent_pos]); (__pyx_v_heap[__pyx_v_parent_pos]) = __pyx_t_2; (__pyx_v_heap[__pyx_v_pos]) = __pyx_t_3; /* "sklearn/_tree.pyx":184 * if heap[parent_pos].improvement < heap[pos].improvement: * heap[parent_pos], heap[pos] = heap[pos], heap[parent_pos] * heapify_up(heap, parent_pos) # <<<<<<<<<<<<<< * * */ __pyx_f_7sklearn_5_tree_heapify_up(__pyx_v_heap, __pyx_v_parent_pos); goto __pyx_L4; } __pyx_L4:; /* "sklearn/_tree.pyx":174 * # ============================================================================= * * cdef void heapify_up(PriorityHeapRecord* heap, SIZE_t pos) nogil: # <<<<<<<<<<<<<< * """Restore heap invariant parent.improvement > child.improvement from * ``pos`` upwards. """ */ /* function exit code */ __pyx_L0:; } /* "sklearn/_tree.pyx":187 * * * cdef void heapify_down(PriorityHeapRecord* heap, SIZE_t pos, # <<<<<<<<<<<<<< * SIZE_t heap_length) nogil: * """Restore heap invariant parent.improvement > children.improvement from */ static void __pyx_f_7sklearn_5_tree_heapify_down(struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord *__pyx_v_heap, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_pos, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_heap_length) { __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_left_pos; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_right_pos; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_largest; int __pyx_t_1; int __pyx_t_2; struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord __pyx_t_3; struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord __pyx_t_4; /* "sklearn/_tree.pyx":191 * """Restore heap invariant parent.improvement > children.improvement from * ``pos`` downwards. """ * cdef SIZE_t left_pos = 2 * (pos + 1) - 1 # <<<<<<<<<<<<<< * cdef SIZE_t right_pos = 2 * (pos + 1) * cdef SIZE_t largest = pos */ __pyx_v_left_pos = ((2 * (__pyx_v_pos + 1)) - 1); /* "sklearn/_tree.pyx":192 * ``pos`` downwards. """ * cdef SIZE_t left_pos = 2 * (pos + 1) - 1 * cdef SIZE_t right_pos = 2 * (pos + 1) # <<<<<<<<<<<<<< * cdef SIZE_t largest = pos * */ __pyx_v_right_pos = (2 * (__pyx_v_pos + 1)); /* "sklearn/_tree.pyx":193 * cdef SIZE_t left_pos = 2 * (pos + 1) - 1 * cdef SIZE_t right_pos = 2 * (pos + 1) * cdef SIZE_t largest = pos # <<<<<<<<<<<<<< * * if (left_pos < heap_length and */ __pyx_v_largest = __pyx_v_pos; /* "sklearn/_tree.pyx":195 * cdef SIZE_t largest = pos * * if (left_pos < heap_length and # <<<<<<<<<<<<<< * heap[left_pos].improvement > heap[largest].improvement): * largest = left_pos */ __pyx_t_2 = ((__pyx_v_left_pos < __pyx_v_heap_length) != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L4_bool_binop_done; } /* "sklearn/_tree.pyx":196 * * if (left_pos < heap_length and * heap[left_pos].improvement > heap[largest].improvement): # <<<<<<<<<<<<<< * largest = left_pos * */ __pyx_t_2 = (((__pyx_v_heap[__pyx_v_left_pos]).improvement > (__pyx_v_heap[__pyx_v_largest]).improvement) != 0); __pyx_t_1 = __pyx_t_2; __pyx_L4_bool_binop_done:; if (__pyx_t_1) { /* "sklearn/_tree.pyx":197 * if (left_pos < heap_length and * heap[left_pos].improvement > heap[largest].improvement): * largest = left_pos # <<<<<<<<<<<<<< * * if (right_pos < heap_length and */ __pyx_v_largest = __pyx_v_left_pos; goto __pyx_L3; } __pyx_L3:; /* "sklearn/_tree.pyx":199 * largest = left_pos * * if (right_pos < heap_length and # <<<<<<<<<<<<<< * heap[right_pos].improvement > heap[largest].improvement): * largest = right_pos */ __pyx_t_2 = ((__pyx_v_right_pos < __pyx_v_heap_length) != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L7_bool_binop_done; } /* "sklearn/_tree.pyx":200 * * if (right_pos < heap_length and * heap[right_pos].improvement > heap[largest].improvement): # <<<<<<<<<<<<<< * largest = right_pos * */ __pyx_t_2 = (((__pyx_v_heap[__pyx_v_right_pos]).improvement > (__pyx_v_heap[__pyx_v_largest]).improvement) != 0); __pyx_t_1 = __pyx_t_2; __pyx_L7_bool_binop_done:; if (__pyx_t_1) { /* "sklearn/_tree.pyx":201 * if (right_pos < heap_length and * heap[right_pos].improvement > heap[largest].improvement): * largest = right_pos # <<<<<<<<<<<<<< * * if largest != pos: */ __pyx_v_largest = __pyx_v_right_pos; goto __pyx_L6; } __pyx_L6:; /* "sklearn/_tree.pyx":203 * largest = right_pos * * if largest != pos: # <<<<<<<<<<<<<< * heap[pos], heap[largest] = heap[largest], heap[pos] * heapify_down(heap, largest, heap_length) */ __pyx_t_1 = ((__pyx_v_largest != __pyx_v_pos) != 0); if (__pyx_t_1) { /* "sklearn/_tree.pyx":204 * * if largest != pos: * heap[pos], heap[largest] = heap[largest], heap[pos] # <<<<<<<<<<<<<< * heapify_down(heap, largest, heap_length) * */ __pyx_t_3 = (__pyx_v_heap[__pyx_v_largest]); __pyx_t_4 = (__pyx_v_heap[__pyx_v_pos]); (__pyx_v_heap[__pyx_v_pos]) = __pyx_t_3; (__pyx_v_heap[__pyx_v_largest]) = __pyx_t_4; /* "sklearn/_tree.pyx":205 * if largest != pos: * heap[pos], heap[largest] = heap[largest], heap[pos] * heapify_down(heap, largest, heap_length) # <<<<<<<<<<<<<< * * */ __pyx_f_7sklearn_5_tree_heapify_down(__pyx_v_heap, __pyx_v_largest, __pyx_v_heap_length); goto __pyx_L9; } __pyx_L9:; /* "sklearn/_tree.pyx":187 * * * cdef void heapify_down(PriorityHeapRecord* heap, SIZE_t pos, # <<<<<<<<<<<<<< * SIZE_t heap_length) nogil: * """Restore heap invariant parent.improvement > children.improvement from */ /* function exit code */ } /* "sklearn/_tree.pyx":228 * """ * * def __cinit__(self, SIZE_t capacity): # <<<<<<<<<<<<<< * self.capacity = capacity * self.heap_ptr = 0 */ /* Python wrapper */ static int __pyx_pw_7sklearn_5_tree_12PriorityHeap_1__cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static int __pyx_pw_7sklearn_5_tree_12PriorityHeap_1__cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_capacity; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__cinit__ (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_capacity,0}; PyObject* values[1] = {0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_capacity)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__cinit__") < 0)) {__pyx_filename = __pyx_f[0]; 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/* "sklearn/_tree.pyx":232 * self.heap_ptr = 0 * self.heap_ = malloc(capacity * sizeof(PriorityHeapRecord)) * if self.heap_ == NULL: # <<<<<<<<<<<<<< * raise MemoryError() * */ __pyx_t_1 = ((__pyx_v_self->heap_ == NULL) != 0); if (__pyx_t_1) { /* "sklearn/_tree.pyx":233 * self.heap_ = malloc(capacity * sizeof(PriorityHeapRecord)) * if self.heap_ == NULL: * raise MemoryError() # <<<<<<<<<<<<<< * * def __dealloc__(self): */ PyErr_NoMemory(); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 233; __pyx_clineno = __LINE__; goto __pyx_L1_error;} } /* "sklearn/_tree.pyx":228 * """ * * def __cinit__(self, SIZE_t capacity): # <<<<<<<<<<<<<< * self.capacity = capacity * self.heap_ptr = 0 */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_AddTraceback("sklearn._tree.PriorityHeap.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "sklearn/_tree.pyx":235 * raise MemoryError() * * def __dealloc__(self): # <<<<<<<<<<<<<< * free(self.heap_) * */ /* Python wrapper */ static void __pyx_pw_7sklearn_5_tree_12PriorityHeap_3__dealloc__(PyObject *__pyx_v_self); /*proto*/ static void __pyx_pw_7sklearn_5_tree_12PriorityHeap_3__dealloc__(PyObject *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0); __pyx_pf_7sklearn_5_tree_12PriorityHeap_2__dealloc__(((struct __pyx_obj_7sklearn_5_tree_PriorityHeap *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); } static void __pyx_pf_7sklearn_5_tree_12PriorityHeap_2__dealloc__(struct __pyx_obj_7sklearn_5_tree_PriorityHeap *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__", 0); /* "sklearn/_tree.pyx":236 * * def __dealloc__(self): * free(self.heap_) # <<<<<<<<<<<<<< * * cdef bint is_empty(self) nogil: */ free(__pyx_v_self->heap_); /* "sklearn/_tree.pyx":235 * raise MemoryError() * * def __dealloc__(self): # <<<<<<<<<<<<<< * free(self.heap_) * */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "sklearn/_tree.pyx":238 * free(self.heap_) * * cdef bint is_empty(self) nogil: # <<<<<<<<<<<<<< * return self.heap_ptr <= 0 * */ static int __pyx_f_7sklearn_5_tree_12PriorityHeap_is_empty(struct __pyx_obj_7sklearn_5_tree_PriorityHeap *__pyx_v_self) { int __pyx_r; /* "sklearn/_tree.pyx":239 * * cdef bint is_empty(self) nogil: * return self.heap_ptr <= 0 # <<<<<<<<<<<<<< * * cdef int push(self, SIZE_t node_id, SIZE_t start, SIZE_t end, SIZE_t pos, */ __pyx_r = (__pyx_v_self->heap_ptr <= 0); goto __pyx_L0; /* "sklearn/_tree.pyx":238 * free(self.heap_) * * cdef bint is_empty(self) nogil: # <<<<<<<<<<<<<< * return self.heap_ptr <= 0 * */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "sklearn/_tree.pyx":241 * return self.heap_ptr <= 0 * * cdef int push(self, SIZE_t node_id, SIZE_t start, SIZE_t end, SIZE_t pos, # <<<<<<<<<<<<<< * SIZE_t depth, bint is_leaf, double improvement, * double impurity, double impurity_left, */ static int __pyx_f_7sklearn_5_tree_12PriorityHeap_push(struct __pyx_obj_7sklearn_5_tree_PriorityHeap *__pyx_v_self, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_node_id, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_start, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_end, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_pos, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_depth, int __pyx_v_is_leaf, double __pyx_v_improvement, double __pyx_v_impurity, double __pyx_v_impurity_left, double __pyx_v_impurity_right) { __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_heap_ptr; struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord *__pyx_v_heap; int __pyx_r; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_1; int __pyx_t_2; struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord *__pyx_t_3; /* "sklearn/_tree.pyx":249 * Returns 0 if successful; -1 on out of memory error. * """ * cdef SIZE_t heap_ptr = self.heap_ptr # <<<<<<<<<<<<<< * cdef PriorityHeapRecord* heap = NULL * */ __pyx_t_1 = __pyx_v_self->heap_ptr; __pyx_v_heap_ptr = __pyx_t_1; /* "sklearn/_tree.pyx":250 * """ * cdef SIZE_t heap_ptr = self.heap_ptr * cdef PriorityHeapRecord* heap = NULL # <<<<<<<<<<<<<< * * # Resize if capacity not sufficient */ __pyx_v_heap = NULL; /* "sklearn/_tree.pyx":253 * * # Resize if capacity not sufficient * if heap_ptr >= self.capacity: # <<<<<<<<<<<<<< * self.capacity *= 2 * heap = realloc(self.heap_, */ __pyx_t_2 = ((__pyx_v_heap_ptr >= __pyx_v_self->capacity) != 0); if (__pyx_t_2) { /* "sklearn/_tree.pyx":254 * # Resize if capacity not sufficient * if heap_ptr >= self.capacity: * self.capacity *= 2 # <<<<<<<<<<<<<< * heap = realloc(self.heap_, * self.capacity * */ __pyx_v_self->capacity = (__pyx_v_self->capacity * 2); /* "sklearn/_tree.pyx":255 * if heap_ptr >= self.capacity: * self.capacity *= 2 * heap = realloc(self.heap_, # <<<<<<<<<<<<<< * self.capacity * * sizeof(PriorityHeapRecord)) */ __pyx_v_heap = ((struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord *)realloc(__pyx_v_self->heap_, (__pyx_v_self->capacity * (sizeof(struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord))))); /* "sklearn/_tree.pyx":258 * self.capacity * * sizeof(PriorityHeapRecord)) * if heap == NULL: # <<<<<<<<<<<<<< * # no free; __dealloc__ handles that * return -1 */ __pyx_t_2 = ((__pyx_v_heap == NULL) != 0); if (__pyx_t_2) { /* "sklearn/_tree.pyx":260 * if heap == NULL: * # no free; __dealloc__ handles that * return -1 # <<<<<<<<<<<<<< * self.heap_ = heap * */ __pyx_r = -1; goto __pyx_L0; } /* "sklearn/_tree.pyx":261 * # no free; __dealloc__ handles that * return -1 * self.heap_ = heap # <<<<<<<<<<<<<< * * # Put element as last element of heap */ __pyx_v_self->heap_ = __pyx_v_heap; goto __pyx_L3; } __pyx_L3:; /* "sklearn/_tree.pyx":264 * * # Put element as last element of heap * heap = self.heap_ # <<<<<<<<<<<<<< * heap[heap_ptr].node_id = node_id * heap[heap_ptr].start = start */ __pyx_t_3 = __pyx_v_self->heap_; __pyx_v_heap = __pyx_t_3; /* "sklearn/_tree.pyx":265 * # Put element as last element of heap * heap = self.heap_ * heap[heap_ptr].node_id = node_id # <<<<<<<<<<<<<< * heap[heap_ptr].start = start * heap[heap_ptr].end = end */ (__pyx_v_heap[__pyx_v_heap_ptr]).node_id = __pyx_v_node_id; /* "sklearn/_tree.pyx":266 * heap = self.heap_ * heap[heap_ptr].node_id = node_id * heap[heap_ptr].start = start # <<<<<<<<<<<<<< * heap[heap_ptr].end = end * heap[heap_ptr].pos = pos */ (__pyx_v_heap[__pyx_v_heap_ptr]).start = __pyx_v_start; /* "sklearn/_tree.pyx":267 * heap[heap_ptr].node_id = node_id * heap[heap_ptr].start = start * heap[heap_ptr].end = end # <<<<<<<<<<<<<< * heap[heap_ptr].pos = pos * heap[heap_ptr].depth = depth */ (__pyx_v_heap[__pyx_v_heap_ptr]).end = __pyx_v_end; /* "sklearn/_tree.pyx":268 * heap[heap_ptr].start = start * heap[heap_ptr].end = end * heap[heap_ptr].pos = pos # <<<<<<<<<<<<<< * heap[heap_ptr].depth = depth * heap[heap_ptr].is_leaf = is_leaf */ (__pyx_v_heap[__pyx_v_heap_ptr]).pos = __pyx_v_pos; /* "sklearn/_tree.pyx":269 * heap[heap_ptr].end = end * heap[heap_ptr].pos = pos * heap[heap_ptr].depth = depth # <<<<<<<<<<<<<< * heap[heap_ptr].is_leaf = is_leaf * heap[heap_ptr].impurity = impurity */ (__pyx_v_heap[__pyx_v_heap_ptr]).depth = __pyx_v_depth; /* "sklearn/_tree.pyx":270 * heap[heap_ptr].pos = pos * heap[heap_ptr].depth = depth * heap[heap_ptr].is_leaf = is_leaf # <<<<<<<<<<<<<< * heap[heap_ptr].impurity = impurity * heap[heap_ptr].impurity_left = impurity_left */ (__pyx_v_heap[__pyx_v_heap_ptr]).is_leaf = __pyx_v_is_leaf; /* "sklearn/_tree.pyx":271 * heap[heap_ptr].depth = depth * heap[heap_ptr].is_leaf = is_leaf * heap[heap_ptr].impurity = impurity # <<<<<<<<<<<<<< * heap[heap_ptr].impurity_left = impurity_left * heap[heap_ptr].impurity_right = impurity_right */ (__pyx_v_heap[__pyx_v_heap_ptr]).impurity = __pyx_v_impurity; /* "sklearn/_tree.pyx":272 * heap[heap_ptr].is_leaf = is_leaf * heap[heap_ptr].impurity = impurity * heap[heap_ptr].impurity_left = impurity_left # <<<<<<<<<<<<<< * heap[heap_ptr].impurity_right = impurity_right * heap[heap_ptr].improvement = improvement */ (__pyx_v_heap[__pyx_v_heap_ptr]).impurity_left = __pyx_v_impurity_left; /* "sklearn/_tree.pyx":273 * heap[heap_ptr].impurity = impurity * heap[heap_ptr].impurity_left = impurity_left * heap[heap_ptr].impurity_right = impurity_right # <<<<<<<<<<<<<< * heap[heap_ptr].improvement = improvement * */ (__pyx_v_heap[__pyx_v_heap_ptr]).impurity_right = __pyx_v_impurity_right; /* "sklearn/_tree.pyx":274 * heap[heap_ptr].impurity_left = impurity_left * heap[heap_ptr].impurity_right = impurity_right * heap[heap_ptr].improvement = improvement # <<<<<<<<<<<<<< * * # Heapify up */ (__pyx_v_heap[__pyx_v_heap_ptr]).improvement = __pyx_v_improvement; /* "sklearn/_tree.pyx":277 * * # Heapify up * heapify_up(heap, heap_ptr) # <<<<<<<<<<<<<< * * # Increase element count */ __pyx_f_7sklearn_5_tree_heapify_up(__pyx_v_heap, __pyx_v_heap_ptr); /* "sklearn/_tree.pyx":280 * * # Increase element count * self.heap_ptr = heap_ptr + 1 # <<<<<<<<<<<<<< * return 0 * */ __pyx_v_self->heap_ptr = (__pyx_v_heap_ptr + 1); /* "sklearn/_tree.pyx":281 * # Increase element count * self.heap_ptr = heap_ptr + 1 * return 0 # <<<<<<<<<<<<<< * * cdef int pop(self, PriorityHeapRecord* res) nogil: */ __pyx_r = 0; goto __pyx_L0; /* "sklearn/_tree.pyx":241 * return self.heap_ptr <= 0 * * cdef int push(self, SIZE_t node_id, SIZE_t start, SIZE_t end, SIZE_t pos, # <<<<<<<<<<<<<< * SIZE_t depth, bint is_leaf, double improvement, * double impurity, double impurity_left, */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "sklearn/_tree.pyx":283 * return 0 * * cdef int pop(self, PriorityHeapRecord* res) nogil: # <<<<<<<<<<<<<< * """Remove max element from the heap. """ * cdef SIZE_t heap_ptr = self.heap_ptr */ static int __pyx_f_7sklearn_5_tree_12PriorityHeap_pop(struct __pyx_obj_7sklearn_5_tree_PriorityHeap *__pyx_v_self, struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord *__pyx_v_res) { __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_heap_ptr; struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord *__pyx_v_heap; int __pyx_r; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_1; struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord *__pyx_t_2; int __pyx_t_3; struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord __pyx_t_4; struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord __pyx_t_5; /* "sklearn/_tree.pyx":285 * cdef int pop(self, PriorityHeapRecord* res) nogil: * """Remove max element from the heap. """ * cdef SIZE_t heap_ptr = self.heap_ptr # <<<<<<<<<<<<<< * cdef PriorityHeapRecord* heap = self.heap_ * */ __pyx_t_1 = __pyx_v_self->heap_ptr; __pyx_v_heap_ptr = __pyx_t_1; /* "sklearn/_tree.pyx":286 * """Remove max element from the heap. """ * cdef SIZE_t heap_ptr = self.heap_ptr * cdef PriorityHeapRecord* heap = self.heap_ # <<<<<<<<<<<<<< * * if heap_ptr <= 0: */ __pyx_t_2 = __pyx_v_self->heap_; __pyx_v_heap = __pyx_t_2; /* "sklearn/_tree.pyx":288 * cdef PriorityHeapRecord* heap = self.heap_ * * if heap_ptr <= 0: # <<<<<<<<<<<<<< * return -1 * */ __pyx_t_3 = ((__pyx_v_heap_ptr <= 0) != 0); if (__pyx_t_3) { /* "sklearn/_tree.pyx":289 * * if heap_ptr <= 0: * return -1 # <<<<<<<<<<<<<< * * # Take first element */ __pyx_r = -1; goto __pyx_L0; } /* "sklearn/_tree.pyx":292 * * # Take first element * res[0] = heap[0] # <<<<<<<<<<<<<< * * # Put last element to the front */ (__pyx_v_res[0]) = (__pyx_v_heap[0]); /* "sklearn/_tree.pyx":295 * * # Put last element to the front * heap[0], heap[heap_ptr - 1] = heap[heap_ptr - 1], heap[0] # <<<<<<<<<<<<<< * * # Restore heap invariant */ __pyx_t_4 = (__pyx_v_heap[(__pyx_v_heap_ptr - 1)]); __pyx_t_5 = (__pyx_v_heap[0]); (__pyx_v_heap[0]) = __pyx_t_4; (__pyx_v_heap[(__pyx_v_heap_ptr - 1)]) = __pyx_t_5; /* "sklearn/_tree.pyx":298 * * # Restore heap invariant * if heap_ptr > 1: # <<<<<<<<<<<<<< * heapify_down(heap, 0, heap_ptr - 1) * */ __pyx_t_3 = ((__pyx_v_heap_ptr > 1) != 0); if (__pyx_t_3) { /* "sklearn/_tree.pyx":299 * # Restore heap invariant * if heap_ptr > 1: * heapify_down(heap, 0, heap_ptr - 1) # <<<<<<<<<<<<<< * * self.heap_ptr = heap_ptr - 1 */ __pyx_f_7sklearn_5_tree_heapify_down(__pyx_v_heap, 0, (__pyx_v_heap_ptr - 1)); goto __pyx_L4; } __pyx_L4:; /* "sklearn/_tree.pyx":301 * heapify_down(heap, 0, heap_ptr - 1) * * self.heap_ptr = heap_ptr - 1 # <<<<<<<<<<<<<< * * return 0 */ __pyx_v_self->heap_ptr = (__pyx_v_heap_ptr - 1); /* "sklearn/_tree.pyx":303 * self.heap_ptr = heap_ptr - 1 * * return 0 # <<<<<<<<<<<<<< * * */ __pyx_r = 0; goto __pyx_L0; /* "sklearn/_tree.pyx":283 * return 0 * * cdef int pop(self, PriorityHeapRecord* res) nogil: # <<<<<<<<<<<<<< * """Remove max element from the heap. """ * cdef SIZE_t heap_ptr = self.heap_ptr */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "sklearn/_tree.pyx":313 * """Interface for impurity criteria.""" * * cdef void init(self, DOUBLE_t* y, SIZE_t y_stride, DOUBLE_t* sample_weight, # <<<<<<<<<<<<<< * double weighted_n_samples, SIZE_t* samples, SIZE_t start, * SIZE_t end) nogil: */ static void __pyx_f_7sklearn_5_tree_9Criterion_init(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_Criterion *__pyx_v_self, CYTHON_UNUSED __pyx_t_7sklearn_5_tree_DOUBLE_t *__pyx_v_y, CYTHON_UNUSED __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_y_stride, CYTHON_UNUSED __pyx_t_7sklearn_5_tree_DOUBLE_t *__pyx_v_sample_weight, CYTHON_UNUSED double __pyx_v_weighted_n_samples, CYTHON_UNUSED __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_samples, CYTHON_UNUSED __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_start, CYTHON_UNUSED __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_end) { /* function exit code */ } /* "sklearn/_tree.pyx":320 * pass * * cdef void reset(self) nogil: # <<<<<<<<<<<<<< * """Reset the criterion at pos=start.""" * pass */ static void __pyx_f_7sklearn_5_tree_9Criterion_reset(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_Criterion *__pyx_v_self) { /* function exit code */ } /* "sklearn/_tree.pyx":324 * pass * * cdef void update(self, SIZE_t new_pos) nogil: # <<<<<<<<<<<<<< * """Update the collected statistics by moving samples[pos:new_pos] from * the right child to the left child.""" */ static void __pyx_f_7sklearn_5_tree_9Criterion_update(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_Criterion *__pyx_v_self, CYTHON_UNUSED __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_new_pos) { /* function exit code */ } /* "sklearn/_tree.pyx":329 * pass * * cdef double node_impurity(self) nogil: # <<<<<<<<<<<<<< * """Evaluate the impurity of the current node, i.e. the impurity of * samples[start:end].""" */ static double __pyx_f_7sklearn_5_tree_9Criterion_node_impurity(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_Criterion *__pyx_v_self) { double __pyx_r; /* function exit code */ __pyx_r = 0; return __pyx_r; } /* "sklearn/_tree.pyx":334 * pass * * cdef void children_impurity(self, double* impurity_left, # <<<<<<<<<<<<<< * double* impurity_right) nogil: * """Evaluate the impurity in children nodes, i.e. the impurity of */ static void __pyx_f_7sklearn_5_tree_9Criterion_children_impurity(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_Criterion *__pyx_v_self, CYTHON_UNUSED double *__pyx_v_impurity_left, CYTHON_UNUSED double *__pyx_v_impurity_right) { /* function exit code */ } /* "sklearn/_tree.pyx":340 * pass * * cdef void node_value(self, double* dest) nogil: # <<<<<<<<<<<<<< * """Compute the node value of samples[start:end] into dest.""" * pass */ static void __pyx_f_7sklearn_5_tree_9Criterion_node_value(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_Criterion *__pyx_v_self, CYTHON_UNUSED double *__pyx_v_dest) { /* function exit code */ } /* "sklearn/_tree.pyx":344 * pass * * cdef double impurity_improvement(self, double impurity) nogil: # <<<<<<<<<<<<<< * """Weighted impurity improvement, i.e. * */ static double __pyx_f_7sklearn_5_tree_9Criterion_impurity_improvement(struct __pyx_obj_7sklearn_5_tree_Criterion *__pyx_v_self, double __pyx_v_impurity) { double __pyx_v_impurity_left; double __pyx_v_impurity_right; double __pyx_r; /* "sklearn/_tree.pyx":356 * cdef double impurity_right * * self.children_impurity(&impurity_left, &impurity_right) # <<<<<<<<<<<<<< * * return ((self.weighted_n_node_samples / self.weighted_n_samples) * */ ((struct __pyx_vtabstruct_7sklearn_5_tree_Criterion *)__pyx_v_self->__pyx_vtab)->children_impurity(__pyx_v_self, (&__pyx_v_impurity_left), (&__pyx_v_impurity_right)); /* "sklearn/_tree.pyx":358 * self.children_impurity(&impurity_left, &impurity_right) * * return ((self.weighted_n_node_samples / self.weighted_n_samples) * # <<<<<<<<<<<<<< * (impurity - self.weighted_n_right / self.weighted_n_node_samples * impurity_right * - self.weighted_n_left / self.weighted_n_node_samples * impurity_left)) */ __pyx_r = ((__pyx_v_self->weighted_n_node_samples / __pyx_v_self->weighted_n_samples) * ((__pyx_v_impurity - ((__pyx_v_self->weighted_n_right / __pyx_v_self->weighted_n_node_samples) * __pyx_v_impurity_right)) - ((__pyx_v_self->weighted_n_left / __pyx_v_self->weighted_n_node_samples) * __pyx_v_impurity_left))); 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/* "sklearn/_tree.pyx":471 * memset(label_count_total + offset, 0, * n_classes[k] * sizeof(double)) * offset += label_count_stride # <<<<<<<<<<<<<< * * for p in range(start, end): */ __pyx_v_offset = (__pyx_v_offset + __pyx_v_label_count_stride); } /* "sklearn/_tree.pyx":473 * offset += label_count_stride * * for p in range(start, end): # <<<<<<<<<<<<<< * i = samples[p] * */ __pyx_t_1 = __pyx_v_end; for (__pyx_t_4 = __pyx_v_start; __pyx_t_4 < __pyx_t_1; __pyx_t_4+=1) { __pyx_v_p = __pyx_t_4; /* "sklearn/_tree.pyx":474 * * for p in range(start, end): * i = samples[p] # <<<<<<<<<<<<<< * * if sample_weight != NULL: */ __pyx_v_i = (__pyx_v_samples[__pyx_v_p]); /* "sklearn/_tree.pyx":476 * i = samples[p] * * if sample_weight != NULL: # <<<<<<<<<<<<<< * w = sample_weight[i] * */ __pyx_t_5 = ((__pyx_v_sample_weight != NULL) != 0); if (__pyx_t_5) { /* "sklearn/_tree.pyx":477 * * if sample_weight != NULL: * w = sample_weight[i] # <<<<<<<<<<<<<< * * for k in range(n_outputs): */ __pyx_v_w = (__pyx_v_sample_weight[__pyx_v_i]); goto __pyx_L7; } __pyx_L7:; /* "sklearn/_tree.pyx":479 * w = sample_weight[i] * * for k in range(n_outputs): # <<<<<<<<<<<<<< * c = y[i * y_stride + k] * label_count_total[k * label_count_stride + c] += w */ __pyx_t_6 = __pyx_v_n_outputs; for (__pyx_t_7 = 0; __pyx_t_7 < __pyx_t_6; __pyx_t_7+=1) { __pyx_v_k = __pyx_t_7; /* "sklearn/_tree.pyx":480 * * for k in range(n_outputs): * c = y[i * y_stride + k] # <<<<<<<<<<<<<< * label_count_total[k * label_count_stride + c] += w * */ __pyx_v_c = ((__pyx_t_7sklearn_5_tree_SIZE_t)(__pyx_v_y[((__pyx_v_i * __pyx_v_y_stride) + __pyx_v_k)])); /* "sklearn/_tree.pyx":481 * for k in range(n_outputs): * c = y[i * y_stride + k] * label_count_total[k * label_count_stride + c] += w # <<<<<<<<<<<<<< * * weighted_n_node_samples += w */ __pyx_t_8 = ((__pyx_v_k * __pyx_v_label_count_stride) + __pyx_v_c); (__pyx_v_label_count_total[__pyx_t_8]) = ((__pyx_v_label_count_total[__pyx_t_8]) + __pyx_v_w); } /* "sklearn/_tree.pyx":483 * label_count_total[k * label_count_stride + c] += w * * weighted_n_node_samples += w # <<<<<<<<<<<<<< * * self.weighted_n_node_samples = weighted_n_node_samples */ __pyx_v_weighted_n_node_samples = (__pyx_v_weighted_n_node_samples + __pyx_v_w); } /* "sklearn/_tree.pyx":485 * weighted_n_node_samples += w * * self.weighted_n_node_samples = weighted_n_node_samples # <<<<<<<<<<<<<< * * # Reset to pos=start */ __pyx_v_self->__pyx_base.weighted_n_node_samples = __pyx_v_weighted_n_node_samples; /* "sklearn/_tree.pyx":488 * * # Reset to pos=start * self.reset() # <<<<<<<<<<<<<< * * cdef void reset(self) nogil: */ ((struct __pyx_vtabstruct_7sklearn_5_tree_ClassificationCriterion *)__pyx_v_self->__pyx_base.__pyx_vtab)->__pyx_base.reset(((struct __pyx_obj_7sklearn_5_tree_Criterion *)__pyx_v_self)); /* "sklearn/_tree.pyx":439 * pass * * cdef void init(self, DOUBLE_t* y, SIZE_t y_stride, # <<<<<<<<<<<<<< * DOUBLE_t* sample_weight, double weighted_n_samples, * SIZE_t* samples, SIZE_t start, SIZE_t end) nogil: */ /* function exit code */ } /* "sklearn/_tree.pyx":490 * self.reset() * * cdef void reset(self) nogil: # <<<<<<<<<<<<<< * """Reset the criterion at pos=start.""" * self.pos = self.start */ static void __pyx_f_7sklearn_5_tree_23ClassificationCriterion_reset(struct __pyx_obj_7sklearn_5_tree_ClassificationCriterion *__pyx_v_self) { __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_outputs; __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_n_classes; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_label_count_stride; double *__pyx_v_label_count_total; double *__pyx_v_label_count_left; double *__pyx_v_label_count_right; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_k; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_1; double __pyx_t_2; __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_t_3; double *__pyx_t_4; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_5; /* "sklearn/_tree.pyx":492 * cdef void reset(self) nogil: * """Reset the criterion at pos=start.""" * self.pos = self.start # <<<<<<<<<<<<<< * * self.weighted_n_left = 0.0 */ __pyx_t_1 = __pyx_v_self->__pyx_base.start; __pyx_v_self->__pyx_base.pos = __pyx_t_1; /* "sklearn/_tree.pyx":494 * self.pos = self.start * * self.weighted_n_left = 0.0 # <<<<<<<<<<<<<< * self.weighted_n_right = self.weighted_n_node_samples * */ __pyx_v_self->__pyx_base.weighted_n_left = 0.0; /* "sklearn/_tree.pyx":495 * * self.weighted_n_left = 0.0 * self.weighted_n_right = self.weighted_n_node_samples # <<<<<<<<<<<<<< * * cdef SIZE_t n_outputs = self.n_outputs */ __pyx_t_2 = __pyx_v_self->__pyx_base.weighted_n_node_samples; __pyx_v_self->__pyx_base.weighted_n_right = __pyx_t_2; /* "sklearn/_tree.pyx":497 * self.weighted_n_right = self.weighted_n_node_samples * * cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<< * cdef SIZE_t* n_classes = self.n_classes * cdef SIZE_t label_count_stride = self.label_count_stride */ __pyx_t_1 = __pyx_v_self->__pyx_base.n_outputs; __pyx_v_n_outputs = __pyx_t_1; /* "sklearn/_tree.pyx":498 * * cdef SIZE_t n_outputs = self.n_outputs * cdef SIZE_t* n_classes = self.n_classes # <<<<<<<<<<<<<< * cdef SIZE_t label_count_stride = self.label_count_stride * cdef double* label_count_total = self.label_count_total */ __pyx_t_3 = __pyx_v_self->n_classes; __pyx_v_n_classes = __pyx_t_3; /* "sklearn/_tree.pyx":499 * cdef SIZE_t n_outputs = self.n_outputs * cdef SIZE_t* n_classes = self.n_classes * cdef SIZE_t label_count_stride = self.label_count_stride # <<<<<<<<<<<<<< * cdef double* label_count_total = self.label_count_total * cdef double* label_count_left = self.label_count_left */ __pyx_t_1 = __pyx_v_self->label_count_stride; __pyx_v_label_count_stride = __pyx_t_1; /* "sklearn/_tree.pyx":500 * cdef SIZE_t* n_classes = self.n_classes * cdef SIZE_t label_count_stride = self.label_count_stride * cdef double* label_count_total = self.label_count_total # <<<<<<<<<<<<<< * cdef double* label_count_left = self.label_count_left * cdef double* label_count_right = self.label_count_right */ __pyx_t_4 = __pyx_v_self->label_count_total; __pyx_v_label_count_total = __pyx_t_4; /* "sklearn/_tree.pyx":501 * cdef SIZE_t label_count_stride = self.label_count_stride * cdef double* label_count_total = self.label_count_total * cdef double* label_count_left = self.label_count_left # <<<<<<<<<<<<<< * cdef double* label_count_right = self.label_count_right * */ __pyx_t_4 = __pyx_v_self->label_count_left; __pyx_v_label_count_left = __pyx_t_4; /* "sklearn/_tree.pyx":502 * cdef double* label_count_total = self.label_count_total * cdef double* label_count_left = self.label_count_left * cdef double* label_count_right = self.label_count_right # <<<<<<<<<<<<<< * * cdef SIZE_t k = 0 */ __pyx_t_4 = __pyx_v_self->label_count_right; __pyx_v_label_count_right = __pyx_t_4; /* "sklearn/_tree.pyx":504 * cdef double* label_count_right = self.label_count_right * * cdef SIZE_t k = 0 # <<<<<<<<<<<<<< * * for k in range(n_outputs): */ __pyx_v_k = 0; /* "sklearn/_tree.pyx":506 * cdef SIZE_t k = 0 * * for k in range(n_outputs): # <<<<<<<<<<<<<< * memset(label_count_left, 0, n_classes[k] * sizeof(double)) * memcpy(label_count_right, label_count_total, */ __pyx_t_1 = __pyx_v_n_outputs; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_1; __pyx_t_5+=1) { __pyx_v_k = __pyx_t_5; /* "sklearn/_tree.pyx":507 * * for k in range(n_outputs): * memset(label_count_left, 0, n_classes[k] * sizeof(double)) # <<<<<<<<<<<<<< * memcpy(label_count_right, label_count_total, * n_classes[k] * sizeof(double)) */ memset(__pyx_v_label_count_left, 0, ((__pyx_v_n_classes[__pyx_v_k]) * (sizeof(double)))); /* "sklearn/_tree.pyx":508 * for k in range(n_outputs): * memset(label_count_left, 0, n_classes[k] * sizeof(double)) * memcpy(label_count_right, label_count_total, # <<<<<<<<<<<<<< * n_classes[k] * sizeof(double)) * */ memcpy(__pyx_v_label_count_right, __pyx_v_label_count_total, ((__pyx_v_n_classes[__pyx_v_k]) * (sizeof(double)))); /* "sklearn/_tree.pyx":511 * n_classes[k] * sizeof(double)) * * label_count_total += label_count_stride # <<<<<<<<<<<<<< * label_count_left += label_count_stride * label_count_right += label_count_stride */ __pyx_v_label_count_total = (__pyx_v_label_count_total + __pyx_v_label_count_stride); /* "sklearn/_tree.pyx":512 * * label_count_total += label_count_stride * label_count_left += label_count_stride # <<<<<<<<<<<<<< * label_count_right += label_count_stride * */ __pyx_v_label_count_left = (__pyx_v_label_count_left + __pyx_v_label_count_stride); /* "sklearn/_tree.pyx":513 * label_count_total += label_count_stride * label_count_left += label_count_stride * label_count_right += label_count_stride # <<<<<<<<<<<<<< * * cdef void update(self, SIZE_t new_pos) nogil: */ __pyx_v_label_count_right = (__pyx_v_label_count_right + __pyx_v_label_count_stride); } /* "sklearn/_tree.pyx":490 * self.reset() * * cdef void reset(self) nogil: # <<<<<<<<<<<<<< * """Reset the criterion at pos=start.""" * self.pos = self.start */ /* function exit code */ } /* "sklearn/_tree.pyx":515 * label_count_right += label_count_stride * * cdef void update(self, SIZE_t new_pos) nogil: # <<<<<<<<<<<<<< * """Update the collected statistics by moving samples[pos:new_pos] from * the right child to the left child.""" */ static void __pyx_f_7sklearn_5_tree_23ClassificationCriterion_update(struct __pyx_obj_7sklearn_5_tree_ClassificationCriterion *__pyx_v_self, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_new_pos) { __pyx_t_7sklearn_5_tree_DOUBLE_t *__pyx_v_y; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_y_stride; __pyx_t_7sklearn_5_tree_DOUBLE_t *__pyx_v_sample_weight; __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_samples; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_pos; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_outputs; CYTHON_UNUSED __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_n_classes; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_label_count_stride; CYTHON_UNUSED double *__pyx_v_label_count_total; double *__pyx_v_label_count_left; double *__pyx_v_label_count_right; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_i; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_p; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_k; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_label_index; __pyx_t_7sklearn_5_tree_DOUBLE_t __pyx_v_w; __pyx_t_7sklearn_5_tree_DOUBLE_t __pyx_v_diff_w; __pyx_t_7sklearn_5_tree_DOUBLE_t *__pyx_t_1; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_2; __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_t_3; double *__pyx_t_4; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_5; int __pyx_t_6; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_7; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_8; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_9; /* "sklearn/_tree.pyx":518 * """Update the collected statistics by moving samples[pos:new_pos] from * the right child to the left child.""" * cdef DOUBLE_t* y = self.y # <<<<<<<<<<<<<< * cdef SIZE_t y_stride = self.y_stride * cdef DOUBLE_t* sample_weight = self.sample_weight */ __pyx_t_1 = __pyx_v_self->__pyx_base.y; __pyx_v_y = __pyx_t_1; /* "sklearn/_tree.pyx":519 * the right child to the left child.""" * cdef DOUBLE_t* y = self.y * cdef SIZE_t y_stride = self.y_stride # <<<<<<<<<<<<<< * cdef DOUBLE_t* sample_weight = self.sample_weight * */ __pyx_t_2 = __pyx_v_self->__pyx_base.y_stride; __pyx_v_y_stride = __pyx_t_2; /* "sklearn/_tree.pyx":520 * cdef DOUBLE_t* y = self.y * cdef SIZE_t y_stride = self.y_stride * cdef DOUBLE_t* sample_weight = self.sample_weight # <<<<<<<<<<<<<< * * cdef SIZE_t* samples = self.samples */ __pyx_t_1 = __pyx_v_self->__pyx_base.sample_weight; __pyx_v_sample_weight = __pyx_t_1; /* "sklearn/_tree.pyx":522 * cdef DOUBLE_t* sample_weight = self.sample_weight * * cdef SIZE_t* samples = self.samples # <<<<<<<<<<<<<< * cdef SIZE_t pos = self.pos * */ __pyx_t_3 = __pyx_v_self->__pyx_base.samples; __pyx_v_samples = __pyx_t_3; /* "sklearn/_tree.pyx":523 * * cdef SIZE_t* samples = self.samples * cdef SIZE_t pos = self.pos # <<<<<<<<<<<<<< * * cdef SIZE_t n_outputs = self.n_outputs */ __pyx_t_2 = __pyx_v_self->__pyx_base.pos; __pyx_v_pos = __pyx_t_2; /* "sklearn/_tree.pyx":525 * cdef SIZE_t pos = self.pos * * cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<< * cdef SIZE_t* n_classes = self.n_classes * cdef SIZE_t label_count_stride = self.label_count_stride */ __pyx_t_2 = __pyx_v_self->__pyx_base.n_outputs; __pyx_v_n_outputs = __pyx_t_2; /* "sklearn/_tree.pyx":526 * * cdef SIZE_t n_outputs = self.n_outputs * cdef SIZE_t* n_classes = self.n_classes # <<<<<<<<<<<<<< * cdef SIZE_t label_count_stride = self.label_count_stride * cdef double* label_count_total = self.label_count_total */ __pyx_t_3 = __pyx_v_self->n_classes; __pyx_v_n_classes = __pyx_t_3; /* "sklearn/_tree.pyx":527 * cdef SIZE_t n_outputs = self.n_outputs * cdef SIZE_t* n_classes = self.n_classes * cdef SIZE_t label_count_stride = self.label_count_stride # <<<<<<<<<<<<<< * cdef double* label_count_total = self.label_count_total * cdef double* label_count_left = self.label_count_left */ __pyx_t_2 = __pyx_v_self->label_count_stride; __pyx_v_label_count_stride = __pyx_t_2; /* "sklearn/_tree.pyx":528 * cdef SIZE_t* n_classes = self.n_classes * cdef SIZE_t label_count_stride = self.label_count_stride * cdef double* label_count_total = self.label_count_total # <<<<<<<<<<<<<< * cdef double* label_count_left = self.label_count_left * cdef double* label_count_right = self.label_count_right */ __pyx_t_4 = __pyx_v_self->label_count_total; __pyx_v_label_count_total = __pyx_t_4; /* "sklearn/_tree.pyx":529 * cdef SIZE_t label_count_stride = self.label_count_stride * cdef double* label_count_total = self.label_count_total * cdef double* label_count_left = self.label_count_left # <<<<<<<<<<<<<< * cdef double* label_count_right = self.label_count_right * */ __pyx_t_4 = __pyx_v_self->label_count_left; __pyx_v_label_count_left = __pyx_t_4; /* "sklearn/_tree.pyx":530 * cdef double* label_count_total = self.label_count_total * cdef double* label_count_left = self.label_count_left * cdef double* label_count_right = self.label_count_right # <<<<<<<<<<<<<< * * cdef SIZE_t i */ __pyx_t_4 = __pyx_v_self->label_count_right; __pyx_v_label_count_right = __pyx_t_4; /* "sklearn/_tree.pyx":536 * cdef SIZE_t k * cdef SIZE_t label_index * cdef DOUBLE_t w = 1.0 # <<<<<<<<<<<<<< * cdef DOUBLE_t diff_w = 0.0 * */ __pyx_v_w = 1.0; /* "sklearn/_tree.pyx":537 * cdef SIZE_t label_index * cdef DOUBLE_t w = 1.0 * cdef DOUBLE_t diff_w = 0.0 # <<<<<<<<<<<<<< * * # Note: We assume start <= pos < new_pos <= end */ __pyx_v_diff_w = 0.0; /* "sklearn/_tree.pyx":541 * # Note: We assume start <= pos < new_pos <= end * * for p in range(pos, new_pos): # <<<<<<<<<<<<<< * i = samples[p] * */ __pyx_t_2 = __pyx_v_new_pos; for (__pyx_t_5 = __pyx_v_pos; __pyx_t_5 < __pyx_t_2; __pyx_t_5+=1) { __pyx_v_p = __pyx_t_5; /* "sklearn/_tree.pyx":542 * * for p in range(pos, new_pos): * i = samples[p] # <<<<<<<<<<<<<< * * if sample_weight != NULL: */ __pyx_v_i = (__pyx_v_samples[__pyx_v_p]); /* "sklearn/_tree.pyx":544 * i = samples[p] * * if sample_weight != NULL: # <<<<<<<<<<<<<< * w = sample_weight[i] * */ __pyx_t_6 = ((__pyx_v_sample_weight != NULL) != 0); if (__pyx_t_6) { /* "sklearn/_tree.pyx":545 * * if sample_weight != NULL: * w = sample_weight[i] # <<<<<<<<<<<<<< * * for k in range(n_outputs): */ __pyx_v_w = (__pyx_v_sample_weight[__pyx_v_i]); goto __pyx_L5; } __pyx_L5:; /* "sklearn/_tree.pyx":547 * w = sample_weight[i] * * for k in range(n_outputs): # <<<<<<<<<<<<<< * label_index = (k * label_count_stride + * y[i * y_stride + k]) */ __pyx_t_7 = __pyx_v_n_outputs; for (__pyx_t_8 = 0; __pyx_t_8 < __pyx_t_7; __pyx_t_8+=1) { __pyx_v_k = __pyx_t_8; /* "sklearn/_tree.pyx":548 * * for k in range(n_outputs): * label_index = (k * label_count_stride + # <<<<<<<<<<<<<< * y[i * y_stride + k]) * label_count_left[label_index] += w */ __pyx_v_label_index = ((__pyx_v_k * __pyx_v_label_count_stride) + ((__pyx_t_7sklearn_5_tree_SIZE_t)(__pyx_v_y[((__pyx_v_i * __pyx_v_y_stride) + __pyx_v_k)]))); /* "sklearn/_tree.pyx":550 * label_index = (k * label_count_stride + * y[i * y_stride + k]) * label_count_left[label_index] += w # <<<<<<<<<<<<<< * label_count_right[label_index] -= w * */ __pyx_t_9 = __pyx_v_label_index; (__pyx_v_label_count_left[__pyx_t_9]) = ((__pyx_v_label_count_left[__pyx_t_9]) + __pyx_v_w); /* "sklearn/_tree.pyx":551 * y[i * y_stride + k]) * label_count_left[label_index] += w * label_count_right[label_index] -= w # <<<<<<<<<<<<<< * * diff_w += w */ __pyx_t_9 = __pyx_v_label_index; (__pyx_v_label_count_right[__pyx_t_9]) = ((__pyx_v_label_count_right[__pyx_t_9]) - __pyx_v_w); } /* "sklearn/_tree.pyx":553 * label_count_right[label_index] -= w * * diff_w += w # <<<<<<<<<<<<<< * * self.weighted_n_left += diff_w */ __pyx_v_diff_w = (__pyx_v_diff_w + __pyx_v_w); } /* "sklearn/_tree.pyx":555 * diff_w += w * * self.weighted_n_left += diff_w # <<<<<<<<<<<<<< * self.weighted_n_right -= diff_w * */ __pyx_v_self->__pyx_base.weighted_n_left = (__pyx_v_self->__pyx_base.weighted_n_left + __pyx_v_diff_w); /* "sklearn/_tree.pyx":556 * * self.weighted_n_left += diff_w * self.weighted_n_right -= diff_w # <<<<<<<<<<<<<< * * self.pos = new_pos */ __pyx_v_self->__pyx_base.weighted_n_right = (__pyx_v_self->__pyx_base.weighted_n_right - __pyx_v_diff_w); /* "sklearn/_tree.pyx":558 * self.weighted_n_right -= diff_w * * self.pos = new_pos # <<<<<<<<<<<<<< * * cdef double node_impurity(self) nogil: */ __pyx_v_self->__pyx_base.pos = __pyx_v_new_pos; /* "sklearn/_tree.pyx":515 * label_count_right += label_count_stride * * cdef void update(self, SIZE_t new_pos) nogil: # <<<<<<<<<<<<<< * """Update the collected statistics by moving samples[pos:new_pos] from * the right child to the left child.""" */ /* function exit code */ } /* "sklearn/_tree.pyx":560 * self.pos = new_pos * * cdef double node_impurity(self) nogil: # <<<<<<<<<<<<<< * pass * */ static double __pyx_f_7sklearn_5_tree_23ClassificationCriterion_node_impurity(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_ClassificationCriterion *__pyx_v_self) { double __pyx_r; /* function exit code */ __pyx_r = 0; return __pyx_r; } /* "sklearn/_tree.pyx":563 * pass * * cdef void children_impurity(self, double* impurity_left, # <<<<<<<<<<<<<< * double* impurity_right) nogil: * pass */ static void __pyx_f_7sklearn_5_tree_23ClassificationCriterion_children_impurity(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_ClassificationCriterion *__pyx_v_self, CYTHON_UNUSED double *__pyx_v_impurity_left, CYTHON_UNUSED double *__pyx_v_impurity_right) { /* function exit code */ } /* "sklearn/_tree.pyx":567 * pass * * cdef void node_value(self, double* dest) nogil: # <<<<<<<<<<<<<< * """Compute the node value of samples[start:end] into dest.""" * cdef SIZE_t n_outputs = self.n_outputs */ static void __pyx_f_7sklearn_5_tree_23ClassificationCriterion_node_value(struct __pyx_obj_7sklearn_5_tree_ClassificationCriterion *__pyx_v_self, double *__pyx_v_dest) { __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_outputs; __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_n_classes; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_label_count_stride; double *__pyx_v_label_count_total; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_k; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_1; __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_t_2; double *__pyx_t_3; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_4; /* "sklearn/_tree.pyx":569 * cdef void node_value(self, double* dest) nogil: * """Compute the node value of samples[start:end] into dest.""" * cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<< * cdef SIZE_t* n_classes = self.n_classes * cdef SIZE_t label_count_stride = self.label_count_stride */ __pyx_t_1 = __pyx_v_self->__pyx_base.n_outputs; __pyx_v_n_outputs = __pyx_t_1; /* "sklearn/_tree.pyx":570 * """Compute the node value of samples[start:end] into dest.""" * cdef SIZE_t n_outputs = self.n_outputs * cdef SIZE_t* n_classes = self.n_classes # <<<<<<<<<<<<<< * cdef SIZE_t label_count_stride = self.label_count_stride * cdef double* label_count_total = self.label_count_total */ __pyx_t_2 = __pyx_v_self->n_classes; __pyx_v_n_classes = __pyx_t_2; /* "sklearn/_tree.pyx":571 * cdef SIZE_t n_outputs = self.n_outputs * cdef SIZE_t* n_classes = self.n_classes * cdef SIZE_t label_count_stride = self.label_count_stride # <<<<<<<<<<<<<< * cdef double* label_count_total = self.label_count_total * cdef SIZE_t k */ __pyx_t_1 = __pyx_v_self->label_count_stride; __pyx_v_label_count_stride = __pyx_t_1; /* "sklearn/_tree.pyx":572 * cdef SIZE_t* n_classes = self.n_classes * cdef SIZE_t label_count_stride = self.label_count_stride * cdef double* label_count_total = self.label_count_total # <<<<<<<<<<<<<< * cdef SIZE_t k * */ __pyx_t_3 = __pyx_v_self->label_count_total; __pyx_v_label_count_total = __pyx_t_3; /* "sklearn/_tree.pyx":575 * cdef SIZE_t k * * for k in range(n_outputs): # <<<<<<<<<<<<<< * memcpy(dest, label_count_total, n_classes[k] * sizeof(double)) * dest += label_count_stride */ __pyx_t_1 = __pyx_v_n_outputs; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_1; __pyx_t_4+=1) { __pyx_v_k = __pyx_t_4; /* "sklearn/_tree.pyx":576 * * for k in range(n_outputs): * memcpy(dest, label_count_total, n_classes[k] * sizeof(double)) # <<<<<<<<<<<<<< * dest += label_count_stride * label_count_total += label_count_stride */ memcpy(__pyx_v_dest, __pyx_v_label_count_total, ((__pyx_v_n_classes[__pyx_v_k]) * (sizeof(double)))); /* "sklearn/_tree.pyx":577 * for k in range(n_outputs): * memcpy(dest, label_count_total, n_classes[k] * sizeof(double)) * dest += label_count_stride # <<<<<<<<<<<<<< * label_count_total += label_count_stride * */ __pyx_v_dest = (__pyx_v_dest + __pyx_v_label_count_stride); /* "sklearn/_tree.pyx":578 * memcpy(dest, label_count_total, n_classes[k] * sizeof(double)) * dest += label_count_stride * label_count_total += label_count_stride # <<<<<<<<<<<<<< * * */ __pyx_v_label_count_total = (__pyx_v_label_count_total + __pyx_v_label_count_stride); } /* "sklearn/_tree.pyx":567 * pass * * cdef void node_value(self, double* dest) nogil: # <<<<<<<<<<<<<< * """Compute the node value of samples[start:end] into dest.""" * cdef SIZE_t n_outputs = self.n_outputs */ /* function exit code */ } /* "sklearn/_tree.pyx":595 * cross-entropy = - \sum_{k=0}^{K-1} pmk log(pmk) * """ * cdef double node_impurity(self) nogil: # <<<<<<<<<<<<<< * """Evaluate the impurity of the current node, i.e. the impurity of * samples[start:end].""" */ static double __pyx_f_7sklearn_5_tree_7Entropy_node_impurity(struct __pyx_obj_7sklearn_5_tree_Entropy *__pyx_v_self) { double __pyx_v_weighted_n_node_samples; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_outputs; __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_n_classes; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_label_count_stride; double *__pyx_v_label_count_total; double __pyx_v_entropy; double __pyx_v_total; double __pyx_v_tmp; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_k; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_c; double __pyx_r; double __pyx_t_1; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_2; __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_t_3; double *__pyx_t_4; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_5; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_6; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_7; int __pyx_t_8; /* "sklearn/_tree.pyx":598 * """Evaluate the impurity of the current node, i.e. the impurity of * samples[start:end].""" * cdef double weighted_n_node_samples = self.weighted_n_node_samples # <<<<<<<<<<<<<< * * cdef SIZE_t n_outputs = self.n_outputs */ __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.weighted_n_node_samples; __pyx_v_weighted_n_node_samples = __pyx_t_1; /* "sklearn/_tree.pyx":600 * cdef double weighted_n_node_samples = self.weighted_n_node_samples * * cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<< * cdef SIZE_t* n_classes = self.n_classes * cdef SIZE_t label_count_stride = self.label_count_stride */ __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.n_outputs; __pyx_v_n_outputs = __pyx_t_2; /* "sklearn/_tree.pyx":601 * * cdef SIZE_t n_outputs = self.n_outputs * cdef SIZE_t* n_classes = self.n_classes # <<<<<<<<<<<<<< * cdef SIZE_t label_count_stride = self.label_count_stride * cdef double* label_count_total = self.label_count_total */ __pyx_t_3 = __pyx_v_self->__pyx_base.n_classes; __pyx_v_n_classes = __pyx_t_3; /* "sklearn/_tree.pyx":602 * cdef SIZE_t n_outputs = self.n_outputs * cdef SIZE_t* n_classes = self.n_classes * cdef SIZE_t label_count_stride = self.label_count_stride # <<<<<<<<<<<<<< * cdef double* label_count_total = self.label_count_total * */ __pyx_t_2 = __pyx_v_self->__pyx_base.label_count_stride; __pyx_v_label_count_stride = __pyx_t_2; /* "sklearn/_tree.pyx":603 * cdef SIZE_t* n_classes = self.n_classes * cdef SIZE_t label_count_stride = self.label_count_stride * cdef double* label_count_total = self.label_count_total # <<<<<<<<<<<<<< * * cdef double entropy = 0.0 */ __pyx_t_4 = __pyx_v_self->__pyx_base.label_count_total; __pyx_v_label_count_total = __pyx_t_4; /* "sklearn/_tree.pyx":605 * cdef double* label_count_total = self.label_count_total * * cdef double entropy = 0.0 # <<<<<<<<<<<<<< * cdef double total = 0.0 * cdef double tmp */ __pyx_v_entropy = 0.0; /* "sklearn/_tree.pyx":606 * * cdef double entropy = 0.0 * cdef double total = 0.0 # <<<<<<<<<<<<<< * cdef double tmp * cdef SIZE_t k */ __pyx_v_total = 0.0; /* "sklearn/_tree.pyx":611 * cdef SIZE_t c * * for k in range(n_outputs): # <<<<<<<<<<<<<< * entropy = 0.0 * */ __pyx_t_2 = __pyx_v_n_outputs; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_2; __pyx_t_5+=1) { __pyx_v_k = __pyx_t_5; /* "sklearn/_tree.pyx":612 * * for k in range(n_outputs): * entropy = 0.0 # <<<<<<<<<<<<<< * * for c in range(n_classes[k]): */ __pyx_v_entropy = 0.0; /* "sklearn/_tree.pyx":614 * entropy = 0.0 * * for c in range(n_classes[k]): # <<<<<<<<<<<<<< * tmp = label_count_total[c] * if tmp > 0.0: */ __pyx_t_6 = (__pyx_v_n_classes[__pyx_v_k]); for (__pyx_t_7 = 0; __pyx_t_7 < __pyx_t_6; __pyx_t_7+=1) { __pyx_v_c = __pyx_t_7; /* "sklearn/_tree.pyx":615 * * for c in range(n_classes[k]): * tmp = label_count_total[c] # <<<<<<<<<<<<<< * if tmp > 0.0: * tmp /= weighted_n_node_samples */ __pyx_v_tmp = (__pyx_v_label_count_total[__pyx_v_c]); /* "sklearn/_tree.pyx":616 * for c in range(n_classes[k]): * tmp = label_count_total[c] * if tmp > 0.0: # <<<<<<<<<<<<<< * tmp /= weighted_n_node_samples * entropy -= tmp * log(tmp) */ __pyx_t_8 = ((__pyx_v_tmp > 0.0) != 0); if (__pyx_t_8) { /* "sklearn/_tree.pyx":617 * tmp = label_count_total[c] * if tmp > 0.0: * tmp /= weighted_n_node_samples # <<<<<<<<<<<<<< * entropy -= tmp * log(tmp) * */ __pyx_v_tmp = (__pyx_v_tmp / __pyx_v_weighted_n_node_samples); /* "sklearn/_tree.pyx":618 * if tmp > 0.0: * tmp /= weighted_n_node_samples * entropy -= tmp * log(tmp) # <<<<<<<<<<<<<< * * total += entropy */ __pyx_v_entropy = (__pyx_v_entropy - (__pyx_v_tmp * __pyx_f_7sklearn_5_tree_log(__pyx_v_tmp))); goto __pyx_L7; } __pyx_L7:; } /* "sklearn/_tree.pyx":620 * entropy -= tmp * log(tmp) * * total += entropy # <<<<<<<<<<<<<< * label_count_total += label_count_stride * */ __pyx_v_total = (__pyx_v_total + __pyx_v_entropy); /* "sklearn/_tree.pyx":621 * * total += entropy * label_count_total += label_count_stride # <<<<<<<<<<<<<< * * return total / n_outputs */ __pyx_v_label_count_total = (__pyx_v_label_count_total + __pyx_v_label_count_stride); } /* "sklearn/_tree.pyx":623 * label_count_total += label_count_stride * * return total / n_outputs # <<<<<<<<<<<<<< * * cdef void children_impurity(self, double* impurity_left, */ __pyx_r = (__pyx_v_total / __pyx_v_n_outputs); goto __pyx_L0; /* "sklearn/_tree.pyx":595 * cross-entropy = - \sum_{k=0}^{K-1} pmk log(pmk) * """ * cdef double node_impurity(self) nogil: # <<<<<<<<<<<<<< * """Evaluate the impurity of the current node, i.e. the impurity of * samples[start:end].""" */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "sklearn/_tree.pyx":625 * return total / n_outputs * * cdef void children_impurity(self, double* impurity_left, # <<<<<<<<<<<<<< * double* impurity_right) nogil: * """Evaluate the impurity in children nodes, i.e. the impurity of the */ static void __pyx_f_7sklearn_5_tree_7Entropy_children_impurity(struct __pyx_obj_7sklearn_5_tree_Entropy *__pyx_v_self, double *__pyx_v_impurity_left, double *__pyx_v_impurity_right) { CYTHON_UNUSED double __pyx_v_weighted_n_node_samples; double __pyx_v_weighted_n_left; double __pyx_v_weighted_n_right; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_outputs; __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_n_classes; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_label_count_stride; double *__pyx_v_label_count_left; double *__pyx_v_label_count_right; double __pyx_v_entropy_left; double __pyx_v_entropy_right; double __pyx_v_total_left; double __pyx_v_total_right; double __pyx_v_tmp; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_k; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_c; double __pyx_t_1; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_2; __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_t_3; double *__pyx_t_4; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_5; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_6; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_7; int __pyx_t_8; /* "sklearn/_tree.pyx":630 * left child (samples[start:pos]) and the impurity the right child * (samples[pos:end]).""" * cdef double weighted_n_node_samples = self.weighted_n_node_samples # <<<<<<<<<<<<<< * cdef double weighted_n_left = self.weighted_n_left * cdef double weighted_n_right = self.weighted_n_right */ __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.weighted_n_node_samples; __pyx_v_weighted_n_node_samples = __pyx_t_1; /* "sklearn/_tree.pyx":631 * (samples[pos:end]).""" * cdef double weighted_n_node_samples = self.weighted_n_node_samples * cdef double weighted_n_left = self.weighted_n_left # <<<<<<<<<<<<<< * cdef double weighted_n_right = self.weighted_n_right * */ __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.weighted_n_left; __pyx_v_weighted_n_left = __pyx_t_1; /* "sklearn/_tree.pyx":632 * cdef double weighted_n_node_samples = self.weighted_n_node_samples * cdef double weighted_n_left = self.weighted_n_left * cdef double weighted_n_right = self.weighted_n_right # <<<<<<<<<<<<<< * * cdef SIZE_t n_outputs = self.n_outputs */ __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.weighted_n_right; __pyx_v_weighted_n_right = __pyx_t_1; /* "sklearn/_tree.pyx":634 * cdef double weighted_n_right = self.weighted_n_right * * cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<< * cdef SIZE_t* n_classes = self.n_classes * cdef SIZE_t label_count_stride = self.label_count_stride */ __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.n_outputs; __pyx_v_n_outputs = __pyx_t_2; /* "sklearn/_tree.pyx":635 * * cdef SIZE_t n_outputs = self.n_outputs * cdef SIZE_t* n_classes = self.n_classes # <<<<<<<<<<<<<< * cdef SIZE_t label_count_stride = self.label_count_stride * cdef double* label_count_left = self.label_count_left */ __pyx_t_3 = __pyx_v_self->__pyx_base.n_classes; __pyx_v_n_classes = __pyx_t_3; /* "sklearn/_tree.pyx":636 * cdef SIZE_t n_outputs = self.n_outputs * cdef SIZE_t* n_classes = self.n_classes * cdef SIZE_t label_count_stride = self.label_count_stride # <<<<<<<<<<<<<< * cdef double* label_count_left = self.label_count_left * cdef double* label_count_right = self.label_count_right */ __pyx_t_2 = __pyx_v_self->__pyx_base.label_count_stride; __pyx_v_label_count_stride = __pyx_t_2; /* "sklearn/_tree.pyx":637 * cdef SIZE_t* n_classes = self.n_classes * cdef SIZE_t label_count_stride = self.label_count_stride * cdef double* label_count_left = self.label_count_left # <<<<<<<<<<<<<< * cdef double* label_count_right = self.label_count_right * */ __pyx_t_4 = __pyx_v_self->__pyx_base.label_count_left; __pyx_v_label_count_left = __pyx_t_4; /* "sklearn/_tree.pyx":638 * cdef SIZE_t label_count_stride = self.label_count_stride * cdef double* label_count_left = self.label_count_left * cdef double* label_count_right = self.label_count_right # <<<<<<<<<<<<<< * * cdef double entropy_left = 0.0 */ __pyx_t_4 = __pyx_v_self->__pyx_base.label_count_right; __pyx_v_label_count_right = __pyx_t_4; /* "sklearn/_tree.pyx":640 * cdef double* label_count_right = self.label_count_right * * cdef double entropy_left = 0.0 # <<<<<<<<<<<<<< * cdef double entropy_right = 0.0 * cdef double total_left = 0.0 */ __pyx_v_entropy_left = 0.0; /* "sklearn/_tree.pyx":641 * * cdef double entropy_left = 0.0 * cdef double entropy_right = 0.0 # <<<<<<<<<<<<<< * cdef double total_left = 0.0 * cdef double total_right = 0.0 */ __pyx_v_entropy_right = 0.0; /* "sklearn/_tree.pyx":642 * cdef double entropy_left = 0.0 * cdef double entropy_right = 0.0 * cdef double total_left = 0.0 # <<<<<<<<<<<<<< * cdef double total_right = 0.0 * cdef double tmp */ __pyx_v_total_left = 0.0; /* "sklearn/_tree.pyx":643 * cdef double entropy_right = 0.0 * cdef double total_left = 0.0 * cdef double total_right = 0.0 # <<<<<<<<<<<<<< * cdef double tmp * cdef SIZE_t k */ __pyx_v_total_right = 0.0; /* "sklearn/_tree.pyx":648 * cdef SIZE_t c * * for k in range(n_outputs): # <<<<<<<<<<<<<< * entropy_left = 0.0 * entropy_right = 0.0 */ __pyx_t_2 = __pyx_v_n_outputs; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_2; __pyx_t_5+=1) { __pyx_v_k = __pyx_t_5; /* "sklearn/_tree.pyx":649 * * for k in range(n_outputs): * entropy_left = 0.0 # <<<<<<<<<<<<<< * entropy_right = 0.0 * */ __pyx_v_entropy_left = 0.0; /* "sklearn/_tree.pyx":650 * for k in range(n_outputs): * entropy_left = 0.0 * entropy_right = 0.0 # <<<<<<<<<<<<<< * * for c in range(n_classes[k]): */ __pyx_v_entropy_right = 0.0; /* "sklearn/_tree.pyx":652 * entropy_right = 0.0 * * for c in range(n_classes[k]): # <<<<<<<<<<<<<< * tmp = label_count_left[c] * if tmp > 0.0: */ __pyx_t_6 = (__pyx_v_n_classes[__pyx_v_k]); for (__pyx_t_7 = 0; __pyx_t_7 < __pyx_t_6; __pyx_t_7+=1) { __pyx_v_c = __pyx_t_7; /* "sklearn/_tree.pyx":653 * * for c in range(n_classes[k]): * tmp = label_count_left[c] # <<<<<<<<<<<<<< * if tmp > 0.0: * tmp /= weighted_n_left */ __pyx_v_tmp = (__pyx_v_label_count_left[__pyx_v_c]); /* "sklearn/_tree.pyx":654 * for c in range(n_classes[k]): * tmp = label_count_left[c] * if tmp > 0.0: # <<<<<<<<<<<<<< * tmp /= weighted_n_left * entropy_left -= tmp * log(tmp) */ __pyx_t_8 = ((__pyx_v_tmp > 0.0) != 0); if (__pyx_t_8) { /* "sklearn/_tree.pyx":655 * tmp = label_count_left[c] * if tmp > 0.0: * tmp /= weighted_n_left # <<<<<<<<<<<<<< * entropy_left -= tmp * log(tmp) * */ __pyx_v_tmp = (__pyx_v_tmp / __pyx_v_weighted_n_left); /* "sklearn/_tree.pyx":656 * if tmp > 0.0: * tmp /= weighted_n_left * entropy_left -= tmp * log(tmp) # <<<<<<<<<<<<<< * * tmp = label_count_right[c] */ __pyx_v_entropy_left = (__pyx_v_entropy_left - (__pyx_v_tmp * __pyx_f_7sklearn_5_tree_log(__pyx_v_tmp))); goto __pyx_L7; } __pyx_L7:; /* "sklearn/_tree.pyx":658 * entropy_left -= tmp * log(tmp) * * tmp = label_count_right[c] # <<<<<<<<<<<<<< * if tmp > 0.0: * tmp /= weighted_n_right */ __pyx_v_tmp = (__pyx_v_label_count_right[__pyx_v_c]); /* "sklearn/_tree.pyx":659 * * tmp = label_count_right[c] * if tmp > 0.0: # <<<<<<<<<<<<<< * tmp /= weighted_n_right * entropy_right -= tmp * log(tmp) */ __pyx_t_8 = ((__pyx_v_tmp > 0.0) != 0); if (__pyx_t_8) { /* "sklearn/_tree.pyx":660 * tmp = label_count_right[c] * if tmp > 0.0: * tmp /= weighted_n_right # <<<<<<<<<<<<<< * entropy_right -= tmp * log(tmp) * */ __pyx_v_tmp = (__pyx_v_tmp / __pyx_v_weighted_n_right); /* "sklearn/_tree.pyx":661 * if tmp > 0.0: * tmp /= weighted_n_right * entropy_right -= tmp * log(tmp) # <<<<<<<<<<<<<< * * total_left += entropy_left */ __pyx_v_entropy_right = (__pyx_v_entropy_right - (__pyx_v_tmp * __pyx_f_7sklearn_5_tree_log(__pyx_v_tmp))); goto __pyx_L8; } __pyx_L8:; } /* "sklearn/_tree.pyx":663 * entropy_right -= tmp * log(tmp) * * total_left += entropy_left # <<<<<<<<<<<<<< * total_right += entropy_right * label_count_left += label_count_stride */ __pyx_v_total_left = (__pyx_v_total_left + __pyx_v_entropy_left); /* "sklearn/_tree.pyx":664 * * total_left += entropy_left * total_right += entropy_right # <<<<<<<<<<<<<< * label_count_left += label_count_stride * label_count_right += label_count_stride */ __pyx_v_total_right = (__pyx_v_total_right + __pyx_v_entropy_right); /* "sklearn/_tree.pyx":665 * total_left += entropy_left * total_right += entropy_right * label_count_left += label_count_stride # <<<<<<<<<<<<<< * label_count_right += label_count_stride * */ __pyx_v_label_count_left = (__pyx_v_label_count_left + __pyx_v_label_count_stride); /* "sklearn/_tree.pyx":666 * total_right += entropy_right * label_count_left += label_count_stride * label_count_right += label_count_stride # <<<<<<<<<<<<<< * * impurity_left[0] = total_left / n_outputs */ __pyx_v_label_count_right = (__pyx_v_label_count_right + __pyx_v_label_count_stride); } /* "sklearn/_tree.pyx":668 * label_count_right += label_count_stride * * impurity_left[0] = total_left / n_outputs # <<<<<<<<<<<<<< * impurity_right[0] = total_right / n_outputs * */ (__pyx_v_impurity_left[0]) = (__pyx_v_total_left / __pyx_v_n_outputs); /* "sklearn/_tree.pyx":669 * * impurity_left[0] = total_left / n_outputs * impurity_right[0] = total_right / n_outputs # <<<<<<<<<<<<<< * * */ (__pyx_v_impurity_right[0]) = (__pyx_v_total_right / __pyx_v_n_outputs); /* "sklearn/_tree.pyx":625 * return total / n_outputs * * cdef void children_impurity(self, double* impurity_left, # <<<<<<<<<<<<<< * double* impurity_right) nogil: * """Evaluate the impurity in children nodes, i.e. the impurity of the */ /* function exit code */ } /* "sklearn/_tree.pyx":687 * = 1 - \sum_{k=0}^{K-1} pmk ** 2 * """ * cdef double node_impurity(self) nogil: # <<<<<<<<<<<<<< * """Evaluate the impurity of the current node, i.e. the impurity of * samples[start:end].""" */ static double __pyx_f_7sklearn_5_tree_4Gini_node_impurity(struct __pyx_obj_7sklearn_5_tree_Gini *__pyx_v_self) { double __pyx_v_weighted_n_node_samples; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_outputs; __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_n_classes; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_label_count_stride; double *__pyx_v_label_count_total; double __pyx_v_gini; double __pyx_v_total; double __pyx_v_tmp; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_k; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_c; double __pyx_r; double __pyx_t_1; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_2; __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_t_3; double *__pyx_t_4; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_5; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_6; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_7; /* "sklearn/_tree.pyx":690 * """Evaluate the impurity of the current node, i.e. the impurity of * samples[start:end].""" * cdef double weighted_n_node_samples = self.weighted_n_node_samples # <<<<<<<<<<<<<< * * cdef SIZE_t n_outputs = self.n_outputs */ __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.weighted_n_node_samples; __pyx_v_weighted_n_node_samples = __pyx_t_1; /* "sklearn/_tree.pyx":692 * cdef double weighted_n_node_samples = self.weighted_n_node_samples * * cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<< * cdef SIZE_t* n_classes = self.n_classes * cdef SIZE_t label_count_stride = self.label_count_stride */ __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.n_outputs; __pyx_v_n_outputs = __pyx_t_2; /* "sklearn/_tree.pyx":693 * * cdef SIZE_t n_outputs = self.n_outputs * cdef SIZE_t* n_classes = self.n_classes # <<<<<<<<<<<<<< * cdef SIZE_t label_count_stride = self.label_count_stride * cdef double* label_count_total = self.label_count_total */ __pyx_t_3 = __pyx_v_self->__pyx_base.n_classes; __pyx_v_n_classes = __pyx_t_3; /* "sklearn/_tree.pyx":694 * cdef SIZE_t n_outputs = self.n_outputs * cdef SIZE_t* n_classes = self.n_classes * cdef SIZE_t label_count_stride = self.label_count_stride # <<<<<<<<<<<<<< * cdef double* label_count_total = self.label_count_total * */ __pyx_t_2 = __pyx_v_self->__pyx_base.label_count_stride; __pyx_v_label_count_stride = __pyx_t_2; /* "sklearn/_tree.pyx":695 * cdef SIZE_t* n_classes = self.n_classes * cdef SIZE_t label_count_stride = self.label_count_stride * cdef double* label_count_total = self.label_count_total # <<<<<<<<<<<<<< * * cdef double gini = 0.0 */ __pyx_t_4 = __pyx_v_self->__pyx_base.label_count_total; __pyx_v_label_count_total = __pyx_t_4; /* "sklearn/_tree.pyx":697 * cdef double* label_count_total = self.label_count_total * * cdef double gini = 0.0 # <<<<<<<<<<<<<< * cdef double total = 0.0 * cdef double tmp */ __pyx_v_gini = 0.0; /* "sklearn/_tree.pyx":698 * * cdef double gini = 0.0 * cdef double total = 0.0 # <<<<<<<<<<<<<< * cdef double tmp * cdef SIZE_t k */ __pyx_v_total = 0.0; /* "sklearn/_tree.pyx":703 * cdef SIZE_t c * * for k in range(n_outputs): # <<<<<<<<<<<<<< * gini = 0.0 * */ __pyx_t_2 = __pyx_v_n_outputs; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_2; __pyx_t_5+=1) { __pyx_v_k = __pyx_t_5; /* "sklearn/_tree.pyx":704 * * for k in range(n_outputs): * gini = 0.0 # <<<<<<<<<<<<<< * * for c in range(n_classes[k]): */ __pyx_v_gini = 0.0; /* "sklearn/_tree.pyx":706 * gini = 0.0 * * for c in range(n_classes[k]): # <<<<<<<<<<<<<< * tmp = label_count_total[c] * gini += tmp * tmp */ __pyx_t_6 = (__pyx_v_n_classes[__pyx_v_k]); for (__pyx_t_7 = 0; __pyx_t_7 < __pyx_t_6; __pyx_t_7+=1) { __pyx_v_c = __pyx_t_7; /* "sklearn/_tree.pyx":707 * * for c in range(n_classes[k]): * tmp = label_count_total[c] # <<<<<<<<<<<<<< * gini += tmp * tmp * */ __pyx_v_tmp = (__pyx_v_label_count_total[__pyx_v_c]); /* "sklearn/_tree.pyx":708 * for c in range(n_classes[k]): * tmp = label_count_total[c] * gini += tmp * tmp # <<<<<<<<<<<<<< * * gini = 1.0 - gini / (weighted_n_node_samples * */ __pyx_v_gini = (__pyx_v_gini + (__pyx_v_tmp * __pyx_v_tmp)); } /* "sklearn/_tree.pyx":710 * gini += tmp * tmp * * gini = 1.0 - gini / (weighted_n_node_samples * # <<<<<<<<<<<<<< * weighted_n_node_samples) * */ __pyx_v_gini = (1.0 - (__pyx_v_gini / (__pyx_v_weighted_n_node_samples * __pyx_v_weighted_n_node_samples))); /* "sklearn/_tree.pyx":713 * weighted_n_node_samples) * * total += gini # <<<<<<<<<<<<<< * label_count_total += label_count_stride * */ __pyx_v_total = (__pyx_v_total + __pyx_v_gini); /* "sklearn/_tree.pyx":714 * * total += gini * label_count_total += label_count_stride # <<<<<<<<<<<<<< * * return total / n_outputs */ __pyx_v_label_count_total = (__pyx_v_label_count_total + __pyx_v_label_count_stride); } /* "sklearn/_tree.pyx":716 * label_count_total += label_count_stride * * return total / n_outputs # <<<<<<<<<<<<<< * * cdef void children_impurity(self, double* impurity_left, */ __pyx_r = (__pyx_v_total / __pyx_v_n_outputs); goto __pyx_L0; /* "sklearn/_tree.pyx":687 * = 1 - \sum_{k=0}^{K-1} pmk ** 2 * """ * cdef double node_impurity(self) nogil: # <<<<<<<<<<<<<< * """Evaluate the impurity of the current node, i.e. the impurity of * samples[start:end].""" */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "sklearn/_tree.pyx":718 * return total / n_outputs * * cdef void children_impurity(self, double* impurity_left, # <<<<<<<<<<<<<< * double* impurity_right) nogil: * """Evaluate the impurity in children nodes, i.e. the impurity of the */ static void __pyx_f_7sklearn_5_tree_4Gini_children_impurity(struct __pyx_obj_7sklearn_5_tree_Gini *__pyx_v_self, double *__pyx_v_impurity_left, double *__pyx_v_impurity_right) { CYTHON_UNUSED double __pyx_v_weighted_n_node_samples; double __pyx_v_weighted_n_left; double __pyx_v_weighted_n_right; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_outputs; __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_n_classes; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_label_count_stride; double *__pyx_v_label_count_left; double *__pyx_v_label_count_right; double __pyx_v_gini_left; double __pyx_v_gini_right; CYTHON_UNUSED double __pyx_v_total; double __pyx_v_total_left; double __pyx_v_total_right; double __pyx_v_tmp; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_k; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_c; double __pyx_t_1; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_2; __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_t_3; double *__pyx_t_4; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_5; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_6; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_7; /* "sklearn/_tree.pyx":723 * left child (samples[start:pos]) and the impurity the right child * (samples[pos:end]).""" * cdef double weighted_n_node_samples = self.weighted_n_node_samples # <<<<<<<<<<<<<< * cdef double weighted_n_left = self.weighted_n_left * cdef double weighted_n_right = self.weighted_n_right */ __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.weighted_n_node_samples; __pyx_v_weighted_n_node_samples = __pyx_t_1; /* "sklearn/_tree.pyx":724 * (samples[pos:end]).""" * cdef double weighted_n_node_samples = self.weighted_n_node_samples * cdef double weighted_n_left = self.weighted_n_left # <<<<<<<<<<<<<< * cdef double weighted_n_right = self.weighted_n_right * */ __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.weighted_n_left; __pyx_v_weighted_n_left = __pyx_t_1; /* "sklearn/_tree.pyx":725 * cdef double weighted_n_node_samples = self.weighted_n_node_samples * cdef double weighted_n_left = self.weighted_n_left * cdef double weighted_n_right = self.weighted_n_right # <<<<<<<<<<<<<< * * cdef SIZE_t n_outputs = self.n_outputs */ __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.weighted_n_right; __pyx_v_weighted_n_right = __pyx_t_1; /* "sklearn/_tree.pyx":727 * cdef double weighted_n_right = self.weighted_n_right * * cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<< * cdef SIZE_t* n_classes = self.n_classes * cdef SIZE_t label_count_stride = self.label_count_stride */ __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.n_outputs; __pyx_v_n_outputs = __pyx_t_2; /* "sklearn/_tree.pyx":728 * * cdef SIZE_t n_outputs = self.n_outputs * cdef SIZE_t* n_classes = self.n_classes # <<<<<<<<<<<<<< * cdef SIZE_t label_count_stride = self.label_count_stride * cdef double* label_count_left = self.label_count_left */ __pyx_t_3 = __pyx_v_self->__pyx_base.n_classes; __pyx_v_n_classes = __pyx_t_3; /* "sklearn/_tree.pyx":729 * cdef SIZE_t n_outputs = self.n_outputs * cdef SIZE_t* n_classes = self.n_classes * cdef SIZE_t label_count_stride = self.label_count_stride # <<<<<<<<<<<<<< * cdef double* label_count_left = self.label_count_left * cdef double* label_count_right = self.label_count_right */ __pyx_t_2 = __pyx_v_self->__pyx_base.label_count_stride; __pyx_v_label_count_stride = __pyx_t_2; /* "sklearn/_tree.pyx":730 * cdef SIZE_t* n_classes = self.n_classes * cdef SIZE_t label_count_stride = self.label_count_stride * cdef double* label_count_left = self.label_count_left # <<<<<<<<<<<<<< * cdef double* label_count_right = self.label_count_right * */ __pyx_t_4 = __pyx_v_self->__pyx_base.label_count_left; __pyx_v_label_count_left = __pyx_t_4; /* "sklearn/_tree.pyx":731 * cdef SIZE_t label_count_stride = self.label_count_stride * cdef double* label_count_left = self.label_count_left * cdef double* label_count_right = self.label_count_right # <<<<<<<<<<<<<< * * cdef double gini_left = 0.0 */ __pyx_t_4 = __pyx_v_self->__pyx_base.label_count_right; __pyx_v_label_count_right = __pyx_t_4; /* "sklearn/_tree.pyx":733 * cdef double* label_count_right = self.label_count_right * * cdef double gini_left = 0.0 # <<<<<<<<<<<<<< * cdef double gini_right = 0.0 * cdef double total = 0.0 */ __pyx_v_gini_left = 0.0; /* "sklearn/_tree.pyx":734 * * cdef double gini_left = 0.0 * cdef double gini_right = 0.0 # <<<<<<<<<<<<<< * cdef double total = 0.0 * cdef double total_left = 0.0 */ __pyx_v_gini_right = 0.0; /* "sklearn/_tree.pyx":735 * cdef double gini_left = 0.0 * cdef double gini_right = 0.0 * cdef double total = 0.0 # <<<<<<<<<<<<<< * cdef double total_left = 0.0 * cdef double total_right = 0.0 */ __pyx_v_total = 0.0; /* "sklearn/_tree.pyx":736 * cdef double gini_right = 0.0 * cdef double total = 0.0 * cdef double total_left = 0.0 # <<<<<<<<<<<<<< * cdef double total_right = 0.0 * cdef double tmp */ __pyx_v_total_left = 0.0; /* "sklearn/_tree.pyx":737 * cdef double total = 0.0 * cdef double total_left = 0.0 * cdef double total_right = 0.0 # <<<<<<<<<<<<<< * cdef double tmp * cdef SIZE_t k */ __pyx_v_total_right = 0.0; /* "sklearn/_tree.pyx":742 * cdef SIZE_t c * * for k in range(n_outputs): # <<<<<<<<<<<<<< * gini_left = 0.0 * gini_right = 0.0 */ __pyx_t_2 = __pyx_v_n_outputs; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_2; __pyx_t_5+=1) { __pyx_v_k = __pyx_t_5; /* "sklearn/_tree.pyx":743 * * for k in range(n_outputs): * gini_left = 0.0 # <<<<<<<<<<<<<< * gini_right = 0.0 * */ __pyx_v_gini_left = 0.0; /* "sklearn/_tree.pyx":744 * for k in range(n_outputs): * gini_left = 0.0 * gini_right = 0.0 # <<<<<<<<<<<<<< * * for c in range(n_classes[k]): */ __pyx_v_gini_right = 0.0; /* "sklearn/_tree.pyx":746 * gini_right = 0.0 * * for c in range(n_classes[k]): # <<<<<<<<<<<<<< * tmp = label_count_left[c] * gini_left += tmp * tmp */ __pyx_t_6 = (__pyx_v_n_classes[__pyx_v_k]); for (__pyx_t_7 = 0; __pyx_t_7 < __pyx_t_6; __pyx_t_7+=1) { __pyx_v_c = __pyx_t_7; /* "sklearn/_tree.pyx":747 * * for c in range(n_classes[k]): * tmp = label_count_left[c] # <<<<<<<<<<<<<< * gini_left += tmp * tmp * tmp = label_count_right[c] */ __pyx_v_tmp = (__pyx_v_label_count_left[__pyx_v_c]); /* "sklearn/_tree.pyx":748 * for c in range(n_classes[k]): * tmp = label_count_left[c] * gini_left += tmp * tmp # <<<<<<<<<<<<<< * tmp = label_count_right[c] * gini_right += tmp * tmp */ __pyx_v_gini_left = (__pyx_v_gini_left + (__pyx_v_tmp * __pyx_v_tmp)); /* "sklearn/_tree.pyx":749 * tmp = label_count_left[c] * gini_left += tmp * tmp * tmp = label_count_right[c] # <<<<<<<<<<<<<< * gini_right += tmp * tmp * */ __pyx_v_tmp = (__pyx_v_label_count_right[__pyx_v_c]); /* "sklearn/_tree.pyx":750 * gini_left += tmp * tmp * tmp = label_count_right[c] * gini_right += tmp * tmp # <<<<<<<<<<<<<< * * gini_left = 1.0 - gini_left / (weighted_n_left * */ __pyx_v_gini_right = (__pyx_v_gini_right + (__pyx_v_tmp * __pyx_v_tmp)); } /* "sklearn/_tree.pyx":752 * gini_right += tmp * tmp * * gini_left = 1.0 - gini_left / (weighted_n_left * # <<<<<<<<<<<<<< * weighted_n_left) * gini_right = 1.0 - gini_right / (weighted_n_right * */ __pyx_v_gini_left = (1.0 - (__pyx_v_gini_left / (__pyx_v_weighted_n_left * __pyx_v_weighted_n_left))); /* "sklearn/_tree.pyx":754 * gini_left = 1.0 - gini_left / (weighted_n_left * * weighted_n_left) * gini_right = 1.0 - gini_right / (weighted_n_right * # <<<<<<<<<<<<<< * weighted_n_right) * */ __pyx_v_gini_right = (1.0 - (__pyx_v_gini_right / (__pyx_v_weighted_n_right * __pyx_v_weighted_n_right))); /* "sklearn/_tree.pyx":757 * weighted_n_right) * * total_left += gini_left # <<<<<<<<<<<<<< * total_right += gini_right * label_count_left += label_count_stride */ __pyx_v_total_left = (__pyx_v_total_left + __pyx_v_gini_left); /* "sklearn/_tree.pyx":758 * * total_left += gini_left * total_right += gini_right # <<<<<<<<<<<<<< * label_count_left += label_count_stride * label_count_right += label_count_stride */ __pyx_v_total_right = (__pyx_v_total_right + __pyx_v_gini_right); /* "sklearn/_tree.pyx":759 * total_left += gini_left * total_right += gini_right * label_count_left += label_count_stride # <<<<<<<<<<<<<< * label_count_right += label_count_stride * */ __pyx_v_label_count_left = (__pyx_v_label_count_left + __pyx_v_label_count_stride); /* "sklearn/_tree.pyx":760 * total_right += gini_right * label_count_left += label_count_stride * label_count_right += label_count_stride # <<<<<<<<<<<<<< * * impurity_left[0] = total_left / n_outputs */ __pyx_v_label_count_right = (__pyx_v_label_count_right + __pyx_v_label_count_stride); } /* "sklearn/_tree.pyx":762 * label_count_right += label_count_stride * * impurity_left[0] = total_left / n_outputs # <<<<<<<<<<<<<< * impurity_right[0] = total_right / n_outputs * */ (__pyx_v_impurity_left[0]) = (__pyx_v_total_left / __pyx_v_n_outputs); /* "sklearn/_tree.pyx":763 * * impurity_left[0] = total_left / n_outputs * impurity_right[0] = total_right / n_outputs # <<<<<<<<<<<<<< * * */ (__pyx_v_impurity_right[0]) = (__pyx_v_total_right / __pyx_v_n_outputs); /* "sklearn/_tree.pyx":718 * return total / n_outputs * * cdef void children_impurity(self, double* impurity_left, # <<<<<<<<<<<<<< * double* impurity_right) nogil: * """Evaluate the impurity in children nodes, i.e. the impurity of the */ /* function exit code */ } /* "sklearn/_tree.pyx":787 * cdef double* sum_total * * def __cinit__(self, SIZE_t n_outputs): # <<<<<<<<<<<<<< * # Default values * self.y = NULL */ /* Python wrapper */ static int __pyx_pw_7sklearn_5_tree_19RegressionCriterion_1__cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static int __pyx_pw_7sklearn_5_tree_19RegressionCriterion_1__cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_outputs; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__cinit__ (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_n_outputs,0}; PyObject* values[1] = {0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_n_outputs)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__cinit__") < 0)) {__pyx_filename = __pyx_f[0]; 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/* "sklearn/_tree.pyx":800 * self.n_outputs = n_outputs * self.n_node_samples = 0 * self.weighted_n_node_samples = 0.0 # <<<<<<<<<<<<<< * self.weighted_n_left = 0.0 * self.weighted_n_right = 0.0 */ __pyx_v_self->__pyx_base.weighted_n_node_samples = 0.0; /* "sklearn/_tree.pyx":801 * self.n_node_samples = 0 * self.weighted_n_node_samples = 0.0 * self.weighted_n_left = 0.0 # <<<<<<<<<<<<<< * self.weighted_n_right = 0.0 * */ __pyx_v_self->__pyx_base.weighted_n_left = 0.0; /* "sklearn/_tree.pyx":802 * self.weighted_n_node_samples = 0.0 * self.weighted_n_left = 0.0 * self.weighted_n_right = 0.0 # <<<<<<<<<<<<<< * * # Allocate accumulators. 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/* "sklearn/_tree.pyx":811 * self.sq_sum_left = NULL * self.sq_sum_right = NULL * self.sq_sum_total = NULL # <<<<<<<<<<<<<< * self.var_left = NULL * self.var_right = NULL */ __pyx_v_self->sq_sum_total = NULL; /* "sklearn/_tree.pyx":812 * self.sq_sum_right = NULL * self.sq_sum_total = NULL * self.var_left = NULL # <<<<<<<<<<<<<< * self.var_right = NULL * self.sum_left = NULL */ __pyx_v_self->var_left = NULL; /* "sklearn/_tree.pyx":813 * self.sq_sum_total = NULL * self.var_left = NULL * self.var_right = NULL # <<<<<<<<<<<<<< * self.sum_left = NULL * self.sum_right = NULL */ __pyx_v_self->var_right = NULL; /* "sklearn/_tree.pyx":814 * self.var_left = NULL * self.var_right = NULL * self.sum_left = NULL # <<<<<<<<<<<<<< * self.sum_right = NULL * self.sum_total = NULL */ __pyx_v_self->sum_left = NULL; /* "sklearn/_tree.pyx":815 * self.var_right = NULL * self.sum_left = NULL * self.sum_right = NULL # <<<<<<<<<<<<<< * self.sum_total = NULL * */ __pyx_v_self->sum_right = NULL; /* "sklearn/_tree.pyx":816 * self.sum_left = NULL * self.sum_right = NULL * self.sum_total = NULL # <<<<<<<<<<<<<< * * self.mean_left = calloc(n_outputs, sizeof(double)) */ __pyx_v_self->sum_total = NULL; 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/* "sklearn/_tree.pyx":821 * self.mean_right = calloc(n_outputs, sizeof(double)) * self.mean_total = calloc(n_outputs, sizeof(double)) * self.sq_sum_left = calloc(n_outputs, sizeof(double)) # <<<<<<<<<<<<<< * self.sq_sum_right = calloc(n_outputs, sizeof(double)) * self.sq_sum_total = calloc(n_outputs, sizeof(double)) */ __pyx_v_self->sq_sum_left = ((double *)calloc(__pyx_v_n_outputs, (sizeof(double)))); /* "sklearn/_tree.pyx":822 * self.mean_total = calloc(n_outputs, sizeof(double)) * self.sq_sum_left = calloc(n_outputs, sizeof(double)) * self.sq_sum_right = calloc(n_outputs, sizeof(double)) # <<<<<<<<<<<<<< * self.sq_sum_total = calloc(n_outputs, sizeof(double)) * self.var_left = calloc(n_outputs, sizeof(double)) */ __pyx_v_self->sq_sum_right = ((double *)calloc(__pyx_v_n_outputs, (sizeof(double)))); /* "sklearn/_tree.pyx":823 * self.sq_sum_left = calloc(n_outputs, sizeof(double)) * self.sq_sum_right = calloc(n_outputs, sizeof(double)) * self.sq_sum_total = calloc(n_outputs, sizeof(double)) # <<<<<<<<<<<<<< * self.var_left = calloc(n_outputs, sizeof(double)) * self.var_right = calloc(n_outputs, sizeof(double)) */ __pyx_v_self->sq_sum_total = ((double *)calloc(__pyx_v_n_outputs, (sizeof(double)))); /* "sklearn/_tree.pyx":824 * self.sq_sum_right = calloc(n_outputs, sizeof(double)) * self.sq_sum_total = calloc(n_outputs, sizeof(double)) * self.var_left = calloc(n_outputs, sizeof(double)) # <<<<<<<<<<<<<< * self.var_right = calloc(n_outputs, sizeof(double)) * self.sum_left = calloc(n_outputs, sizeof(double)) */ __pyx_v_self->var_left = ((double *)calloc(__pyx_v_n_outputs, (sizeof(double)))); /* "sklearn/_tree.pyx":825 * self.sq_sum_total = calloc(n_outputs, sizeof(double)) * self.var_left = calloc(n_outputs, sizeof(double)) * self.var_right = calloc(n_outputs, sizeof(double)) # <<<<<<<<<<<<<< * self.sum_left = calloc(n_outputs, sizeof(double)) * self.sum_right = calloc(n_outputs, sizeof(double)) */ __pyx_v_self->var_right = ((double *)calloc(__pyx_v_n_outputs, (sizeof(double)))); /* "sklearn/_tree.pyx":826 * self.var_left = calloc(n_outputs, sizeof(double)) * self.var_right = calloc(n_outputs, sizeof(double)) * self.sum_left = calloc(n_outputs, sizeof(double)) # <<<<<<<<<<<<<< * self.sum_right = calloc(n_outputs, sizeof(double)) * self.sum_total = calloc(n_outputs, sizeof(double)) */ __pyx_v_self->sum_left = ((double *)calloc(__pyx_v_n_outputs, (sizeof(double)))); /* "sklearn/_tree.pyx":827 * self.var_right = calloc(n_outputs, sizeof(double)) * self.sum_left = calloc(n_outputs, sizeof(double)) * self.sum_right = calloc(n_outputs, sizeof(double)) # <<<<<<<<<<<<<< * self.sum_total = calloc(n_outputs, sizeof(double)) * */ __pyx_v_self->sum_right = ((double *)calloc(__pyx_v_n_outputs, (sizeof(double)))); /* "sklearn/_tree.pyx":828 * self.sum_left = calloc(n_outputs, sizeof(double)) * self.sum_right = calloc(n_outputs, sizeof(double)) * self.sum_total = calloc(n_outputs, sizeof(double)) # <<<<<<<<<<<<<< * * if (self.mean_left == NULL or */ __pyx_v_self->sum_total = ((double *)calloc(__pyx_v_n_outputs, (sizeof(double)))); /* "sklearn/_tree.pyx":830 * self.sum_total = calloc(n_outputs, sizeof(double)) * * if (self.mean_left == NULL or # <<<<<<<<<<<<<< * self.mean_right == NULL or * self.mean_total == NULL or */ __pyx_t_2 = ((__pyx_v_self->mean_left == NULL) != 0); if (!__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L4_bool_binop_done; } /* "sklearn/_tree.pyx":831 * * if (self.mean_left == NULL or * self.mean_right == NULL or # <<<<<<<<<<<<<< * self.mean_total == NULL or * self.sq_sum_left == NULL or */ __pyx_t_2 = ((__pyx_v_self->mean_right == NULL) != 0); if (!__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L4_bool_binop_done; } /* "sklearn/_tree.pyx":832 * if (self.mean_left == NULL or * self.mean_right == NULL or * self.mean_total == NULL or # <<<<<<<<<<<<<< * self.sq_sum_left == NULL or * self.sq_sum_right == NULL or */ __pyx_t_2 = ((__pyx_v_self->mean_total == NULL) != 0); if (!__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L4_bool_binop_done; } /* "sklearn/_tree.pyx":833 * self.mean_right == NULL or * self.mean_total == NULL or * self.sq_sum_left == NULL or # <<<<<<<<<<<<<< * self.sq_sum_right == NULL or * self.sq_sum_total == NULL or */ __pyx_t_2 = ((__pyx_v_self->sq_sum_left == NULL) != 0); if (!__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L4_bool_binop_done; } /* "sklearn/_tree.pyx":834 * self.mean_total == NULL or * self.sq_sum_left == NULL or * self.sq_sum_right == NULL or # <<<<<<<<<<<<<< * self.sq_sum_total == NULL or * self.var_left == NULL or */ __pyx_t_2 = ((__pyx_v_self->sq_sum_right == NULL) != 0); 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/* "sklearn/_tree.pyx":887 * cdef double* mean_right = self.mean_right * cdef double* mean_total = self.mean_total * cdef double* sq_sum_left = self.sq_sum_left # <<<<<<<<<<<<<< * cdef double* sq_sum_right = self.sq_sum_right * cdef double* sq_sum_total = self.sq_sum_total */ __pyx_t_2 = __pyx_v_self->sq_sum_left; __pyx_v_sq_sum_left = __pyx_t_2; /* "sklearn/_tree.pyx":888 * cdef double* mean_total = self.mean_total * cdef double* sq_sum_left = self.sq_sum_left * cdef double* sq_sum_right = self.sq_sum_right # <<<<<<<<<<<<<< * cdef double* sq_sum_total = self.sq_sum_total * cdef double* var_left = self.var_left */ __pyx_t_2 = __pyx_v_self->sq_sum_right; __pyx_v_sq_sum_right = __pyx_t_2; /* "sklearn/_tree.pyx":889 * cdef double* sq_sum_left = self.sq_sum_left * cdef double* sq_sum_right = self.sq_sum_right * cdef double* sq_sum_total = self.sq_sum_total # <<<<<<<<<<<<<< * cdef double* var_left = self.var_left * cdef double* var_right = self.var_right */ __pyx_t_2 = __pyx_v_self->sq_sum_total; __pyx_v_sq_sum_total = __pyx_t_2; /* "sklearn/_tree.pyx":890 * cdef double* sq_sum_right = self.sq_sum_right * cdef double* sq_sum_total = self.sq_sum_total * cdef double* var_left = self.var_left # <<<<<<<<<<<<<< * cdef double* var_right = self.var_right * cdef double* sum_left = self.sum_left */ __pyx_t_2 = __pyx_v_self->var_left; __pyx_v_var_left = __pyx_t_2; /* "sklearn/_tree.pyx":891 * cdef double* sq_sum_total = self.sq_sum_total * cdef double* var_left = self.var_left * cdef double* var_right = self.var_right # <<<<<<<<<<<<<< * cdef double* sum_left = self.sum_left * cdef double* sum_right = self.sum_right */ __pyx_t_2 = __pyx_v_self->var_right; __pyx_v_var_right = __pyx_t_2; /* "sklearn/_tree.pyx":892 * cdef double* var_left = self.var_left * cdef double* var_right = self.var_right * cdef double* sum_left = self.sum_left # <<<<<<<<<<<<<< * cdef double* sum_right = self.sum_right * cdef double* sum_total = self.sum_total */ __pyx_t_2 = __pyx_v_self->sum_left; __pyx_v_sum_left = __pyx_t_2; /* "sklearn/_tree.pyx":893 * cdef double* var_right = self.var_right * cdef double* sum_left = self.sum_left * cdef double* sum_right = self.sum_right # <<<<<<<<<<<<<< * cdef double* sum_total = self.sum_total * */ __pyx_t_2 = __pyx_v_self->sum_right; __pyx_v_sum_right = __pyx_t_2; /* "sklearn/_tree.pyx":894 * cdef double* sum_left = self.sum_left * cdef double* sum_right = self.sum_right * cdef double* sum_total = self.sum_total # <<<<<<<<<<<<<< * * cdef SIZE_t i = 0 */ __pyx_t_2 = __pyx_v_self->sum_total; __pyx_v_sum_total = __pyx_t_2; /* "sklearn/_tree.pyx":896 * cdef double* sum_total = self.sum_total * * cdef SIZE_t i = 0 # <<<<<<<<<<<<<< * cdef SIZE_t p = 0 * cdef SIZE_t k = 0 */ __pyx_v_i = 0; /* "sklearn/_tree.pyx":897 * * cdef SIZE_t i = 0 * cdef SIZE_t p = 0 # <<<<<<<<<<<<<< * cdef SIZE_t k = 0 * cdef DOUBLE_t y_ik = 0.0 */ __pyx_v_p = 0; /* "sklearn/_tree.pyx":898 * cdef SIZE_t i = 0 * cdef SIZE_t p = 0 * cdef SIZE_t k = 0 # <<<<<<<<<<<<<< * cdef DOUBLE_t y_ik = 0.0 * cdef DOUBLE_t w_y_ik = 0.0 */ __pyx_v_k = 0; /* "sklearn/_tree.pyx":899 * cdef SIZE_t p = 0 * cdef SIZE_t k = 0 * cdef DOUBLE_t y_ik = 0.0 # <<<<<<<<<<<<<< * cdef DOUBLE_t w_y_ik = 0.0 * cdef DOUBLE_t w = 1.0 */ __pyx_v_y_ik = 0.0; /* "sklearn/_tree.pyx":900 * cdef SIZE_t k = 0 * cdef DOUBLE_t y_ik = 0.0 * cdef DOUBLE_t w_y_ik = 0.0 # <<<<<<<<<<<<<< * cdef DOUBLE_t w = 1.0 * */ __pyx_v_w_y_ik = 0.0; /* "sklearn/_tree.pyx":901 * cdef DOUBLE_t y_ik = 0.0 * cdef DOUBLE_t w_y_ik = 0.0 * cdef DOUBLE_t w = 1.0 # <<<<<<<<<<<<<< * * cdef SIZE_t n_bytes = n_outputs * sizeof(double) */ __pyx_v_w = 1.0; /* "sklearn/_tree.pyx":903 * cdef DOUBLE_t w = 1.0 * * cdef SIZE_t n_bytes = n_outputs * sizeof(double) # <<<<<<<<<<<<<< * memset(mean_left, 0, n_bytes) * memset(mean_right, 0, n_bytes) */ __pyx_v_n_bytes = (__pyx_v_n_outputs * (sizeof(double))); /* "sklearn/_tree.pyx":904 * * cdef SIZE_t n_bytes = n_outputs * sizeof(double) * memset(mean_left, 0, n_bytes) # <<<<<<<<<<<<<< * memset(mean_right, 0, n_bytes) * memset(mean_total, 0, n_bytes) */ memset(__pyx_v_mean_left, 0, __pyx_v_n_bytes); /* "sklearn/_tree.pyx":905 * cdef SIZE_t n_bytes = n_outputs * sizeof(double) * memset(mean_left, 0, n_bytes) * memset(mean_right, 0, n_bytes) # <<<<<<<<<<<<<< * memset(mean_total, 0, n_bytes) * memset(sq_sum_left, 0, n_bytes) */ memset(__pyx_v_mean_right, 0, __pyx_v_n_bytes); /* "sklearn/_tree.pyx":906 * memset(mean_left, 0, n_bytes) * memset(mean_right, 0, n_bytes) * memset(mean_total, 0, n_bytes) # <<<<<<<<<<<<<< * memset(sq_sum_left, 0, n_bytes) * memset(sq_sum_right, 0, n_bytes) */ memset(__pyx_v_mean_total, 0, __pyx_v_n_bytes); /* "sklearn/_tree.pyx":907 * memset(mean_right, 0, n_bytes) * memset(mean_total, 0, n_bytes) * memset(sq_sum_left, 0, n_bytes) # <<<<<<<<<<<<<< * memset(sq_sum_right, 0, n_bytes) * memset(sq_sum_total, 0, n_bytes) */ memset(__pyx_v_sq_sum_left, 0, __pyx_v_n_bytes); /* "sklearn/_tree.pyx":908 * memset(mean_total, 0, n_bytes) * memset(sq_sum_left, 0, n_bytes) * memset(sq_sum_right, 0, n_bytes) # <<<<<<<<<<<<<< * memset(sq_sum_total, 0, n_bytes) * memset(var_left, 0, n_bytes) */ memset(__pyx_v_sq_sum_right, 0, __pyx_v_n_bytes); /* "sklearn/_tree.pyx":909 * memset(sq_sum_left, 0, n_bytes) * memset(sq_sum_right, 0, n_bytes) * memset(sq_sum_total, 0, n_bytes) # <<<<<<<<<<<<<< * memset(var_left, 0, n_bytes) * memset(var_right, 0, n_bytes) */ memset(__pyx_v_sq_sum_total, 0, __pyx_v_n_bytes); /* "sklearn/_tree.pyx":910 * memset(sq_sum_right, 0, n_bytes) * memset(sq_sum_total, 0, n_bytes) * memset(var_left, 0, n_bytes) # <<<<<<<<<<<<<< * memset(var_right, 0, n_bytes) * memset(sum_left, 0, n_bytes) */ memset(__pyx_v_var_left, 0, __pyx_v_n_bytes); /* "sklearn/_tree.pyx":911 * memset(sq_sum_total, 0, n_bytes) * memset(var_left, 0, n_bytes) * memset(var_right, 0, n_bytes) # <<<<<<<<<<<<<< * memset(sum_left, 0, n_bytes) * memset(sum_right, 0, n_bytes) */ memset(__pyx_v_var_right, 0, __pyx_v_n_bytes); /* "sklearn/_tree.pyx":912 * memset(var_left, 0, n_bytes) * memset(var_right, 0, n_bytes) * memset(sum_left, 0, n_bytes) # <<<<<<<<<<<<<< * memset(sum_right, 0, n_bytes) * memset(sum_total, 0, n_bytes) */ memset(__pyx_v_sum_left, 0, __pyx_v_n_bytes); /* "sklearn/_tree.pyx":913 * memset(var_right, 0, n_bytes) * memset(sum_left, 0, n_bytes) * memset(sum_right, 0, n_bytes) # <<<<<<<<<<<<<< * memset(sum_total, 0, n_bytes) * */ memset(__pyx_v_sum_right, 0, __pyx_v_n_bytes); /* "sklearn/_tree.pyx":914 * memset(sum_left, 0, n_bytes) * memset(sum_right, 0, n_bytes) * memset(sum_total, 0, n_bytes) # <<<<<<<<<<<<<< * * for p in range(start, end): */ memset(__pyx_v_sum_total, 0, __pyx_v_n_bytes); /* "sklearn/_tree.pyx":916 * memset(sum_total, 0, n_bytes) * * for p in range(start, end): # <<<<<<<<<<<<<< * i = samples[p] * */ __pyx_t_1 = __pyx_v_end; for (__pyx_t_3 = __pyx_v_start; __pyx_t_3 < __pyx_t_1; __pyx_t_3+=1) { __pyx_v_p = __pyx_t_3; /* "sklearn/_tree.pyx":917 * * for p in range(start, end): * i = samples[p] # <<<<<<<<<<<<<< * * if sample_weight != NULL: */ __pyx_v_i = (__pyx_v_samples[__pyx_v_p]); /* "sklearn/_tree.pyx":919 * i = samples[p] * * if sample_weight != NULL: # <<<<<<<<<<<<<< * w = sample_weight[i] * */ __pyx_t_4 = ((__pyx_v_sample_weight != NULL) != 0); if (__pyx_t_4) { /* "sklearn/_tree.pyx":920 * * if sample_weight != NULL: * w = sample_weight[i] # <<<<<<<<<<<<<< * * for k in range(n_outputs): */ __pyx_v_w = (__pyx_v_sample_weight[__pyx_v_i]); goto __pyx_L5; } __pyx_L5:; /* "sklearn/_tree.pyx":922 * w = sample_weight[i] * * for k in range(n_outputs): # <<<<<<<<<<<<<< * y_ik = y[i * y_stride + k] * w_y_ik = w * y_ik */ __pyx_t_5 = __pyx_v_n_outputs; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_k = __pyx_t_6; /* "sklearn/_tree.pyx":923 * * for k in range(n_outputs): * y_ik = y[i * y_stride + k] # <<<<<<<<<<<<<< * w_y_ik = w * y_ik * sum_total[k] += w_y_ik */ __pyx_v_y_ik = (__pyx_v_y[((__pyx_v_i * __pyx_v_y_stride) + __pyx_v_k)]); /* "sklearn/_tree.pyx":924 * for k in range(n_outputs): * y_ik = y[i * y_stride + k] * w_y_ik = w * y_ik # <<<<<<<<<<<<<< * sum_total[k] += w_y_ik * sq_sum_total[k] += w_y_ik * y_ik */ __pyx_v_w_y_ik = (__pyx_v_w * __pyx_v_y_ik); /* "sklearn/_tree.pyx":925 * y_ik = y[i * y_stride + k] * w_y_ik = w * y_ik * sum_total[k] += w_y_ik # <<<<<<<<<<<<<< * sq_sum_total[k] += w_y_ik * y_ik * */ __pyx_t_7 = __pyx_v_k; (__pyx_v_sum_total[__pyx_t_7]) = ((__pyx_v_sum_total[__pyx_t_7]) + __pyx_v_w_y_ik); /* "sklearn/_tree.pyx":926 * w_y_ik = w * y_ik * sum_total[k] += w_y_ik * sq_sum_total[k] += w_y_ik * y_ik # <<<<<<<<<<<<<< * * weighted_n_node_samples += w */ __pyx_t_7 = __pyx_v_k; (__pyx_v_sq_sum_total[__pyx_t_7]) = ((__pyx_v_sq_sum_total[__pyx_t_7]) + (__pyx_v_w_y_ik * __pyx_v_y_ik)); } /* "sklearn/_tree.pyx":928 * sq_sum_total[k] += w_y_ik * y_ik * * weighted_n_node_samples += w # <<<<<<<<<<<<<< * * self.weighted_n_node_samples = weighted_n_node_samples */ __pyx_v_weighted_n_node_samples = (__pyx_v_weighted_n_node_samples + __pyx_v_w); } /* "sklearn/_tree.pyx":930 * weighted_n_node_samples += w * * self.weighted_n_node_samples = weighted_n_node_samples # <<<<<<<<<<<<<< * * for k in range(n_outputs): */ __pyx_v_self->__pyx_base.weighted_n_node_samples = __pyx_v_weighted_n_node_samples; /* "sklearn/_tree.pyx":932 * self.weighted_n_node_samples = weighted_n_node_samples * * for k in range(n_outputs): # <<<<<<<<<<<<<< * mean_total[k] = sum_total[k] / weighted_n_node_samples * */ __pyx_t_1 = __pyx_v_n_outputs; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_1; __pyx_t_3+=1) { __pyx_v_k = __pyx_t_3; /* "sklearn/_tree.pyx":933 * * for k in range(n_outputs): * mean_total[k] = sum_total[k] / weighted_n_node_samples # <<<<<<<<<<<<<< * * # Reset to pos=start */ (__pyx_v_mean_total[__pyx_v_k]) = ((__pyx_v_sum_total[__pyx_v_k]) / __pyx_v_weighted_n_node_samples); } /* "sklearn/_tree.pyx":936 * * # Reset to pos=start * self.reset() # <<<<<<<<<<<<<< * * cdef void reset(self) nogil: */ ((struct __pyx_vtabstruct_7sklearn_5_tree_RegressionCriterion *)__pyx_v_self->__pyx_base.__pyx_vtab)->__pyx_base.reset(((struct __pyx_obj_7sklearn_5_tree_Criterion *)__pyx_v_self)); /* "sklearn/_tree.pyx":866 * pass * * cdef void init(self, DOUBLE_t* y, SIZE_t y_stride, DOUBLE_t* sample_weight, # <<<<<<<<<<<<<< * double weighted_n_samples, SIZE_t* samples, SIZE_t start, * SIZE_t end) nogil: */ /* function exit code */ } /* "sklearn/_tree.pyx":938 * self.reset() * * cdef void reset(self) nogil: # <<<<<<<<<<<<<< * """Reset the criterion at pos=start.""" * self.pos = self.start */ static void __pyx_f_7sklearn_5_tree_19RegressionCriterion_reset(struct __pyx_obj_7sklearn_5_tree_RegressionCriterion *__pyx_v_self) { __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_outputs; double *__pyx_v_mean_left; double *__pyx_v_mean_right; double *__pyx_v_mean_total; double *__pyx_v_sq_sum_left; double *__pyx_v_sq_sum_right; double *__pyx_v_sq_sum_total; double *__pyx_v_var_left; double *__pyx_v_var_right; double __pyx_v_weighted_n_node_samples; double *__pyx_v_sum_left; double *__pyx_v_sum_right; double *__pyx_v_sum_total; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_k; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_1; double __pyx_t_2; double *__pyx_t_3; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_4; /* "sklearn/_tree.pyx":940 * cdef void reset(self) nogil: * """Reset the criterion at pos=start.""" * self.pos = self.start # <<<<<<<<<<<<<< * * self.weighted_n_left = 0.0 */ __pyx_t_1 = __pyx_v_self->__pyx_base.start; __pyx_v_self->__pyx_base.pos = __pyx_t_1; /* "sklearn/_tree.pyx":942 * self.pos = self.start * * self.weighted_n_left = 0.0 # <<<<<<<<<<<<<< * self.weighted_n_right = self.weighted_n_node_samples * */ __pyx_v_self->__pyx_base.weighted_n_left = 0.0; /* "sklearn/_tree.pyx":943 * * self.weighted_n_left = 0.0 * self.weighted_n_right = self.weighted_n_node_samples # <<<<<<<<<<<<<< * * cdef SIZE_t n_outputs = self.n_outputs */ __pyx_t_2 = __pyx_v_self->__pyx_base.weighted_n_node_samples; __pyx_v_self->__pyx_base.weighted_n_right = __pyx_t_2; /* "sklearn/_tree.pyx":945 * self.weighted_n_right = self.weighted_n_node_samples * * cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<< * cdef double* mean_left = self.mean_left * cdef double* mean_right = self.mean_right */ __pyx_t_1 = __pyx_v_self->__pyx_base.n_outputs; __pyx_v_n_outputs = __pyx_t_1; /* "sklearn/_tree.pyx":946 * * cdef SIZE_t n_outputs = self.n_outputs * cdef double* mean_left = self.mean_left # <<<<<<<<<<<<<< * cdef double* mean_right = self.mean_right * cdef double* mean_total = self.mean_total */ __pyx_t_3 = __pyx_v_self->mean_left; __pyx_v_mean_left = __pyx_t_3; /* "sklearn/_tree.pyx":947 * cdef SIZE_t n_outputs = self.n_outputs * cdef double* mean_left = self.mean_left * cdef double* mean_right = self.mean_right # <<<<<<<<<<<<<< * cdef double* mean_total = self.mean_total * cdef double* sq_sum_left = self.sq_sum_left */ __pyx_t_3 = __pyx_v_self->mean_right; __pyx_v_mean_right = __pyx_t_3; /* "sklearn/_tree.pyx":948 * cdef double* mean_left = self.mean_left * cdef double* mean_right = self.mean_right * cdef double* mean_total = self.mean_total # <<<<<<<<<<<<<< * cdef double* sq_sum_left = self.sq_sum_left * cdef double* sq_sum_right = self.sq_sum_right */ __pyx_t_3 = __pyx_v_self->mean_total; __pyx_v_mean_total = __pyx_t_3; /* "sklearn/_tree.pyx":949 * cdef double* mean_right = self.mean_right * cdef double* mean_total = self.mean_total * cdef double* sq_sum_left = self.sq_sum_left # <<<<<<<<<<<<<< * cdef double* sq_sum_right = self.sq_sum_right * cdef double* sq_sum_total = self.sq_sum_total */ __pyx_t_3 = __pyx_v_self->sq_sum_left; __pyx_v_sq_sum_left = __pyx_t_3; /* "sklearn/_tree.pyx":950 * cdef double* mean_total = self.mean_total * cdef double* sq_sum_left = self.sq_sum_left * cdef double* sq_sum_right = self.sq_sum_right # <<<<<<<<<<<<<< * cdef double* sq_sum_total = self.sq_sum_total * cdef double* var_left = self.var_left */ __pyx_t_3 = __pyx_v_self->sq_sum_right; __pyx_v_sq_sum_right = __pyx_t_3; /* "sklearn/_tree.pyx":951 * cdef double* sq_sum_left = self.sq_sum_left * cdef double* sq_sum_right = self.sq_sum_right * cdef double* sq_sum_total = self.sq_sum_total # <<<<<<<<<<<<<< * cdef double* var_left = self.var_left * cdef double* var_right = self.var_right */ __pyx_t_3 = __pyx_v_self->sq_sum_total; __pyx_v_sq_sum_total = __pyx_t_3; /* "sklearn/_tree.pyx":952 * cdef double* sq_sum_right = self.sq_sum_right * cdef double* sq_sum_total = self.sq_sum_total * cdef double* var_left = self.var_left # <<<<<<<<<<<<<< * cdef double* var_right = self.var_right * cdef double weighted_n_node_samples = self.weighted_n_node_samples */ __pyx_t_3 = __pyx_v_self->var_left; __pyx_v_var_left = __pyx_t_3; /* "sklearn/_tree.pyx":953 * cdef double* sq_sum_total = self.sq_sum_total * cdef double* var_left = self.var_left * cdef double* var_right = self.var_right # <<<<<<<<<<<<<< * cdef double weighted_n_node_samples = self.weighted_n_node_samples * cdef double* sum_left = self.sum_left */ __pyx_t_3 = __pyx_v_self->var_right; __pyx_v_var_right = __pyx_t_3; /* "sklearn/_tree.pyx":954 * cdef double* var_left = self.var_left * cdef double* var_right = self.var_right * cdef double weighted_n_node_samples = self.weighted_n_node_samples # <<<<<<<<<<<<<< * cdef double* sum_left = self.sum_left * cdef double* sum_right = self.sum_right */ __pyx_t_2 = __pyx_v_self->__pyx_base.weighted_n_node_samples; __pyx_v_weighted_n_node_samples = __pyx_t_2; /* "sklearn/_tree.pyx":955 * cdef double* var_right = self.var_right * cdef double weighted_n_node_samples = self.weighted_n_node_samples * cdef double* sum_left = self.sum_left # <<<<<<<<<<<<<< * cdef double* sum_right = self.sum_right * cdef double* sum_total = self.sum_total */ __pyx_t_3 = __pyx_v_self->sum_left; __pyx_v_sum_left = __pyx_t_3; /* "sklearn/_tree.pyx":956 * cdef double weighted_n_node_samples = self.weighted_n_node_samples * cdef double* sum_left = self.sum_left * cdef double* sum_right = self.sum_right # <<<<<<<<<<<<<< * cdef double* sum_total = self.sum_total * */ __pyx_t_3 = __pyx_v_self->sum_right; __pyx_v_sum_right = __pyx_t_3; /* "sklearn/_tree.pyx":957 * cdef double* sum_left = self.sum_left * cdef double* sum_right = self.sum_right * cdef double* sum_total = self.sum_total # <<<<<<<<<<<<<< * * cdef SIZE_t k = 0 */ __pyx_t_3 = __pyx_v_self->sum_total; __pyx_v_sum_total = __pyx_t_3; /* "sklearn/_tree.pyx":959 * cdef double* sum_total = self.sum_total * * cdef SIZE_t k = 0 # <<<<<<<<<<<<<< * * for k in range(n_outputs): */ __pyx_v_k = 0; /* "sklearn/_tree.pyx":961 * cdef SIZE_t k = 0 * * for k in range(n_outputs): # <<<<<<<<<<<<<< * mean_right[k] = mean_total[k] * mean_left[k] = 0.0 */ __pyx_t_1 = __pyx_v_n_outputs; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_1; __pyx_t_4+=1) { __pyx_v_k = __pyx_t_4; /* "sklearn/_tree.pyx":962 * * for k in range(n_outputs): * mean_right[k] = mean_total[k] # <<<<<<<<<<<<<< * mean_left[k] = 0.0 * sq_sum_right[k] = sq_sum_total[k] */ (__pyx_v_mean_right[__pyx_v_k]) = (__pyx_v_mean_total[__pyx_v_k]); /* "sklearn/_tree.pyx":963 * for k in range(n_outputs): * mean_right[k] = mean_total[k] * mean_left[k] = 0.0 # <<<<<<<<<<<<<< * sq_sum_right[k] = sq_sum_total[k] * sq_sum_left[k] = 0.0 */ (__pyx_v_mean_left[__pyx_v_k]) = 0.0; /* "sklearn/_tree.pyx":964 * mean_right[k] = mean_total[k] * mean_left[k] = 0.0 * sq_sum_right[k] = sq_sum_total[k] # <<<<<<<<<<<<<< * sq_sum_left[k] = 0.0 * var_right[k] = (sq_sum_right[k] / weighted_n_node_samples - */ (__pyx_v_sq_sum_right[__pyx_v_k]) = (__pyx_v_sq_sum_total[__pyx_v_k]); /* "sklearn/_tree.pyx":965 * mean_left[k] = 0.0 * sq_sum_right[k] = sq_sum_total[k] * sq_sum_left[k] = 0.0 # <<<<<<<<<<<<<< * var_right[k] = (sq_sum_right[k] / weighted_n_node_samples - * mean_right[k] * mean_right[k]) */ (__pyx_v_sq_sum_left[__pyx_v_k]) = 0.0; /* "sklearn/_tree.pyx":966 * sq_sum_right[k] = sq_sum_total[k] * sq_sum_left[k] = 0.0 * var_right[k] = (sq_sum_right[k] / weighted_n_node_samples - # <<<<<<<<<<<<<< * mean_right[k] * mean_right[k]) * var_left[k] = 0.0 */ (__pyx_v_var_right[__pyx_v_k]) = (((__pyx_v_sq_sum_right[__pyx_v_k]) / __pyx_v_weighted_n_node_samples) - ((__pyx_v_mean_right[__pyx_v_k]) * (__pyx_v_mean_right[__pyx_v_k]))); /* "sklearn/_tree.pyx":968 * var_right[k] = (sq_sum_right[k] / weighted_n_node_samples - * mean_right[k] * mean_right[k]) * var_left[k] = 0.0 # <<<<<<<<<<<<<< * sum_right[k] = sum_total[k] * sum_left[k] = 0.0 */ (__pyx_v_var_left[__pyx_v_k]) = 0.0; /* "sklearn/_tree.pyx":969 * mean_right[k] * mean_right[k]) * var_left[k] = 0.0 * sum_right[k] = sum_total[k] # <<<<<<<<<<<<<< * sum_left[k] = 0.0 * */ (__pyx_v_sum_right[__pyx_v_k]) = (__pyx_v_sum_total[__pyx_v_k]); /* "sklearn/_tree.pyx":970 * var_left[k] = 0.0 * sum_right[k] = sum_total[k] * sum_left[k] = 0.0 # <<<<<<<<<<<<<< * * cdef void update(self, SIZE_t new_pos) nogil: */ (__pyx_v_sum_left[__pyx_v_k]) = 0.0; } /* "sklearn/_tree.pyx":938 * self.reset() * * cdef void reset(self) nogil: # <<<<<<<<<<<<<< * """Reset the criterion at pos=start.""" * self.pos = self.start */ /* function exit code */ } /* "sklearn/_tree.pyx":972 * sum_left[k] = 0.0 * * cdef void update(self, SIZE_t new_pos) nogil: # <<<<<<<<<<<<<< * """Update the collected statistics by moving samples[pos:new_pos] from * the right child to the left child.""" */ static void __pyx_f_7sklearn_5_tree_19RegressionCriterion_update(struct __pyx_obj_7sklearn_5_tree_RegressionCriterion *__pyx_v_self, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_new_pos) { __pyx_t_7sklearn_5_tree_DOUBLE_t *__pyx_v_y; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_y_stride; __pyx_t_7sklearn_5_tree_DOUBLE_t *__pyx_v_sample_weight; __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_samples; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_pos; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_outputs; double *__pyx_v_mean_left; double *__pyx_v_mean_right; double *__pyx_v_sq_sum_left; double *__pyx_v_sq_sum_right; double *__pyx_v_var_left; double *__pyx_v_var_right; double *__pyx_v_sum_left; double *__pyx_v_sum_right; double __pyx_v_weighted_n_left; double __pyx_v_weighted_n_right; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_i; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_p; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_k; __pyx_t_7sklearn_5_tree_DOUBLE_t __pyx_v_w; __pyx_t_7sklearn_5_tree_DOUBLE_t __pyx_v_diff_w; __pyx_t_7sklearn_5_tree_DOUBLE_t __pyx_v_y_ik; __pyx_t_7sklearn_5_tree_DOUBLE_t __pyx_v_w_y_ik; __pyx_t_7sklearn_5_tree_DOUBLE_t *__pyx_t_1; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_2; __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_t_3; double *__pyx_t_4; double __pyx_t_5; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_6; int __pyx_t_7; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_8; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_9; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_10; /* "sklearn/_tree.pyx":975 * """Update the collected statistics by moving samples[pos:new_pos] from * the right child to the left child.""" * cdef DOUBLE_t* y = self.y # <<<<<<<<<<<<<< * cdef SIZE_t y_stride = self.y_stride * cdef DOUBLE_t* sample_weight = self.sample_weight */ __pyx_t_1 = __pyx_v_self->__pyx_base.y; __pyx_v_y = __pyx_t_1; /* "sklearn/_tree.pyx":976 * the right child to the left child.""" * cdef DOUBLE_t* y = self.y * cdef SIZE_t y_stride = self.y_stride # <<<<<<<<<<<<<< * cdef DOUBLE_t* sample_weight = self.sample_weight * */ __pyx_t_2 = __pyx_v_self->__pyx_base.y_stride; __pyx_v_y_stride = __pyx_t_2; /* "sklearn/_tree.pyx":977 * cdef DOUBLE_t* y = self.y * cdef SIZE_t y_stride = self.y_stride * cdef DOUBLE_t* sample_weight = self.sample_weight # <<<<<<<<<<<<<< * * cdef SIZE_t* samples = self.samples */ __pyx_t_1 = __pyx_v_self->__pyx_base.sample_weight; __pyx_v_sample_weight = __pyx_t_1; /* "sklearn/_tree.pyx":979 * cdef DOUBLE_t* sample_weight = self.sample_weight * * cdef SIZE_t* samples = self.samples # <<<<<<<<<<<<<< * cdef SIZE_t pos = self.pos * */ __pyx_t_3 = __pyx_v_self->__pyx_base.samples; __pyx_v_samples = __pyx_t_3; /* "sklearn/_tree.pyx":980 * * cdef SIZE_t* samples = self.samples * cdef SIZE_t pos = self.pos # <<<<<<<<<<<<<< * * cdef SIZE_t n_outputs = self.n_outputs */ __pyx_t_2 = __pyx_v_self->__pyx_base.pos; __pyx_v_pos = __pyx_t_2; /* "sklearn/_tree.pyx":982 * cdef SIZE_t pos = self.pos * * cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<< * cdef double* mean_left = self.mean_left * cdef double* mean_right = self.mean_right */ __pyx_t_2 = __pyx_v_self->__pyx_base.n_outputs; __pyx_v_n_outputs = __pyx_t_2; /* "sklearn/_tree.pyx":983 * * cdef SIZE_t n_outputs = self.n_outputs * cdef double* mean_left = self.mean_left # <<<<<<<<<<<<<< * cdef double* mean_right = self.mean_right * cdef double* sq_sum_left = self.sq_sum_left */ __pyx_t_4 = __pyx_v_self->mean_left; __pyx_v_mean_left = __pyx_t_4; /* "sklearn/_tree.pyx":984 * cdef SIZE_t n_outputs = self.n_outputs * cdef double* mean_left = self.mean_left * cdef double* mean_right = self.mean_right # <<<<<<<<<<<<<< * cdef double* sq_sum_left = self.sq_sum_left * cdef double* sq_sum_right = self.sq_sum_right */ __pyx_t_4 = __pyx_v_self->mean_right; __pyx_v_mean_right = __pyx_t_4; /* "sklearn/_tree.pyx":985 * cdef double* mean_left = self.mean_left * cdef double* mean_right = self.mean_right * cdef double* sq_sum_left = self.sq_sum_left # <<<<<<<<<<<<<< * cdef double* sq_sum_right = self.sq_sum_right * cdef double* var_left = self.var_left */ __pyx_t_4 = __pyx_v_self->sq_sum_left; __pyx_v_sq_sum_left = __pyx_t_4; /* "sklearn/_tree.pyx":986 * cdef double* mean_right = self.mean_right * cdef double* sq_sum_left = self.sq_sum_left * cdef double* sq_sum_right = self.sq_sum_right # <<<<<<<<<<<<<< * cdef double* var_left = self.var_left * cdef double* var_right = self.var_right */ __pyx_t_4 = __pyx_v_self->sq_sum_right; __pyx_v_sq_sum_right = __pyx_t_4; /* "sklearn/_tree.pyx":987 * cdef double* sq_sum_left = self.sq_sum_left * cdef double* sq_sum_right = self.sq_sum_right * cdef double* var_left = self.var_left # <<<<<<<<<<<<<< * cdef double* var_right = self.var_right * cdef double* sum_left = self.sum_left */ __pyx_t_4 = __pyx_v_self->var_left; __pyx_v_var_left = __pyx_t_4; /* "sklearn/_tree.pyx":988 * cdef double* sq_sum_right = self.sq_sum_right * cdef double* var_left = self.var_left * cdef double* var_right = self.var_right # <<<<<<<<<<<<<< * cdef double* sum_left = self.sum_left * cdef double* sum_right = self.sum_right */ __pyx_t_4 = __pyx_v_self->var_right; __pyx_v_var_right = __pyx_t_4; /* "sklearn/_tree.pyx":989 * cdef double* var_left = self.var_left * cdef double* var_right = self.var_right * cdef double* sum_left = self.sum_left # <<<<<<<<<<<<<< * cdef double* sum_right = self.sum_right * */ __pyx_t_4 = __pyx_v_self->sum_left; __pyx_v_sum_left = __pyx_t_4; /* "sklearn/_tree.pyx":990 * cdef double* var_right = self.var_right * cdef double* sum_left = self.sum_left * cdef double* sum_right = self.sum_right # <<<<<<<<<<<<<< * * cdef double weighted_n_left = self.weighted_n_left */ __pyx_t_4 = __pyx_v_self->sum_right; __pyx_v_sum_right = __pyx_t_4; /* "sklearn/_tree.pyx":992 * cdef double* sum_right = self.sum_right * * cdef double weighted_n_left = self.weighted_n_left # <<<<<<<<<<<<<< * cdef double weighted_n_right = self.weighted_n_right * */ __pyx_t_5 = __pyx_v_self->__pyx_base.weighted_n_left; __pyx_v_weighted_n_left = __pyx_t_5; /* "sklearn/_tree.pyx":993 * * cdef double weighted_n_left = self.weighted_n_left * cdef double weighted_n_right = self.weighted_n_right # <<<<<<<<<<<<<< * * cdef SIZE_t i */ __pyx_t_5 = __pyx_v_self->__pyx_base.weighted_n_right; __pyx_v_weighted_n_right = __pyx_t_5; /* "sklearn/_tree.pyx":998 * cdef SIZE_t p * cdef SIZE_t k * cdef DOUBLE_t w = 1.0 # <<<<<<<<<<<<<< * cdef DOUBLE_t diff_w = 0.0 * cdef DOUBLE_t y_ik, w_y_ik */ __pyx_v_w = 1.0; /* "sklearn/_tree.pyx":999 * cdef SIZE_t k * cdef DOUBLE_t w = 1.0 * cdef DOUBLE_t diff_w = 0.0 # <<<<<<<<<<<<<< * cdef DOUBLE_t y_ik, w_y_ik * */ __pyx_v_diff_w = 0.0; /* "sklearn/_tree.pyx":1003 * * # Note: We assume start <= pos < new_pos <= end * for p in range(pos, new_pos): # <<<<<<<<<<<<<< * i = samples[p] * */ __pyx_t_2 = __pyx_v_new_pos; for (__pyx_t_6 = __pyx_v_pos; __pyx_t_6 < __pyx_t_2; __pyx_t_6+=1) { __pyx_v_p = __pyx_t_6; /* "sklearn/_tree.pyx":1004 * # Note: We assume start <= pos < new_pos <= end * for p in range(pos, new_pos): * i = samples[p] # <<<<<<<<<<<<<< * * if sample_weight != NULL: */ __pyx_v_i = (__pyx_v_samples[__pyx_v_p]); /* "sklearn/_tree.pyx":1006 * i = samples[p] * * if sample_weight != NULL: # <<<<<<<<<<<<<< * w = sample_weight[i] * */ __pyx_t_7 = ((__pyx_v_sample_weight != NULL) != 0); if (__pyx_t_7) { /* "sklearn/_tree.pyx":1007 * * if sample_weight != NULL: * w = sample_weight[i] # <<<<<<<<<<<<<< * * for k in range(n_outputs): */ __pyx_v_w = (__pyx_v_sample_weight[__pyx_v_i]); goto __pyx_L5; } __pyx_L5:; /* "sklearn/_tree.pyx":1009 * w = sample_weight[i] * * for k in range(n_outputs): # <<<<<<<<<<<<<< * y_ik = y[i * y_stride + k] * w_y_ik = w * y_ik */ __pyx_t_8 = __pyx_v_n_outputs; for (__pyx_t_9 = 0; __pyx_t_9 < __pyx_t_8; __pyx_t_9+=1) { __pyx_v_k = __pyx_t_9; /* "sklearn/_tree.pyx":1010 * * for k in range(n_outputs): * y_ik = y[i * y_stride + k] # <<<<<<<<<<<<<< * w_y_ik = w * y_ik * */ __pyx_v_y_ik = (__pyx_v_y[((__pyx_v_i * __pyx_v_y_stride) + __pyx_v_k)]); /* "sklearn/_tree.pyx":1011 * for k in range(n_outputs): * y_ik = y[i * y_stride + k] * w_y_ik = w * y_ik # <<<<<<<<<<<<<< * * sum_left[k] += w_y_ik */ __pyx_v_w_y_ik = (__pyx_v_w * __pyx_v_y_ik); /* "sklearn/_tree.pyx":1013 * w_y_ik = w * y_ik * * sum_left[k] += w_y_ik # <<<<<<<<<<<<<< * sum_right[k] -= w_y_ik * */ __pyx_t_10 = __pyx_v_k; (__pyx_v_sum_left[__pyx_t_10]) = ((__pyx_v_sum_left[__pyx_t_10]) + __pyx_v_w_y_ik); /* "sklearn/_tree.pyx":1014 * * sum_left[k] += w_y_ik * sum_right[k] -= w_y_ik # <<<<<<<<<<<<<< * * sq_sum_left[k] += w_y_ik * y_ik */ __pyx_t_10 = __pyx_v_k; (__pyx_v_sum_right[__pyx_t_10]) = ((__pyx_v_sum_right[__pyx_t_10]) - __pyx_v_w_y_ik); /* "sklearn/_tree.pyx":1016 * sum_right[k] -= w_y_ik * * sq_sum_left[k] += w_y_ik * y_ik # <<<<<<<<<<<<<< * sq_sum_right[k] -= w_y_ik * y_ik * */ __pyx_t_10 = __pyx_v_k; (__pyx_v_sq_sum_left[__pyx_t_10]) = ((__pyx_v_sq_sum_left[__pyx_t_10]) + (__pyx_v_w_y_ik * __pyx_v_y_ik)); /* "sklearn/_tree.pyx":1017 * * sq_sum_left[k] += w_y_ik * y_ik * sq_sum_right[k] -= w_y_ik * y_ik # <<<<<<<<<<<<<< * * diff_w += w */ __pyx_t_10 = __pyx_v_k; (__pyx_v_sq_sum_right[__pyx_t_10]) = ((__pyx_v_sq_sum_right[__pyx_t_10]) - (__pyx_v_w_y_ik * __pyx_v_y_ik)); } /* "sklearn/_tree.pyx":1019 * sq_sum_right[k] -= w_y_ik * y_ik * * diff_w += w # <<<<<<<<<<<<<< * * weighted_n_left += diff_w */ __pyx_v_diff_w = (__pyx_v_diff_w + __pyx_v_w); } /* "sklearn/_tree.pyx":1021 * diff_w += w * * weighted_n_left += diff_w # <<<<<<<<<<<<<< * weighted_n_right -= diff_w * */ __pyx_v_weighted_n_left = (__pyx_v_weighted_n_left + __pyx_v_diff_w); /* "sklearn/_tree.pyx":1022 * * weighted_n_left += diff_w * weighted_n_right -= diff_w # <<<<<<<<<<<<<< * * for k in range(n_outputs): */ __pyx_v_weighted_n_right = (__pyx_v_weighted_n_right - __pyx_v_diff_w); /* "sklearn/_tree.pyx":1024 * weighted_n_right -= diff_w * * for k in range(n_outputs): # <<<<<<<<<<<<<< * mean_left[k] = sum_left[k] / weighted_n_left * mean_right[k] = sum_right[k] / weighted_n_right */ __pyx_t_2 = __pyx_v_n_outputs; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_2; __pyx_t_6+=1) { __pyx_v_k = __pyx_t_6; /* "sklearn/_tree.pyx":1025 * * for k in range(n_outputs): * mean_left[k] = sum_left[k] / weighted_n_left # <<<<<<<<<<<<<< * mean_right[k] = sum_right[k] / weighted_n_right * var_left[k] = (sq_sum_left[k] / weighted_n_left - */ (__pyx_v_mean_left[__pyx_v_k]) = ((__pyx_v_sum_left[__pyx_v_k]) / __pyx_v_weighted_n_left); /* "sklearn/_tree.pyx":1026 * for k in range(n_outputs): * mean_left[k] = sum_left[k] / weighted_n_left * mean_right[k] = sum_right[k] / weighted_n_right # <<<<<<<<<<<<<< * var_left[k] = (sq_sum_left[k] / weighted_n_left - * mean_left[k] * mean_left[k]) */ (__pyx_v_mean_right[__pyx_v_k]) = ((__pyx_v_sum_right[__pyx_v_k]) / __pyx_v_weighted_n_right); /* "sklearn/_tree.pyx":1027 * mean_left[k] = sum_left[k] / weighted_n_left * mean_right[k] = sum_right[k] / weighted_n_right * var_left[k] = (sq_sum_left[k] / weighted_n_left - # <<<<<<<<<<<<<< * mean_left[k] * mean_left[k]) * var_right[k] = (sq_sum_right[k] / weighted_n_right - */ (__pyx_v_var_left[__pyx_v_k]) = (((__pyx_v_sq_sum_left[__pyx_v_k]) / __pyx_v_weighted_n_left) - ((__pyx_v_mean_left[__pyx_v_k]) * (__pyx_v_mean_left[__pyx_v_k]))); /* "sklearn/_tree.pyx":1029 * var_left[k] = (sq_sum_left[k] / weighted_n_left - * mean_left[k] * mean_left[k]) * var_right[k] = (sq_sum_right[k] / weighted_n_right - # <<<<<<<<<<<<<< * mean_right[k] * mean_right[k]) * */ (__pyx_v_var_right[__pyx_v_k]) = (((__pyx_v_sq_sum_right[__pyx_v_k]) / __pyx_v_weighted_n_right) - ((__pyx_v_mean_right[__pyx_v_k]) * (__pyx_v_mean_right[__pyx_v_k]))); } /* "sklearn/_tree.pyx":1032 * mean_right[k] * mean_right[k]) * * self.weighted_n_left = weighted_n_left # <<<<<<<<<<<<<< * self.weighted_n_right = weighted_n_right * */ __pyx_v_self->__pyx_base.weighted_n_left = __pyx_v_weighted_n_left; /* "sklearn/_tree.pyx":1033 * * self.weighted_n_left = weighted_n_left * self.weighted_n_right = weighted_n_right # <<<<<<<<<<<<<< * * self.pos = new_pos */ __pyx_v_self->__pyx_base.weighted_n_right = __pyx_v_weighted_n_right; /* "sklearn/_tree.pyx":1035 * self.weighted_n_right = weighted_n_right * * self.pos = new_pos # <<<<<<<<<<<<<< * * cdef double node_impurity(self) nogil: */ __pyx_v_self->__pyx_base.pos = __pyx_v_new_pos; /* "sklearn/_tree.pyx":972 * sum_left[k] = 0.0 * * cdef void update(self, SIZE_t new_pos) nogil: # <<<<<<<<<<<<<< * """Update the collected statistics by moving samples[pos:new_pos] from * the right child to the left child.""" */ /* function exit code */ } /* "sklearn/_tree.pyx":1037 * self.pos = new_pos * * cdef double node_impurity(self) nogil: # <<<<<<<<<<<<<< * pass * */ static double __pyx_f_7sklearn_5_tree_19RegressionCriterion_node_impurity(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_RegressionCriterion *__pyx_v_self) { double __pyx_r; /* function exit code */ __pyx_r = 0; return __pyx_r; } /* "sklearn/_tree.pyx":1040 * pass * * cdef void children_impurity(self, double* impurity_left, # <<<<<<<<<<<<<< * double* impurity_right) nogil: * pass */ static void __pyx_f_7sklearn_5_tree_19RegressionCriterion_children_impurity(CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_RegressionCriterion *__pyx_v_self, CYTHON_UNUSED double *__pyx_v_impurity_left, CYTHON_UNUSED double *__pyx_v_impurity_right) { /* function exit code */ } /* "sklearn/_tree.pyx":1044 * pass * * cdef void node_value(self, double* dest) nogil: # <<<<<<<<<<<<<< * """Compute the node value of samples[start:end] into dest.""" * memcpy(dest, self.mean_total, self.n_outputs * sizeof(double)) */ static void __pyx_f_7sklearn_5_tree_19RegressionCriterion_node_value(struct __pyx_obj_7sklearn_5_tree_RegressionCriterion *__pyx_v_self, double *__pyx_v_dest) { /* "sklearn/_tree.pyx":1046 * cdef void node_value(self, double* dest) nogil: * """Compute the node value of samples[start:end] into dest.""" * memcpy(dest, self.mean_total, self.n_outputs * sizeof(double)) # <<<<<<<<<<<<<< * * */ memcpy(__pyx_v_dest, __pyx_v_self->mean_total, (__pyx_v_self->__pyx_base.n_outputs * (sizeof(double)))); /* "sklearn/_tree.pyx":1044 * pass * * cdef void node_value(self, double* dest) nogil: # <<<<<<<<<<<<<< * """Compute the node value of samples[start:end] into dest.""" * memcpy(dest, self.mean_total, self.n_outputs * sizeof(double)) */ /* function exit code */ } /* "sklearn/_tree.pyx":1054 * MSE = var_left + var_right * """ * cdef double node_impurity(self) nogil: # <<<<<<<<<<<<<< * """Evaluate the impurity of the current node, i.e. the impurity of * samples[start:end].""" */ static double __pyx_f_7sklearn_5_tree_3MSE_node_impurity(struct __pyx_obj_7sklearn_5_tree_MSE *__pyx_v_self) { __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_outputs; double *__pyx_v_sq_sum_total; double *__pyx_v_mean_total; double __pyx_v_weighted_n_node_samples; double __pyx_v_total; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_k; double __pyx_r; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_1; double *__pyx_t_2; double __pyx_t_3; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_4; /* "sklearn/_tree.pyx":1057 * """Evaluate the impurity of the current node, i.e. the impurity of * samples[start:end].""" * cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<< * cdef double* sq_sum_total = self.sq_sum_total * cdef double* mean_total = self.mean_total */ __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.n_outputs; __pyx_v_n_outputs = __pyx_t_1; /* "sklearn/_tree.pyx":1058 * samples[start:end].""" * cdef SIZE_t n_outputs = self.n_outputs * cdef double* sq_sum_total = self.sq_sum_total # <<<<<<<<<<<<<< * cdef double* mean_total = self.mean_total * cdef double weighted_n_node_samples = self.weighted_n_node_samples */ __pyx_t_2 = __pyx_v_self->__pyx_base.sq_sum_total; __pyx_v_sq_sum_total = __pyx_t_2; /* "sklearn/_tree.pyx":1059 * cdef SIZE_t n_outputs = self.n_outputs * cdef double* sq_sum_total = self.sq_sum_total * cdef double* mean_total = self.mean_total # <<<<<<<<<<<<<< * cdef double weighted_n_node_samples = self.weighted_n_node_samples * cdef double total = 0.0 */ __pyx_t_2 = __pyx_v_self->__pyx_base.mean_total; __pyx_v_mean_total = __pyx_t_2; /* "sklearn/_tree.pyx":1060 * cdef double* sq_sum_total = self.sq_sum_total * cdef double* mean_total = self.mean_total * cdef double weighted_n_node_samples = self.weighted_n_node_samples # <<<<<<<<<<<<<< * cdef double total = 0.0 * cdef SIZE_t k */ __pyx_t_3 = __pyx_v_self->__pyx_base.__pyx_base.weighted_n_node_samples; __pyx_v_weighted_n_node_samples = __pyx_t_3; /* "sklearn/_tree.pyx":1061 * cdef double* mean_total = self.mean_total * cdef double weighted_n_node_samples = self.weighted_n_node_samples * cdef double total = 0.0 # <<<<<<<<<<<<<< * cdef SIZE_t k * */ __pyx_v_total = 0.0; /* "sklearn/_tree.pyx":1064 * cdef SIZE_t k * * for k in range(n_outputs): # <<<<<<<<<<<<<< * total += (sq_sum_total[k] / weighted_n_node_samples - * mean_total[k] * mean_total[k]) */ __pyx_t_1 = __pyx_v_n_outputs; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_1; __pyx_t_4+=1) { __pyx_v_k = __pyx_t_4; /* "sklearn/_tree.pyx":1065 * * for k in range(n_outputs): * total += (sq_sum_total[k] / weighted_n_node_samples - # <<<<<<<<<<<<<< * mean_total[k] * mean_total[k]) * */ __pyx_v_total = (__pyx_v_total + (((__pyx_v_sq_sum_total[__pyx_v_k]) / __pyx_v_weighted_n_node_samples) - ((__pyx_v_mean_total[__pyx_v_k]) * (__pyx_v_mean_total[__pyx_v_k])))); } /* "sklearn/_tree.pyx":1068 * mean_total[k] * mean_total[k]) * * return total / n_outputs # <<<<<<<<<<<<<< * * cdef void children_impurity(self, double* impurity_left, */ __pyx_r = (__pyx_v_total / __pyx_v_n_outputs); goto __pyx_L0; /* "sklearn/_tree.pyx":1054 * MSE = var_left + var_right * """ * cdef double node_impurity(self) nogil: # <<<<<<<<<<<<<< * """Evaluate the impurity of the current node, i.e. the impurity of * samples[start:end].""" */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "sklearn/_tree.pyx":1070 * return total / n_outputs * * cdef void children_impurity(self, double* impurity_left, # <<<<<<<<<<<<<< * double* impurity_right) nogil: * """Evaluate the impurity in children nodes, i.e. the impurity of the */ static void __pyx_f_7sklearn_5_tree_3MSE_children_impurity(struct __pyx_obj_7sklearn_5_tree_MSE *__pyx_v_self, double *__pyx_v_impurity_left, double *__pyx_v_impurity_right) { __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_outputs; double *__pyx_v_var_left; double *__pyx_v_var_right; double __pyx_v_total_left; double __pyx_v_total_right; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_k; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_1; double *__pyx_t_2; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_3; /* "sklearn/_tree.pyx":1075 * left child (samples[start:pos]) and the impurity the right child * (samples[pos:end]).""" * cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<< * cdef double* var_left = self.var_left * cdef double* var_right = self.var_right */ __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.n_outputs; __pyx_v_n_outputs = __pyx_t_1; /* "sklearn/_tree.pyx":1076 * (samples[pos:end]).""" * cdef SIZE_t n_outputs = self.n_outputs * cdef double* var_left = self.var_left # <<<<<<<<<<<<<< * cdef double* var_right = self.var_right * cdef double total_left = 0.0 */ __pyx_t_2 = __pyx_v_self->__pyx_base.var_left; __pyx_v_var_left = __pyx_t_2; /* "sklearn/_tree.pyx":1077 * cdef SIZE_t n_outputs = self.n_outputs * cdef double* var_left = self.var_left * cdef double* var_right = self.var_right # <<<<<<<<<<<<<< * cdef double total_left = 0.0 * cdef double total_right = 0.0 */ __pyx_t_2 = __pyx_v_self->__pyx_base.var_right; __pyx_v_var_right = __pyx_t_2; /* "sklearn/_tree.pyx":1078 * cdef double* var_left = self.var_left * cdef double* var_right = self.var_right * cdef double total_left = 0.0 # <<<<<<<<<<<<<< * cdef double total_right = 0.0 * cdef SIZE_t k */ __pyx_v_total_left = 0.0; /* "sklearn/_tree.pyx":1079 * cdef double* var_right = self.var_right * cdef double total_left = 0.0 * cdef double total_right = 0.0 # <<<<<<<<<<<<<< * cdef SIZE_t k * */ __pyx_v_total_right = 0.0; /* "sklearn/_tree.pyx":1082 * cdef SIZE_t k * * for k in range(n_outputs): # <<<<<<<<<<<<<< * total_left += var_left[k] * total_right += var_right[k] */ __pyx_t_1 = __pyx_v_n_outputs; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_1; __pyx_t_3+=1) { __pyx_v_k = __pyx_t_3; /* "sklearn/_tree.pyx":1083 * * for k in range(n_outputs): * total_left += var_left[k] # <<<<<<<<<<<<<< * total_right += var_right[k] * */ __pyx_v_total_left = (__pyx_v_total_left + (__pyx_v_var_left[__pyx_v_k])); /* "sklearn/_tree.pyx":1084 * for k in range(n_outputs): * total_left += var_left[k] * total_right += var_right[k] # <<<<<<<<<<<<<< * * impurity_left[0] = total_left / n_outputs */ __pyx_v_total_right = (__pyx_v_total_right + (__pyx_v_var_right[__pyx_v_k])); } /* "sklearn/_tree.pyx":1086 * total_right += var_right[k] * * impurity_left[0] = total_left / n_outputs # <<<<<<<<<<<<<< * impurity_right[0] = total_right / n_outputs * */ (__pyx_v_impurity_left[0]) = (__pyx_v_total_left / __pyx_v_n_outputs); /* "sklearn/_tree.pyx":1087 * * impurity_left[0] = total_left / n_outputs * impurity_right[0] = total_right / n_outputs # <<<<<<<<<<<<<< * * */ (__pyx_v_impurity_right[0]) = (__pyx_v_total_right / __pyx_v_n_outputs); /* "sklearn/_tree.pyx":1070 * return total / n_outputs * * cdef void children_impurity(self, double* impurity_left, # <<<<<<<<<<<<<< * double* impurity_right) nogil: * """Evaluate the impurity in children nodes, i.e. the impurity of the */ /* function exit code */ } /* "sklearn/_tree.pyx":1099 * """ * * cdef double impurity_improvement(self, double impurity) nogil: # <<<<<<<<<<<<<< * cdef SIZE_t n_outputs = self.n_outputs * cdef SIZE_t k */ static double __pyx_f_7sklearn_5_tree_11FriedmanMSE_impurity_improvement(struct __pyx_obj_7sklearn_5_tree_FriedmanMSE *__pyx_v_self, CYTHON_UNUSED double __pyx_v_impurity) { __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_outputs; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_k; double *__pyx_v_sum_left; double *__pyx_v_sum_right; double __pyx_v_total_sum_left; double __pyx_v_total_sum_right; double __pyx_v_weighted_n_left; double __pyx_v_weighted_n_right; double __pyx_v_diff; double __pyx_r; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_1; double *__pyx_t_2; double __pyx_t_3; /* "sklearn/_tree.pyx":1100 * * cdef double impurity_improvement(self, double impurity) nogil: * cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<< * cdef SIZE_t k * cdef double* sum_left = self.sum_left */ __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.__pyx_base.n_outputs; __pyx_v_n_outputs = __pyx_t_1; /* "sklearn/_tree.pyx":1102 * cdef SIZE_t n_outputs = self.n_outputs * cdef SIZE_t k * cdef double* sum_left = self.sum_left # <<<<<<<<<<<<<< * cdef double* sum_right = self.sum_right * cdef double total_sum_left = 0.0 */ __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.sum_left; __pyx_v_sum_left = __pyx_t_2; /* "sklearn/_tree.pyx":1103 * cdef SIZE_t k * cdef double* sum_left = self.sum_left * cdef double* sum_right = self.sum_right # <<<<<<<<<<<<<< * cdef double total_sum_left = 0.0 * cdef double total_sum_right = 0.0 */ __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.sum_right; __pyx_v_sum_right = __pyx_t_2; /* "sklearn/_tree.pyx":1104 * cdef double* sum_left = self.sum_left * cdef double* sum_right = self.sum_right * cdef double total_sum_left = 0.0 # <<<<<<<<<<<<<< * cdef double total_sum_right = 0.0 * cdef double weighted_n_left = self.weighted_n_left */ __pyx_v_total_sum_left = 0.0; /* "sklearn/_tree.pyx":1105 * cdef double* sum_right = self.sum_right * cdef double total_sum_left = 0.0 * cdef double total_sum_right = 0.0 # <<<<<<<<<<<<<< * cdef double weighted_n_left = self.weighted_n_left * cdef double weighted_n_right = self.weighted_n_right */ __pyx_v_total_sum_right = 0.0; /* "sklearn/_tree.pyx":1106 * cdef double total_sum_left = 0.0 * cdef double total_sum_right = 0.0 * cdef double weighted_n_left = self.weighted_n_left # <<<<<<<<<<<<<< * cdef double weighted_n_right = self.weighted_n_right * cdef double diff = 0.0 */ __pyx_t_3 = __pyx_v_self->__pyx_base.__pyx_base.__pyx_base.weighted_n_left; __pyx_v_weighted_n_left = __pyx_t_3; /* "sklearn/_tree.pyx":1107 * cdef double total_sum_right = 0.0 * cdef double weighted_n_left = self.weighted_n_left * cdef double weighted_n_right = self.weighted_n_right # <<<<<<<<<<<<<< * cdef double diff = 0.0 * */ __pyx_t_3 = __pyx_v_self->__pyx_base.__pyx_base.__pyx_base.weighted_n_right; __pyx_v_weighted_n_right = __pyx_t_3; /* "sklearn/_tree.pyx":1108 * cdef double weighted_n_left = self.weighted_n_left * cdef double weighted_n_right = self.weighted_n_right * cdef double diff = 0.0 # <<<<<<<<<<<<<< * * for k from 0 <= k < n_outputs: */ __pyx_v_diff = 0.0; /* "sklearn/_tree.pyx":1110 * cdef double diff = 0.0 * * for k from 0 <= k < n_outputs: # <<<<<<<<<<<<<< * total_sum_left += sum_left[k] * total_sum_right += sum_right[k] */ __pyx_t_1 = __pyx_v_n_outputs; for (__pyx_v_k = 0; __pyx_v_k < __pyx_t_1; __pyx_v_k++) { /* "sklearn/_tree.pyx":1111 * * for k from 0 <= k < n_outputs: * total_sum_left += sum_left[k] # <<<<<<<<<<<<<< * total_sum_right += sum_right[k] * */ __pyx_v_total_sum_left = (__pyx_v_total_sum_left + (__pyx_v_sum_left[__pyx_v_k])); /* "sklearn/_tree.pyx":1112 * for k from 0 <= k < n_outputs: * total_sum_left += sum_left[k] * total_sum_right += sum_right[k] # <<<<<<<<<<<<<< * * total_sum_left = total_sum_left / n_outputs */ __pyx_v_total_sum_right = (__pyx_v_total_sum_right + (__pyx_v_sum_right[__pyx_v_k])); } /* "sklearn/_tree.pyx":1114 * total_sum_right += sum_right[k] * * total_sum_left = total_sum_left / n_outputs # <<<<<<<<<<<<<< * total_sum_right = total_sum_right / n_outputs * diff = ((total_sum_left / weighted_n_left) - */ __pyx_v_total_sum_left = (__pyx_v_total_sum_left / __pyx_v_n_outputs); /* "sklearn/_tree.pyx":1115 * * total_sum_left = total_sum_left / n_outputs * total_sum_right = total_sum_right / n_outputs # <<<<<<<<<<<<<< * diff = ((total_sum_left / weighted_n_left) - * (total_sum_right / weighted_n_right)) */ __pyx_v_total_sum_right = (__pyx_v_total_sum_right / __pyx_v_n_outputs); /* "sklearn/_tree.pyx":1116 * total_sum_left = total_sum_left / n_outputs * total_sum_right = total_sum_right / n_outputs * diff = ((total_sum_left / weighted_n_left) - # <<<<<<<<<<<<<< * (total_sum_right / weighted_n_right)) * */ __pyx_v_diff = ((__pyx_v_total_sum_left / __pyx_v_weighted_n_left) - (__pyx_v_total_sum_right / __pyx_v_weighted_n_right)); /* "sklearn/_tree.pyx":1119 * (total_sum_right / weighted_n_right)) * * return (weighted_n_left * weighted_n_right * diff * diff / # <<<<<<<<<<<<<< * (weighted_n_left + weighted_n_right)) * */ __pyx_r = ((((__pyx_v_weighted_n_left * __pyx_v_weighted_n_right) * __pyx_v_diff) * __pyx_v_diff) / (__pyx_v_weighted_n_left + __pyx_v_weighted_n_right)); goto __pyx_L0; /* "sklearn/_tree.pyx":1099 * """ * * cdef double impurity_improvement(self, double impurity) nogil: # <<<<<<<<<<<<<< * cdef SIZE_t n_outputs = self.n_outputs * cdef SIZE_t k */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "sklearn/_tree.pyx":1126 * # ============================================================================= * * cdef inline void _init_split(SplitRecord* self, SIZE_t start_pos) nogil: # <<<<<<<<<<<<<< * self.impurity_left = INFINITY * self.impurity_right = INFINITY */ static CYTHON_INLINE void __pyx_f_7sklearn_5_tree__init_split(struct __pyx_t_7sklearn_5_tree_SplitRecord *__pyx_v_self, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_start_pos) { /* "sklearn/_tree.pyx":1127 * * cdef inline void _init_split(SplitRecord* self, SIZE_t start_pos) nogil: * self.impurity_left = INFINITY # <<<<<<<<<<<<<< * self.impurity_right = INFINITY * self.pos = start_pos */ __pyx_v_self->impurity_left = __pyx_v_7sklearn_5_tree_INFINITY; /* "sklearn/_tree.pyx":1128 * cdef inline void _init_split(SplitRecord* self, SIZE_t start_pos) nogil: * self.impurity_left = INFINITY * self.impurity_right = INFINITY # <<<<<<<<<<<<<< * self.pos = start_pos * self.feature = 0 */ __pyx_v_self->impurity_right = __pyx_v_7sklearn_5_tree_INFINITY; /* "sklearn/_tree.pyx":1129 * self.impurity_left = INFINITY * self.impurity_right = INFINITY * self.pos = start_pos # <<<<<<<<<<<<<< * self.feature = 0 * self.threshold = 0. */ __pyx_v_self->pos = __pyx_v_start_pos; /* "sklearn/_tree.pyx":1130 * self.impurity_right = INFINITY * self.pos = start_pos * self.feature = 0 # <<<<<<<<<<<<<< * self.threshold = 0. * self.improvement = -INFINITY */ __pyx_v_self->feature = 0; /* "sklearn/_tree.pyx":1131 * self.pos = start_pos * self.feature = 0 * self.threshold = 0. # <<<<<<<<<<<<<< * self.improvement = -INFINITY * */ __pyx_v_self->threshold = 0.; /* "sklearn/_tree.pyx":1132 * self.feature = 0 * self.threshold = 0. * self.improvement = -INFINITY # <<<<<<<<<<<<<< * * */ __pyx_v_self->improvement = (-__pyx_v_7sklearn_5_tree_INFINITY); /* "sklearn/_tree.pyx":1126 * # ============================================================================= * * cdef inline void _init_split(SplitRecord* self, SIZE_t start_pos) nogil: # <<<<<<<<<<<<<< * self.impurity_left = INFINITY * self.impurity_right = INFINITY */ /* function exit code */ } /* "sklearn/_tree.pyx":1136 * * cdef class Splitter: * def __cinit__(self, Criterion criterion, SIZE_t max_features, # <<<<<<<<<<<<<< * SIZE_t min_samples_leaf, double min_weight_leaf, * object random_state): */ /* Python wrapper */ static int __pyx_pw_7sklearn_5_tree_8Splitter_1__cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static int __pyx_pw_7sklearn_5_tree_8Splitter_1__cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { struct __pyx_obj_7sklearn_5_tree_Criterion *__pyx_v_criterion = 0; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_max_features; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_min_samples_leaf; double __pyx_v_min_weight_leaf; PyObject *__pyx_v_random_state = 0; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__cinit__ (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_criterion,&__pyx_n_s_max_features,&__pyx_n_s_min_samples_leaf,&__pyx_n_s_min_weight_leaf,&__pyx_n_s_random_state,0}; PyObject* values[5] = {0,0,0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); 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__pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_drawn_constants; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_known_constants; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_total_constants; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_partition_end; __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_t_1; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_2; __pyx_t_7sklearn_5_tree_DTYPE_t *__pyx_t_3; double __pyx_t_4; int __pyx_t_5; int __pyx_t_6; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_7; /* "sklearn/_tree.pyx":1288 * """Find the best split on node samples[start:end].""" * # Find the best split * cdef SIZE_t* samples = self.samples # <<<<<<<<<<<<<< * cdef SIZE_t start = self.start * cdef SIZE_t end = self.end */ __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.samples; __pyx_v_samples = __pyx_t_1; /* "sklearn/_tree.pyx":1289 * # Find the best split * cdef SIZE_t* samples = self.samples * cdef SIZE_t start = self.start # <<<<<<<<<<<<<< * cdef SIZE_t end = self.end * */ __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.start; __pyx_v_start = __pyx_t_2; /* "sklearn/_tree.pyx":1290 * cdef SIZE_t* samples = self.samples * cdef SIZE_t start = self.start * cdef SIZE_t end = self.end # <<<<<<<<<<<<<< * * cdef SIZE_t* features = self.features */ __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.end; __pyx_v_end = __pyx_t_2; /* "sklearn/_tree.pyx":1292 * cdef SIZE_t end = self.end * * cdef SIZE_t* features = self.features # <<<<<<<<<<<<<< * cdef SIZE_t* constant_features = self.constant_features * cdef SIZE_t n_features = self.n_features */ __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.features; __pyx_v_features = __pyx_t_1; /* "sklearn/_tree.pyx":1293 * * cdef SIZE_t* features = self.features * cdef SIZE_t* constant_features = self.constant_features # <<<<<<<<<<<<<< * cdef SIZE_t n_features = self.n_features * */ __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.constant_features; __pyx_v_constant_features = __pyx_t_1; /* "sklearn/_tree.pyx":1294 * cdef SIZE_t* features = self.features * cdef SIZE_t* constant_features = self.constant_features * cdef SIZE_t n_features = self.n_features # <<<<<<<<<<<<<< * * cdef DTYPE_t* X = self.X */ __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.n_features; __pyx_v_n_features = __pyx_t_2; /* "sklearn/_tree.pyx":1296 * cdef SIZE_t n_features = self.n_features * * cdef DTYPE_t* X = self.X # <<<<<<<<<<<<<< * cdef DTYPE_t* Xf = self.feature_values * cdef SIZE_t X_sample_stride = self.X_sample_stride */ __pyx_t_3 = __pyx_v_self->__pyx_base.X; __pyx_v_X = __pyx_t_3; /* "sklearn/_tree.pyx":1297 * * cdef DTYPE_t* X = self.X * cdef DTYPE_t* Xf = self.feature_values # <<<<<<<<<<<<<< * cdef SIZE_t X_sample_stride = self.X_sample_stride * cdef SIZE_t X_fx_stride = self.X_fx_stride */ __pyx_t_3 = __pyx_v_self->__pyx_base.__pyx_base.feature_values; __pyx_v_Xf = __pyx_t_3; /* "sklearn/_tree.pyx":1298 * cdef DTYPE_t* X = self.X * cdef DTYPE_t* Xf = self.feature_values * cdef SIZE_t X_sample_stride = self.X_sample_stride # <<<<<<<<<<<<<< * cdef SIZE_t X_fx_stride = self.X_fx_stride * cdef SIZE_t max_features = self.max_features */ __pyx_t_2 = __pyx_v_self->__pyx_base.X_sample_stride; __pyx_v_X_sample_stride = __pyx_t_2; /* "sklearn/_tree.pyx":1299 * cdef DTYPE_t* Xf = self.feature_values * cdef SIZE_t X_sample_stride = self.X_sample_stride * cdef SIZE_t X_fx_stride = self.X_fx_stride # <<<<<<<<<<<<<< * cdef SIZE_t max_features = self.max_features * cdef SIZE_t min_samples_leaf = self.min_samples_leaf */ __pyx_t_2 = __pyx_v_self->__pyx_base.X_fx_stride; __pyx_v_X_fx_stride = __pyx_t_2; /* "sklearn/_tree.pyx":1300 * cdef SIZE_t X_sample_stride = self.X_sample_stride * cdef SIZE_t X_fx_stride = self.X_fx_stride * cdef SIZE_t max_features = self.max_features # <<<<<<<<<<<<<< * cdef SIZE_t min_samples_leaf = self.min_samples_leaf * cdef double min_weight_leaf = self.min_weight_leaf */ __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.max_features; __pyx_v_max_features = __pyx_t_2; /* "sklearn/_tree.pyx":1301 * cdef SIZE_t X_fx_stride = self.X_fx_stride * cdef SIZE_t max_features = self.max_features * cdef SIZE_t min_samples_leaf = self.min_samples_leaf # <<<<<<<<<<<<<< * cdef double min_weight_leaf = self.min_weight_leaf * cdef UINT32_t* random_state = &self.rand_r_state */ __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.min_samples_leaf; __pyx_v_min_samples_leaf = __pyx_t_2; /* "sklearn/_tree.pyx":1302 * cdef SIZE_t max_features = self.max_features * cdef SIZE_t min_samples_leaf = self.min_samples_leaf * cdef double min_weight_leaf = self.min_weight_leaf # <<<<<<<<<<<<<< * cdef UINT32_t* random_state = &self.rand_r_state * */ __pyx_t_4 = __pyx_v_self->__pyx_base.__pyx_base.min_weight_leaf; __pyx_v_min_weight_leaf = __pyx_t_4; /* "sklearn/_tree.pyx":1303 * cdef SIZE_t min_samples_leaf = self.min_samples_leaf * cdef double min_weight_leaf = self.min_weight_leaf * cdef UINT32_t* random_state = &self.rand_r_state # <<<<<<<<<<<<<< * * cdef SplitRecord best, current */ __pyx_v_random_state = (&__pyx_v_self->__pyx_base.__pyx_base.rand_r_state); /* "sklearn/_tree.pyx":1307 * cdef SplitRecord best, current * * cdef SIZE_t f_i = n_features # <<<<<<<<<<<<<< * cdef SIZE_t f_j, p, tmp * cdef SIZE_t n_visited_features = 0 */ __pyx_v_f_i = __pyx_v_n_features; /* "sklearn/_tree.pyx":1309 * cdef SIZE_t f_i = n_features * cdef SIZE_t f_j, p, tmp * cdef SIZE_t n_visited_features = 0 # <<<<<<<<<<<<<< * # Number of features discovered to be constant during the split search * cdef SIZE_t n_found_constants = 0 */ __pyx_v_n_visited_features = 0; /* "sklearn/_tree.pyx":1311 * cdef SIZE_t n_visited_features = 0 * # Number of features discovered to be constant during the split search * cdef SIZE_t n_found_constants = 0 # <<<<<<<<<<<<<< * # Number of features known to be constant and drawn without replacement * cdef SIZE_t n_drawn_constants = 0 */ __pyx_v_n_found_constants = 0; /* "sklearn/_tree.pyx":1313 * cdef SIZE_t n_found_constants = 0 * # Number of features known to be constant and drawn without replacement * cdef SIZE_t n_drawn_constants = 0 # <<<<<<<<<<<<<< * cdef SIZE_t n_known_constants = n_constant_features[0] * # n_total_constants = n_known_constants + n_found_constants */ __pyx_v_n_drawn_constants = 0; /* "sklearn/_tree.pyx":1314 * # Number of features known to be constant and drawn without replacement * cdef SIZE_t n_drawn_constants = 0 * cdef SIZE_t n_known_constants = n_constant_features[0] # <<<<<<<<<<<<<< * # n_total_constants = n_known_constants + n_found_constants * cdef SIZE_t n_total_constants = n_known_constants */ __pyx_v_n_known_constants = (__pyx_v_n_constant_features[0]); /* "sklearn/_tree.pyx":1316 * cdef SIZE_t n_known_constants = n_constant_features[0] * # n_total_constants = n_known_constants + n_found_constants * cdef SIZE_t n_total_constants = n_known_constants # <<<<<<<<<<<<<< * cdef DTYPE_t current_feature_value * cdef SIZE_t partition_end */ __pyx_v_n_total_constants = __pyx_v_n_known_constants; /* "sklearn/_tree.pyx":1320 * cdef SIZE_t partition_end * * _init_split(&best, end) # <<<<<<<<<<<<<< * * # Sample up to max_features without replacement using a */ __pyx_f_7sklearn_5_tree__init_split((&__pyx_v_best), __pyx_v_end); /* "sklearn/_tree.pyx":1331 * # newly discovered constant features to spare computation on descendant * # nodes. * while (f_i > n_total_constants and # Stop early if remaining features # <<<<<<<<<<<<<< * # are constant * (n_visited_features < max_features or */ while (1) { __pyx_t_6 = ((__pyx_v_f_i > __pyx_v_n_total_constants) != 0); if (__pyx_t_6) { } else { __pyx_t_5 = __pyx_t_6; goto __pyx_L5_bool_binop_done; } /* "sklearn/_tree.pyx":1333 * while (f_i > n_total_constants and # Stop early if remaining features * # are constant * (n_visited_features < max_features or # <<<<<<<<<<<<<< * # At least one drawn features must be non constant * n_visited_features <= n_found_constants + n_drawn_constants)): */ __pyx_t_6 = ((__pyx_v_n_visited_features < __pyx_v_max_features) != 0); if (!__pyx_t_6) { } else { __pyx_t_5 = __pyx_t_6; goto __pyx_L5_bool_binop_done; } /* "sklearn/_tree.pyx":1335 * (n_visited_features < max_features or * # At least one drawn features must be non constant * n_visited_features <= n_found_constants + n_drawn_constants)): # <<<<<<<<<<<<<< * * n_visited_features += 1 */ __pyx_t_6 = ((__pyx_v_n_visited_features <= (__pyx_v_n_found_constants + __pyx_v_n_drawn_constants)) != 0); __pyx_t_5 = __pyx_t_6; __pyx_L5_bool_binop_done:; if (!__pyx_t_5) break; /* "sklearn/_tree.pyx":1337 * n_visited_features <= n_found_constants + n_drawn_constants)): * * n_visited_features += 1 # <<<<<<<<<<<<<< * * # Loop invariant: elements of features in */ __pyx_v_n_visited_features = (__pyx_v_n_visited_features + 1); /* "sklearn/_tree.pyx":1351 * * # Draw a feature at random * f_j = rand_int(n_drawn_constants, f_i - n_found_constants, # <<<<<<<<<<<<<< * random_state) * */ __pyx_v_f_j = __pyx_f_7sklearn_5_tree_rand_int(__pyx_v_n_drawn_constants, (__pyx_v_f_i - __pyx_v_n_found_constants), __pyx_v_random_state); /* "sklearn/_tree.pyx":1354 * random_state) * * if f_j < n_known_constants: # <<<<<<<<<<<<<< * # f_j in the interval [n_drawn_constants, n_known_constants[ * tmp = features[f_j] */ __pyx_t_5 = ((__pyx_v_f_j < __pyx_v_n_known_constants) != 0); if (__pyx_t_5) { /* "sklearn/_tree.pyx":1356 * if f_j < n_known_constants: * # f_j in the interval [n_drawn_constants, n_known_constants[ * tmp = features[f_j] # <<<<<<<<<<<<<< * features[f_j] = features[n_drawn_constants] * features[n_drawn_constants] = tmp */ __pyx_v_tmp = (__pyx_v_features[__pyx_v_f_j]); /* "sklearn/_tree.pyx":1357 * # f_j in the interval [n_drawn_constants, n_known_constants[ * tmp = features[f_j] * features[f_j] = features[n_drawn_constants] # <<<<<<<<<<<<<< * features[n_drawn_constants] = tmp * */ (__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_drawn_constants]); /* "sklearn/_tree.pyx":1358 * tmp = features[f_j] * features[f_j] = features[n_drawn_constants] * features[n_drawn_constants] = tmp # <<<<<<<<<<<<<< * * n_drawn_constants += 1 */ (__pyx_v_features[__pyx_v_n_drawn_constants]) = __pyx_v_tmp; /* "sklearn/_tree.pyx":1360 * features[n_drawn_constants] = tmp * * n_drawn_constants += 1 # <<<<<<<<<<<<<< * * else: */ __pyx_v_n_drawn_constants = (__pyx_v_n_drawn_constants + 1); goto __pyx_L8; } /*else*/ { /* "sklearn/_tree.pyx":1364 * else: * # f_j in the interval [n_known_constants, f_i - n_found_constants[ * f_j += n_found_constants # <<<<<<<<<<<<<< * # f_j in the interval [n_total_constants, f_i[ * */ __pyx_v_f_j = (__pyx_v_f_j + __pyx_v_n_found_constants); /* "sklearn/_tree.pyx":1367 * # f_j in the interval [n_total_constants, f_i[ * * current.feature = features[f_j] # <<<<<<<<<<<<<< * * # Sort samples along that feature; first copy the feature */ __pyx_v_current.feature = (__pyx_v_features[__pyx_v_f_j]); /* "sklearn/_tree.pyx":1373 * # Xf[i] == X[samples[i], j], so the sort uses the cache more * # effectively. * for p in range(start, end): # <<<<<<<<<<<<<< * Xf[p] = X[X_sample_stride * samples[p] + * X_fx_stride * current.feature] */ __pyx_t_2 = __pyx_v_end; for (__pyx_t_7 = __pyx_v_start; __pyx_t_7 < __pyx_t_2; __pyx_t_7+=1) { __pyx_v_p = __pyx_t_7; /* "sklearn/_tree.pyx":1374 * # effectively. * for p in range(start, end): * Xf[p] = X[X_sample_stride * samples[p] + # <<<<<<<<<<<<<< * X_fx_stride * current.feature] * */ (__pyx_v_Xf[__pyx_v_p]) = (__pyx_v_X[((__pyx_v_X_sample_stride * (__pyx_v_samples[__pyx_v_p])) + (__pyx_v_X_fx_stride * __pyx_v_current.feature))]); } /* "sklearn/_tree.pyx":1377 * X_fx_stride * current.feature] * * sort(Xf + start, samples + start, end - start) # <<<<<<<<<<<<<< * * if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD: */ __pyx_f_7sklearn_5_tree_sort((__pyx_v_Xf + __pyx_v_start), (__pyx_v_samples + __pyx_v_start), (__pyx_v_end - __pyx_v_start)); /* "sklearn/_tree.pyx":1379 * sort(Xf + start, samples + start, end - start) * * if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD: # <<<<<<<<<<<<<< * features[f_j] = features[n_total_constants] * features[n_total_constants] = current.feature */ __pyx_t_5 = (((__pyx_v_Xf[(__pyx_v_end - 1)]) <= ((__pyx_v_Xf[__pyx_v_start]) + __pyx_v_7sklearn_5_tree_FEATURE_THRESHOLD)) != 0); if (__pyx_t_5) { /* "sklearn/_tree.pyx":1380 * * if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD: * features[f_j] = features[n_total_constants] # <<<<<<<<<<<<<< * features[n_total_constants] = current.feature * */ (__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_total_constants]); /* "sklearn/_tree.pyx":1381 * if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD: * features[f_j] = features[n_total_constants] * features[n_total_constants] = current.feature # <<<<<<<<<<<<<< * * n_found_constants += 1 */ __pyx_t_2 = __pyx_v_current.feature; (__pyx_v_features[__pyx_v_n_total_constants]) = __pyx_t_2; /* "sklearn/_tree.pyx":1383 * features[n_total_constants] = current.feature * * n_found_constants += 1 # <<<<<<<<<<<<<< * n_total_constants += 1 * */ __pyx_v_n_found_constants = (__pyx_v_n_found_constants + 1); /* "sklearn/_tree.pyx":1384 * * n_found_constants += 1 * n_total_constants += 1 # <<<<<<<<<<<<<< * * else: */ __pyx_v_n_total_constants = (__pyx_v_n_total_constants + 1); goto __pyx_L11; } /*else*/ { /* "sklearn/_tree.pyx":1387 * * else: * f_i -= 1 # <<<<<<<<<<<<<< * features[f_i], features[f_j] = features[f_j], features[f_i] * */ __pyx_v_f_i = (__pyx_v_f_i - 1); /* "sklearn/_tree.pyx":1388 * else: * f_i -= 1 * features[f_i], features[f_j] = features[f_j], features[f_i] # <<<<<<<<<<<<<< * * # Evaluate all splits */ __pyx_t_2 = (__pyx_v_features[__pyx_v_f_j]); __pyx_t_7 = (__pyx_v_features[__pyx_v_f_i]); (__pyx_v_features[__pyx_v_f_i]) = __pyx_t_2; (__pyx_v_features[__pyx_v_f_j]) = __pyx_t_7; /* "sklearn/_tree.pyx":1391 * * # Evaluate all splits * self.criterion.reset() # <<<<<<<<<<<<<< * p = start * */ ((struct __pyx_vtabstruct_7sklearn_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion); /* "sklearn/_tree.pyx":1392 * # Evaluate all splits * self.criterion.reset() * p = start # <<<<<<<<<<<<<< * * while p < end: */ __pyx_v_p = __pyx_v_start; /* "sklearn/_tree.pyx":1394 * p = start * * while p < end: # <<<<<<<<<<<<<< * while (p + 1 < end and * Xf[p + 1] <= Xf[p] + FEATURE_THRESHOLD): */ while (1) { __pyx_t_5 = ((__pyx_v_p < __pyx_v_end) != 0); if (!__pyx_t_5) break; /* "sklearn/_tree.pyx":1395 * * while p < end: * while (p + 1 < end and # <<<<<<<<<<<<<< * Xf[p + 1] <= Xf[p] + FEATURE_THRESHOLD): * p += 1 */ while (1) { __pyx_t_6 = (((__pyx_v_p + 1) < __pyx_v_end) != 0); if (__pyx_t_6) { } else { __pyx_t_5 = __pyx_t_6; goto __pyx_L16_bool_binop_done; } /* "sklearn/_tree.pyx":1396 * while p < end: * while (p + 1 < end and * Xf[p + 1] <= Xf[p] + FEATURE_THRESHOLD): # <<<<<<<<<<<<<< * p += 1 * */ __pyx_t_6 = (((__pyx_v_Xf[(__pyx_v_p + 1)]) <= ((__pyx_v_Xf[__pyx_v_p]) + __pyx_v_7sklearn_5_tree_FEATURE_THRESHOLD)) != 0); __pyx_t_5 = __pyx_t_6; __pyx_L16_bool_binop_done:; if (!__pyx_t_5) break; /* "sklearn/_tree.pyx":1397 * while (p + 1 < end and * Xf[p + 1] <= Xf[p] + FEATURE_THRESHOLD): * p += 1 # <<<<<<<<<<<<<< * * # (p + 1 >= end) or (X[samples[p + 1], current.feature] > */ __pyx_v_p = (__pyx_v_p + 1); } /* "sklearn/_tree.pyx":1401 * # (p + 1 >= end) or (X[samples[p + 1], current.feature] > * # X[samples[p], current.feature]) * p += 1 # <<<<<<<<<<<<<< * # (p >= end) or (X[samples[p], current.feature] > * # X[samples[p - 1], current.feature]) */ __pyx_v_p = (__pyx_v_p + 1); /* "sklearn/_tree.pyx":1405 * # X[samples[p - 1], current.feature]) * * if p < end: # <<<<<<<<<<<<<< * current.pos = p * */ __pyx_t_5 = ((__pyx_v_p < __pyx_v_end) != 0); if (__pyx_t_5) { /* "sklearn/_tree.pyx":1406 * * if p < end: * current.pos = p # <<<<<<<<<<<<<< * * # Reject if min_samples_leaf is not guaranteed */ __pyx_v_current.pos = __pyx_v_p; /* "sklearn/_tree.pyx":1409 * * # Reject if min_samples_leaf is not guaranteed * if (((current.pos - start) < min_samples_leaf) or # <<<<<<<<<<<<<< * ((end - current.pos) < min_samples_leaf)): * continue */ __pyx_t_6 = (((__pyx_v_current.pos - __pyx_v_start) < __pyx_v_min_samples_leaf) != 0); if (!__pyx_t_6) { } else { __pyx_t_5 = __pyx_t_6; goto __pyx_L20_bool_binop_done; } /* "sklearn/_tree.pyx":1410 * # Reject if min_samples_leaf is not guaranteed * if (((current.pos - start) < min_samples_leaf) or * ((end - current.pos) < min_samples_leaf)): # <<<<<<<<<<<<<< * continue * */ __pyx_t_6 = (((__pyx_v_end - __pyx_v_current.pos) < __pyx_v_min_samples_leaf) != 0); __pyx_t_5 = __pyx_t_6; __pyx_L20_bool_binop_done:; if (__pyx_t_5) { /* "sklearn/_tree.pyx":1411 * if (((current.pos - start) < min_samples_leaf) or * ((end - current.pos) < min_samples_leaf)): * continue # <<<<<<<<<<<<<< * * self.criterion.update(current.pos) */ goto __pyx_L12_continue; } /* "sklearn/_tree.pyx":1413 * continue * * self.criterion.update(current.pos) # <<<<<<<<<<<<<< * * # Reject if min_weight_leaf is not satisfied */ ((struct __pyx_vtabstruct_7sklearn_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->update(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_current.pos); /* "sklearn/_tree.pyx":1416 * * # Reject if min_weight_leaf is not satisfied * if ((self.criterion.weighted_n_left < min_weight_leaf) or # <<<<<<<<<<<<<< * (self.criterion.weighted_n_right < min_weight_leaf)): * continue */ __pyx_t_6 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_left < __pyx_v_min_weight_leaf) != 0); if (!__pyx_t_6) { } else { __pyx_t_5 = __pyx_t_6; goto __pyx_L23_bool_binop_done; } /* "sklearn/_tree.pyx":1417 * # Reject if min_weight_leaf is not satisfied * if ((self.criterion.weighted_n_left < min_weight_leaf) or * (self.criterion.weighted_n_right < min_weight_leaf)): # <<<<<<<<<<<<<< * continue * */ __pyx_t_6 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_right < __pyx_v_min_weight_leaf) != 0); __pyx_t_5 = __pyx_t_6; __pyx_L23_bool_binop_done:; if (__pyx_t_5) { /* "sklearn/_tree.pyx":1418 * if ((self.criterion.weighted_n_left < min_weight_leaf) or * (self.criterion.weighted_n_right < min_weight_leaf)): * continue # <<<<<<<<<<<<<< * * current.improvement = self.criterion.impurity_improvement(impurity) */ goto __pyx_L12_continue; } /* "sklearn/_tree.pyx":1420 * continue * * current.improvement = self.criterion.impurity_improvement(impurity) # <<<<<<<<<<<<<< * * if current.improvement > best.improvement: */ __pyx_v_current.improvement = ((struct __pyx_vtabstruct_7sklearn_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->impurity_improvement(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_impurity); /* "sklearn/_tree.pyx":1422 * current.improvement = self.criterion.impurity_improvement(impurity) * * if current.improvement > best.improvement: # <<<<<<<<<<<<<< * self.criterion.children_impurity(¤t.impurity_left, * ¤t.impurity_right) */ __pyx_t_5 = ((__pyx_v_current.improvement > __pyx_v_best.improvement) != 0); if (__pyx_t_5) { /* "sklearn/_tree.pyx":1423 * * if current.improvement > best.improvement: * self.criterion.children_impurity(¤t.impurity_left, # <<<<<<<<<<<<<< * ¤t.impurity_right) * current.threshold = (Xf[p - 1] + Xf[p]) / 2.0 */ ((struct __pyx_vtabstruct_7sklearn_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->children_impurity(__pyx_v_self->__pyx_base.__pyx_base.criterion, (&__pyx_v_current.impurity_left), (&__pyx_v_current.impurity_right)); /* "sklearn/_tree.pyx":1425 * self.criterion.children_impurity(¤t.impurity_left, * ¤t.impurity_right) * current.threshold = (Xf[p - 1] + Xf[p]) / 2.0 # <<<<<<<<<<<<<< * * if current.threshold == Xf[p]: */ __pyx_v_current.threshold = (((__pyx_v_Xf[(__pyx_v_p - 1)]) + (__pyx_v_Xf[__pyx_v_p])) / 2.0); /* "sklearn/_tree.pyx":1427 * current.threshold = (Xf[p - 1] + Xf[p]) / 2.0 * * if current.threshold == Xf[p]: # <<<<<<<<<<<<<< * current.threshold = Xf[p - 1] * */ __pyx_t_5 = ((__pyx_v_current.threshold == (__pyx_v_Xf[__pyx_v_p])) != 0); if (__pyx_t_5) { /* "sklearn/_tree.pyx":1428 * * if current.threshold == Xf[p]: * current.threshold = Xf[p - 1] # <<<<<<<<<<<<<< * * best = current # copy */ __pyx_v_current.threshold = (__pyx_v_Xf[(__pyx_v_p - 1)]); goto __pyx_L26; } __pyx_L26:; /* "sklearn/_tree.pyx":1430 * current.threshold = Xf[p - 1] * * best = current # copy # <<<<<<<<<<<<<< * * # Reorganize into samples[start:best.pos] + samples[best.pos:end] */ __pyx_v_best = __pyx_v_current; goto __pyx_L25; } __pyx_L25:; goto __pyx_L18; } __pyx_L18:; __pyx_L12_continue:; } } __pyx_L11:; } __pyx_L8:; } /* "sklearn/_tree.pyx":1433 * * # Reorganize into samples[start:best.pos] + samples[best.pos:end] * if best.pos < end: # <<<<<<<<<<<<<< * partition_end = end * p = start */ __pyx_t_5 = ((__pyx_v_best.pos < __pyx_v_end) != 0); if (__pyx_t_5) { /* "sklearn/_tree.pyx":1434 * # Reorganize into samples[start:best.pos] + samples[best.pos:end] * if best.pos < end: * partition_end = end # <<<<<<<<<<<<<< * p = start * */ __pyx_v_partition_end = __pyx_v_end; /* "sklearn/_tree.pyx":1435 * if best.pos < end: * partition_end = end * p = start # <<<<<<<<<<<<<< * * while p < partition_end: */ __pyx_v_p = __pyx_v_start; /* "sklearn/_tree.pyx":1437 * p = start * * while p < partition_end: # <<<<<<<<<<<<<< * if X[X_sample_stride * samples[p] + * X_fx_stride * best.feature] <= best.threshold: */ while (1) { __pyx_t_5 = ((__pyx_v_p < __pyx_v_partition_end) != 0); if (!__pyx_t_5) break; /* "sklearn/_tree.pyx":1439 * while p < partition_end: * if X[X_sample_stride * samples[p] + * X_fx_stride * best.feature] <= best.threshold: # <<<<<<<<<<<<<< * p += 1 * */ __pyx_t_5 = (((__pyx_v_X[((__pyx_v_X_sample_stride * (__pyx_v_samples[__pyx_v_p])) + (__pyx_v_X_fx_stride * __pyx_v_best.feature))]) <= __pyx_v_best.threshold) != 0); if (__pyx_t_5) { /* "sklearn/_tree.pyx":1440 * if X[X_sample_stride * samples[p] + * X_fx_stride * best.feature] <= best.threshold: * p += 1 # <<<<<<<<<<<<<< * * else: */ __pyx_v_p = (__pyx_v_p + 1); goto __pyx_L30; } /*else*/ { /* "sklearn/_tree.pyx":1443 * * else: * partition_end -= 1 # <<<<<<<<<<<<<< * * tmp = samples[partition_end] */ __pyx_v_partition_end = (__pyx_v_partition_end - 1); /* "sklearn/_tree.pyx":1445 * partition_end -= 1 * * tmp = samples[partition_end] # <<<<<<<<<<<<<< * samples[partition_end] = samples[p] * samples[p] = tmp */ __pyx_v_tmp = (__pyx_v_samples[__pyx_v_partition_end]); /* "sklearn/_tree.pyx":1446 * * tmp = samples[partition_end] * samples[partition_end] = samples[p] # <<<<<<<<<<<<<< * samples[p] = tmp * */ (__pyx_v_samples[__pyx_v_partition_end]) = (__pyx_v_samples[__pyx_v_p]); /* "sklearn/_tree.pyx":1447 * tmp = samples[partition_end] * samples[partition_end] = samples[p] * samples[p] = tmp # <<<<<<<<<<<<<< * * # Respect invariant for constant features: the original order of */ (__pyx_v_samples[__pyx_v_p]) = __pyx_v_tmp; } __pyx_L30:; } goto __pyx_L27; } __pyx_L27:; /* "sklearn/_tree.pyx":1452 * # element in features[:n_known_constants] must be preserved for sibling * # and child nodes * memcpy(features, constant_features, sizeof(SIZE_t) * n_known_constants) # <<<<<<<<<<<<<< * * # Copy newly found constant features */ memcpy(__pyx_v_features, __pyx_v_constant_features, ((sizeof(__pyx_t_7sklearn_5_tree_SIZE_t)) * __pyx_v_n_known_constants)); /* "sklearn/_tree.pyx":1455 * * # Copy newly found constant features * memcpy(constant_features + n_known_constants, # <<<<<<<<<<<<<< * features + n_known_constants, * sizeof(SIZE_t) * n_found_constants) */ memcpy((__pyx_v_constant_features + __pyx_v_n_known_constants), (__pyx_v_features + __pyx_v_n_known_constants), ((sizeof(__pyx_t_7sklearn_5_tree_SIZE_t)) * __pyx_v_n_found_constants)); /* "sklearn/_tree.pyx":1460 * * # Return values * split[0] = best # <<<<<<<<<<<<<< * n_constant_features[0] = n_total_constants * */ (__pyx_v_split[0]) = __pyx_v_best; /* "sklearn/_tree.pyx":1461 * # Return values * split[0] = best * n_constant_features[0] = n_total_constants # <<<<<<<<<<<<<< * * */ (__pyx_v_n_constant_features[0]) = __pyx_v_n_total_constants; /* "sklearn/_tree.pyx":1284 * self.random_state), self.__getstate__()) * * cdef void node_split(self, double impurity, SplitRecord* split, # <<<<<<<<<<<<<< * SIZE_t* n_constant_features) nogil: * """Find the best split on node samples[start:end].""" */ /* function exit code */ } /* "sklearn/_tree.pyx":1466 * # Sort n-element arrays pointed to by Xf and samples, simultaneously, * # by the values in Xf. Algorithm: Introsort (Musser, SP&E, 1997). * cdef inline void sort(DTYPE_t* Xf, SIZE_t* samples, SIZE_t n) nogil: # <<<<<<<<<<<<<< * cdef int maxd = 2 * log(n) * introsort(Xf, samples, n, maxd) */ static CYTHON_INLINE void __pyx_f_7sklearn_5_tree_sort(__pyx_t_7sklearn_5_tree_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n) { int __pyx_v_maxd; /* "sklearn/_tree.pyx":1467 * # by the values in Xf. Algorithm: Introsort (Musser, SP&E, 1997). * cdef inline void sort(DTYPE_t* Xf, SIZE_t* samples, SIZE_t n) nogil: * cdef int maxd = 2 * log(n) # <<<<<<<<<<<<<< * introsort(Xf, samples, n, maxd) * */ __pyx_v_maxd = (2 * ((int)__pyx_f_7sklearn_5_tree_log(__pyx_v_n))); /* "sklearn/_tree.pyx":1468 * cdef inline void sort(DTYPE_t* Xf, SIZE_t* samples, SIZE_t n) nogil: * cdef int maxd = 2 * log(n) * introsort(Xf, samples, n, maxd) # <<<<<<<<<<<<<< * * */ __pyx_f_7sklearn_5_tree_introsort(__pyx_v_Xf, __pyx_v_samples, __pyx_v_n, __pyx_v_maxd); /* "sklearn/_tree.pyx":1466 * # Sort n-element arrays pointed to by Xf and samples, simultaneously, * # by the values in Xf. Algorithm: Introsort (Musser, SP&E, 1997). * cdef inline void sort(DTYPE_t* Xf, SIZE_t* samples, SIZE_t n) nogil: # <<<<<<<<<<<<<< * cdef int maxd = 2 * log(n) * introsort(Xf, samples, n, maxd) */ /* function exit code */ } /* "sklearn/_tree.pyx":1471 * * * cdef inline void swap(DTYPE_t* Xf, SIZE_t* samples, SIZE_t i, SIZE_t j) nogil: # <<<<<<<<<<<<<< * # Helper for sort * Xf[i], Xf[j] = Xf[j], Xf[i] */ static CYTHON_INLINE void __pyx_f_7sklearn_5_tree_swap(__pyx_t_7sklearn_5_tree_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_i, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_j) { __pyx_t_7sklearn_5_tree_DTYPE_t __pyx_t_1; __pyx_t_7sklearn_5_tree_DTYPE_t __pyx_t_2; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_3; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_4; /* "sklearn/_tree.pyx":1473 * cdef inline void swap(DTYPE_t* Xf, SIZE_t* samples, SIZE_t i, SIZE_t j) nogil: * # Helper for sort * Xf[i], Xf[j] = Xf[j], Xf[i] # <<<<<<<<<<<<<< * samples[i], samples[j] = samples[j], samples[i] * */ __pyx_t_1 = (__pyx_v_Xf[__pyx_v_j]); __pyx_t_2 = (__pyx_v_Xf[__pyx_v_i]); (__pyx_v_Xf[__pyx_v_i]) = __pyx_t_1; (__pyx_v_Xf[__pyx_v_j]) = __pyx_t_2; /* "sklearn/_tree.pyx":1474 * # Helper for sort * Xf[i], Xf[j] = Xf[j], Xf[i] * samples[i], samples[j] = samples[j], samples[i] # <<<<<<<<<<<<<< * * */ __pyx_t_3 = (__pyx_v_samples[__pyx_v_j]); __pyx_t_4 = (__pyx_v_samples[__pyx_v_i]); (__pyx_v_samples[__pyx_v_i]) = __pyx_t_3; (__pyx_v_samples[__pyx_v_j]) = __pyx_t_4; /* "sklearn/_tree.pyx":1471 * * * cdef inline void swap(DTYPE_t* Xf, SIZE_t* samples, SIZE_t i, SIZE_t j) nogil: # <<<<<<<<<<<<<< * # Helper for sort * Xf[i], Xf[j] = Xf[j], Xf[i] */ /* function exit code */ } /* "sklearn/_tree.pyx":1477 * * * cdef inline DTYPE_t median3(DTYPE_t* Xf, SIZE_t n) nogil: # <<<<<<<<<<<<<< * # Median of three pivot selection, after Bentley and McIlroy (1993). * # Engineering a sort function. SP&E. Requires 8/3 comparisons on average. */ static CYTHON_INLINE __pyx_t_7sklearn_5_tree_DTYPE_t __pyx_f_7sklearn_5_tree_median3(__pyx_t_7sklearn_5_tree_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n) { __pyx_t_7sklearn_5_tree_DTYPE_t __pyx_v_a; __pyx_t_7sklearn_5_tree_DTYPE_t __pyx_v_b; __pyx_t_7sklearn_5_tree_DTYPE_t __pyx_v_c; __pyx_t_7sklearn_5_tree_DTYPE_t __pyx_r; int __pyx_t_1; /* "sklearn/_tree.pyx":1480 * # Median of three pivot selection, after Bentley and McIlroy (1993). * # Engineering a sort function. SP&E. Requires 8/3 comparisons on average. * cdef DTYPE_t a = Xf[0], b = Xf[n / 2], c = Xf[n - 1] # <<<<<<<<<<<<<< * if a < b: * if b < c: */ __pyx_v_a = (__pyx_v_Xf[0]); __pyx_v_b = (__pyx_v_Xf[(__pyx_v_n / 2)]); __pyx_v_c = (__pyx_v_Xf[(__pyx_v_n - 1)]); /* "sklearn/_tree.pyx":1481 * # Engineering a sort function. SP&E. Requires 8/3 comparisons on average. * cdef DTYPE_t a = Xf[0], b = Xf[n / 2], c = Xf[n - 1] * if a < b: # <<<<<<<<<<<<<< * if b < c: * return b */ __pyx_t_1 = ((__pyx_v_a < __pyx_v_b) != 0); if (__pyx_t_1) { /* "sklearn/_tree.pyx":1482 * cdef DTYPE_t a = Xf[0], b = Xf[n / 2], c = Xf[n - 1] * if a < b: * if b < c: # <<<<<<<<<<<<<< * return b * elif a < c: */ __pyx_t_1 = ((__pyx_v_b < __pyx_v_c) != 0); if (__pyx_t_1) { /* "sklearn/_tree.pyx":1483 * if a < b: * if b < c: * return b # <<<<<<<<<<<<<< * elif a < c: * return c */ __pyx_r = __pyx_v_b; goto __pyx_L0; } /* "sklearn/_tree.pyx":1484 * if b < c: * return b * elif a < c: # <<<<<<<<<<<<<< * return c * else: */ __pyx_t_1 = ((__pyx_v_a < __pyx_v_c) != 0); if (__pyx_t_1) { /* "sklearn/_tree.pyx":1485 * return b * elif a < c: * return c # <<<<<<<<<<<<<< * else: * return a */ __pyx_r = __pyx_v_c; goto __pyx_L0; } /*else*/ { /* "sklearn/_tree.pyx":1487 * return c * else: * return a # <<<<<<<<<<<<<< * elif b < c: * if a < c: */ __pyx_r = __pyx_v_a; goto __pyx_L0; } } /* "sklearn/_tree.pyx":1488 * else: * return a * elif b < c: # <<<<<<<<<<<<<< * if a < c: * return a */ __pyx_t_1 = ((__pyx_v_b < __pyx_v_c) != 0); if (__pyx_t_1) { /* "sklearn/_tree.pyx":1489 * return a * elif b < c: * if a < c: # <<<<<<<<<<<<<< * return a * else: */ __pyx_t_1 = ((__pyx_v_a < __pyx_v_c) != 0); if (__pyx_t_1) { /* "sklearn/_tree.pyx":1490 * elif b < c: * if a < c: * return a # <<<<<<<<<<<<<< * else: * return c */ __pyx_r = __pyx_v_a; goto __pyx_L0; } /*else*/ { /* "sklearn/_tree.pyx":1492 * return a * else: * return c # <<<<<<<<<<<<<< * else: * return b */ __pyx_r = __pyx_v_c; goto __pyx_L0; } } /*else*/ { /* "sklearn/_tree.pyx":1494 * return c * else: * return b # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_b; goto __pyx_L0; } /* "sklearn/_tree.pyx":1477 * * * cdef inline DTYPE_t median3(DTYPE_t* Xf, SIZE_t n) nogil: # <<<<<<<<<<<<<< * # Median of three pivot selection, after Bentley and McIlroy (1993). * # Engineering a sort function. SP&E. Requires 8/3 comparisons on average. */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "sklearn/_tree.pyx":1499 * # Introsort with median of 3 pivot selection and 3-way partition function * # (robust to repeated elements, e.g. lots of zero features). * cdef void introsort(DTYPE_t* Xf, SIZE_t *samples, SIZE_t n, int maxd) nogil: # <<<<<<<<<<<<<< * cdef DTYPE_t pivot * cdef SIZE_t i, l, r */ static void __pyx_f_7sklearn_5_tree_introsort(__pyx_t_7sklearn_5_tree_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n, int __pyx_v_maxd) { __pyx_t_7sklearn_5_tree_DTYPE_t __pyx_v_pivot; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_i; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_l; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_r; int __pyx_t_1; /* "sklearn/_tree.pyx":1503 * cdef SIZE_t i, l, r * * while n > 1: # <<<<<<<<<<<<<< * if maxd <= 0: # max depth limit exceeded ("gone quadratic") * heapsort(Xf, samples, n) */ while (1) { __pyx_t_1 = ((__pyx_v_n > 1) != 0); if (!__pyx_t_1) break; /* "sklearn/_tree.pyx":1504 * * while n > 1: * if maxd <= 0: # max depth limit exceeded ("gone quadratic") # <<<<<<<<<<<<<< * heapsort(Xf, samples, n) * return */ __pyx_t_1 = ((__pyx_v_maxd <= 0) != 0); if (__pyx_t_1) { /* "sklearn/_tree.pyx":1505 * while n > 1: * if maxd <= 0: # max depth limit exceeded ("gone quadratic") * heapsort(Xf, samples, n) # <<<<<<<<<<<<<< * return * maxd -= 1 */ __pyx_f_7sklearn_5_tree_heapsort(__pyx_v_Xf, __pyx_v_samples, __pyx_v_n); /* "sklearn/_tree.pyx":1506 * if maxd <= 0: # max depth limit exceeded ("gone quadratic") * heapsort(Xf, samples, n) * return # <<<<<<<<<<<<<< * maxd -= 1 * */ goto __pyx_L0; } /* "sklearn/_tree.pyx":1507 * heapsort(Xf, samples, n) * return * maxd -= 1 # <<<<<<<<<<<<<< * * pivot = median3(Xf, n) */ __pyx_v_maxd = (__pyx_v_maxd - 1); /* "sklearn/_tree.pyx":1509 * maxd -= 1 * * pivot = median3(Xf, n) # <<<<<<<<<<<<<< * * # Three-way partition. */ __pyx_v_pivot = __pyx_f_7sklearn_5_tree_median3(__pyx_v_Xf, __pyx_v_n); /* "sklearn/_tree.pyx":1512 * * # Three-way partition. * i = l = 0 # <<<<<<<<<<<<<< * r = n * while i < r: */ __pyx_v_i = 0; __pyx_v_l = 0; /* "sklearn/_tree.pyx":1513 * # Three-way partition. * i = l = 0 * r = n # <<<<<<<<<<<<<< * while i < r: * if Xf[i] < pivot: */ __pyx_v_r = __pyx_v_n; /* "sklearn/_tree.pyx":1514 * i = l = 0 * r = n * while i < r: # <<<<<<<<<<<<<< * if Xf[i] < pivot: * swap(Xf, samples, i, l) */ while (1) { __pyx_t_1 = ((__pyx_v_i < __pyx_v_r) != 0); if (!__pyx_t_1) break; /* "sklearn/_tree.pyx":1515 * r = n * while i < r: * if Xf[i] < pivot: # <<<<<<<<<<<<<< * swap(Xf, samples, i, l) * i += 1 */ __pyx_t_1 = (((__pyx_v_Xf[__pyx_v_i]) < __pyx_v_pivot) != 0); if (__pyx_t_1) { /* "sklearn/_tree.pyx":1516 * while i < r: * if Xf[i] < pivot: * swap(Xf, samples, i, l) # <<<<<<<<<<<<<< * i += 1 * l += 1 */ __pyx_f_7sklearn_5_tree_swap(__pyx_v_Xf, __pyx_v_samples, __pyx_v_i, __pyx_v_l); /* "sklearn/_tree.pyx":1517 * if Xf[i] < pivot: * swap(Xf, samples, i, l) * i += 1 # <<<<<<<<<<<<<< * l += 1 * elif Xf[i] > pivot: */ __pyx_v_i = (__pyx_v_i + 1); /* "sklearn/_tree.pyx":1518 * swap(Xf, samples, i, l) * i += 1 * l += 1 # <<<<<<<<<<<<<< * elif Xf[i] > pivot: * r -= 1 */ __pyx_v_l = (__pyx_v_l + 1); goto __pyx_L8; } /* "sklearn/_tree.pyx":1519 * i += 1 * l += 1 * elif Xf[i] > pivot: # <<<<<<<<<<<<<< * r -= 1 * swap(Xf, samples, i, r) */ __pyx_t_1 = (((__pyx_v_Xf[__pyx_v_i]) > __pyx_v_pivot) != 0); if (__pyx_t_1) { /* "sklearn/_tree.pyx":1520 * l += 1 * elif Xf[i] > pivot: * r -= 1 # <<<<<<<<<<<<<< * swap(Xf, samples, i, r) * else: */ __pyx_v_r = (__pyx_v_r - 1); /* "sklearn/_tree.pyx":1521 * elif Xf[i] > pivot: * r -= 1 * swap(Xf, samples, i, r) # <<<<<<<<<<<<<< * else: * i += 1 */ __pyx_f_7sklearn_5_tree_swap(__pyx_v_Xf, __pyx_v_samples, __pyx_v_i, __pyx_v_r); goto __pyx_L8; } /*else*/ { /* "sklearn/_tree.pyx":1523 * swap(Xf, samples, i, r) * else: * i += 1 # <<<<<<<<<<<<<< * * introsort(Xf, samples, l, maxd) */ __pyx_v_i = (__pyx_v_i + 1); } __pyx_L8:; } /* "sklearn/_tree.pyx":1525 * i += 1 * * introsort(Xf, samples, l, maxd) # <<<<<<<<<<<<<< * Xf += r * samples += r */ __pyx_f_7sklearn_5_tree_introsort(__pyx_v_Xf, __pyx_v_samples, __pyx_v_l, __pyx_v_maxd); /* "sklearn/_tree.pyx":1526 * * introsort(Xf, samples, l, maxd) * Xf += r # <<<<<<<<<<<<<< * samples += r * n -= r */ __pyx_v_Xf = (__pyx_v_Xf + __pyx_v_r); /* "sklearn/_tree.pyx":1527 * introsort(Xf, samples, l, maxd) * Xf += r * samples += r # <<<<<<<<<<<<<< * n -= r * */ __pyx_v_samples = (__pyx_v_samples + __pyx_v_r); /* "sklearn/_tree.pyx":1528 * Xf += r * samples += r * n -= r # <<<<<<<<<<<<<< * * */ __pyx_v_n = (__pyx_v_n - __pyx_v_r); } /* "sklearn/_tree.pyx":1499 * # Introsort with median of 3 pivot selection and 3-way partition function * # (robust to repeated elements, e.g. lots of zero features). * cdef void introsort(DTYPE_t* Xf, SIZE_t *samples, SIZE_t n, int maxd) nogil: # <<<<<<<<<<<<<< * cdef DTYPE_t pivot * cdef SIZE_t i, l, r */ /* function exit code */ __pyx_L0:; } /* "sklearn/_tree.pyx":1531 * * * cdef inline void sift_down(DTYPE_t* Xf, SIZE_t* samples, # <<<<<<<<<<<<<< * SIZE_t start, SIZE_t end) nogil: * # Restore heap order in Xf[start:end] by moving the max element to start. */ static CYTHON_INLINE void __pyx_f_7sklearn_5_tree_sift_down(__pyx_t_7sklearn_5_tree_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_start, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_end) { __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_child; 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__pyx_t_7sklearn_5_tree_DTYPE_t __pyx_v_max_feature_value; __pyx_t_7sklearn_5_tree_DTYPE_t __pyx_v_current_feature_value; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_partition_end; __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_t_1; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_2; __pyx_t_7sklearn_5_tree_DTYPE_t *__pyx_t_3; double __pyx_t_4; int __pyx_t_5; int __pyx_t_6; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_7; /* "sklearn/_tree.pyx":1587 * """Find the best random split on node samples[start:end].""" * # Draw random splits and pick the best * cdef SIZE_t* samples = self.samples # <<<<<<<<<<<<<< * cdef SIZE_t start = self.start * cdef SIZE_t end = self.end */ __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.samples; __pyx_v_samples = __pyx_t_1; /* "sklearn/_tree.pyx":1588 * # Draw random splits and pick the best * cdef SIZE_t* samples = self.samples * cdef SIZE_t start = self.start # <<<<<<<<<<<<<< * cdef SIZE_t end = self.end * */ __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.start; __pyx_v_start = __pyx_t_2; /* "sklearn/_tree.pyx":1589 * cdef SIZE_t* samples = self.samples * cdef SIZE_t start = self.start * cdef SIZE_t end = self.end # <<<<<<<<<<<<<< * * cdef SIZE_t* features = self.features */ __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.end; __pyx_v_end = __pyx_t_2; /* "sklearn/_tree.pyx":1591 * cdef SIZE_t end = self.end * * cdef SIZE_t* features = self.features # <<<<<<<<<<<<<< * cdef SIZE_t* constant_features = self.constant_features * cdef SIZE_t n_features = self.n_features */ __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.features; __pyx_v_features = __pyx_t_1; /* "sklearn/_tree.pyx":1592 * * cdef SIZE_t* features = self.features * cdef SIZE_t* constant_features = self.constant_features # <<<<<<<<<<<<<< * cdef SIZE_t n_features = self.n_features * */ __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.constant_features; __pyx_v_constant_features = __pyx_t_1; /* "sklearn/_tree.pyx":1593 * cdef SIZE_t* features = self.features * cdef SIZE_t* constant_features = self.constant_features * cdef SIZE_t n_features = self.n_features # <<<<<<<<<<<<<< * * cdef DTYPE_t* X = self.X */ __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.n_features; __pyx_v_n_features = __pyx_t_2; /* "sklearn/_tree.pyx":1595 * cdef SIZE_t n_features = self.n_features * * cdef DTYPE_t* X = self.X # <<<<<<<<<<<<<< * cdef DTYPE_t* Xf = self.feature_values * cdef SIZE_t X_sample_stride = self.X_sample_stride */ __pyx_t_3 = __pyx_v_self->__pyx_base.X; __pyx_v_X = __pyx_t_3; /* "sklearn/_tree.pyx":1596 * * cdef DTYPE_t* X = self.X * cdef DTYPE_t* Xf = self.feature_values # <<<<<<<<<<<<<< * cdef SIZE_t X_sample_stride = self.X_sample_stride * cdef SIZE_t X_fx_stride = self.X_fx_stride */ __pyx_t_3 = __pyx_v_self->__pyx_base.__pyx_base.feature_values; __pyx_v_Xf = __pyx_t_3; /* "sklearn/_tree.pyx":1597 * cdef DTYPE_t* X = self.X * cdef DTYPE_t* Xf = self.feature_values * cdef SIZE_t X_sample_stride = self.X_sample_stride # <<<<<<<<<<<<<< * cdef SIZE_t X_fx_stride = self.X_fx_stride * cdef SIZE_t max_features = self.max_features */ __pyx_t_2 = __pyx_v_self->__pyx_base.X_sample_stride; __pyx_v_X_sample_stride = __pyx_t_2; /* "sklearn/_tree.pyx":1598 * cdef DTYPE_t* Xf = self.feature_values * cdef SIZE_t X_sample_stride = self.X_sample_stride * cdef SIZE_t X_fx_stride = self.X_fx_stride # <<<<<<<<<<<<<< * cdef SIZE_t max_features = self.max_features * cdef SIZE_t min_samples_leaf = self.min_samples_leaf */ __pyx_t_2 = __pyx_v_self->__pyx_base.X_fx_stride; __pyx_v_X_fx_stride = __pyx_t_2; /* "sklearn/_tree.pyx":1599 * cdef SIZE_t X_sample_stride = self.X_sample_stride * cdef SIZE_t X_fx_stride = self.X_fx_stride * cdef SIZE_t max_features = self.max_features # <<<<<<<<<<<<<< * cdef SIZE_t min_samples_leaf = self.min_samples_leaf * cdef double min_weight_leaf = self.min_weight_leaf */ __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.max_features; __pyx_v_max_features = __pyx_t_2; /* "sklearn/_tree.pyx":1600 * cdef SIZE_t X_fx_stride = self.X_fx_stride * cdef SIZE_t max_features = self.max_features * cdef SIZE_t min_samples_leaf = self.min_samples_leaf # <<<<<<<<<<<<<< * cdef double min_weight_leaf = self.min_weight_leaf * cdef UINT32_t* random_state = &self.rand_r_state */ __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.min_samples_leaf; __pyx_v_min_samples_leaf = __pyx_t_2; /* "sklearn/_tree.pyx":1601 * cdef SIZE_t max_features = self.max_features * cdef SIZE_t min_samples_leaf = self.min_samples_leaf * cdef double min_weight_leaf = self.min_weight_leaf # <<<<<<<<<<<<<< * cdef UINT32_t* random_state = &self.rand_r_state * */ __pyx_t_4 = __pyx_v_self->__pyx_base.__pyx_base.min_weight_leaf; __pyx_v_min_weight_leaf = __pyx_t_4; /* "sklearn/_tree.pyx":1602 * cdef SIZE_t min_samples_leaf = self.min_samples_leaf * cdef double min_weight_leaf = self.min_weight_leaf * cdef UINT32_t* random_state = &self.rand_r_state # <<<<<<<<<<<<<< * * cdef SplitRecord best, current */ __pyx_v_random_state = (&__pyx_v_self->__pyx_base.__pyx_base.rand_r_state); /* "sklearn/_tree.pyx":1606 * cdef SplitRecord best, current * * cdef SIZE_t f_i = n_features # <<<<<<<<<<<<<< * cdef SIZE_t f_j, p, tmp * # Number of features discovered to be constant during the split search */ __pyx_v_f_i = __pyx_v_n_features; /* "sklearn/_tree.pyx":1609 * cdef SIZE_t f_j, p, tmp * # Number of features discovered to be constant during the split search * cdef SIZE_t n_found_constants = 0 # <<<<<<<<<<<<<< * # Number of features known to be constant and drawn without replacement * cdef SIZE_t n_drawn_constants = 0 */ __pyx_v_n_found_constants = 0; /* "sklearn/_tree.pyx":1611 * cdef SIZE_t n_found_constants = 0 * # Number of features known to be constant and drawn without replacement * cdef SIZE_t n_drawn_constants = 0 # <<<<<<<<<<<<<< * cdef SIZE_t n_known_constants = n_constant_features[0] * # n_total_constants = n_known_constants + n_found_constants */ __pyx_v_n_drawn_constants = 0; /* "sklearn/_tree.pyx":1612 * # Number of features known to be constant and drawn without replacement * cdef SIZE_t n_drawn_constants = 0 * cdef SIZE_t n_known_constants = n_constant_features[0] # <<<<<<<<<<<<<< * # n_total_constants = n_known_constants + n_found_constants * cdef SIZE_t n_total_constants = n_known_constants */ __pyx_v_n_known_constants = (__pyx_v_n_constant_features[0]); /* "sklearn/_tree.pyx":1614 * cdef SIZE_t n_known_constants = n_constant_features[0] * # n_total_constants = n_known_constants + n_found_constants * cdef SIZE_t n_total_constants = n_known_constants # <<<<<<<<<<<<<< * cdef SIZE_t n_visited_features = 0 * cdef DTYPE_t min_feature_value */ __pyx_v_n_total_constants = __pyx_v_n_known_constants; /* "sklearn/_tree.pyx":1615 * # n_total_constants = n_known_constants + n_found_constants * cdef SIZE_t n_total_constants = n_known_constants * cdef SIZE_t n_visited_features = 0 # <<<<<<<<<<<<<< * cdef DTYPE_t min_feature_value * cdef DTYPE_t max_feature_value */ __pyx_v_n_visited_features = 0; /* "sklearn/_tree.pyx":1621 * cdef SIZE_t partition_end * * _init_split(&best, end) # <<<<<<<<<<<<<< * * # Sample up to max_features without replacement using a */ __pyx_f_7sklearn_5_tree__init_split((&__pyx_v_best), __pyx_v_end); /* "sklearn/_tree.pyx":1632 * # newly discovered constant features to spare computation on descendant * # nodes. * while (f_i > n_total_constants and # Stop early if remaining features # <<<<<<<<<<<<<< * # are constant * (n_visited_features < max_features or */ while (1) { __pyx_t_6 = ((__pyx_v_f_i > __pyx_v_n_total_constants) != 0); if (__pyx_t_6) { } else { __pyx_t_5 = __pyx_t_6; goto __pyx_L5_bool_binop_done; } /* "sklearn/_tree.pyx":1634 * while (f_i > n_total_constants and # Stop early if remaining features * # are constant * (n_visited_features < max_features or # <<<<<<<<<<<<<< * # At least one drawn features must be non constant * n_visited_features <= n_found_constants + n_drawn_constants)): */ __pyx_t_6 = ((__pyx_v_n_visited_features < __pyx_v_max_features) != 0); if (!__pyx_t_6) { } else { __pyx_t_5 = __pyx_t_6; goto __pyx_L5_bool_binop_done; } /* "sklearn/_tree.pyx":1636 * (n_visited_features < max_features or * # At least one drawn features must be non constant * n_visited_features <= n_found_constants + n_drawn_constants)): # <<<<<<<<<<<<<< * n_visited_features += 1 * */ __pyx_t_6 = ((__pyx_v_n_visited_features <= (__pyx_v_n_found_constants + __pyx_v_n_drawn_constants)) != 0); __pyx_t_5 = __pyx_t_6; __pyx_L5_bool_binop_done:; if (!__pyx_t_5) break; /* "sklearn/_tree.pyx":1637 * # At least one drawn features must be non constant * n_visited_features <= n_found_constants + n_drawn_constants)): * n_visited_features += 1 # <<<<<<<<<<<<<< * * # Loop invariant: elements of features in */ __pyx_v_n_visited_features = (__pyx_v_n_visited_features + 1); /* "sklearn/_tree.pyx":1651 * * # Draw a feature at random * f_j = rand_int(n_drawn_constants, f_i - n_found_constants, # <<<<<<<<<<<<<< * random_state) * */ __pyx_v_f_j = __pyx_f_7sklearn_5_tree_rand_int(__pyx_v_n_drawn_constants, (__pyx_v_f_i - __pyx_v_n_found_constants), __pyx_v_random_state); /* "sklearn/_tree.pyx":1654 * random_state) * * if f_j < n_known_constants: # <<<<<<<<<<<<<< * # f_j in the interval [n_drawn_constants, n_known_constants[ * tmp = features[f_j] */ __pyx_t_5 = ((__pyx_v_f_j < __pyx_v_n_known_constants) != 0); if (__pyx_t_5) { /* "sklearn/_tree.pyx":1656 * if f_j < n_known_constants: * # f_j in the interval [n_drawn_constants, n_known_constants[ * tmp = features[f_j] # <<<<<<<<<<<<<< * features[f_j] = features[n_drawn_constants] * features[n_drawn_constants] = tmp */ __pyx_v_tmp = (__pyx_v_features[__pyx_v_f_j]); /* "sklearn/_tree.pyx":1657 * # f_j in the interval [n_drawn_constants, n_known_constants[ * tmp = features[f_j] * features[f_j] = features[n_drawn_constants] # <<<<<<<<<<<<<< * features[n_drawn_constants] = tmp * */ (__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_drawn_constants]); /* "sklearn/_tree.pyx":1658 * tmp = features[f_j] * features[f_j] = features[n_drawn_constants] * features[n_drawn_constants] = tmp # <<<<<<<<<<<<<< * * n_drawn_constants += 1 */ (__pyx_v_features[__pyx_v_n_drawn_constants]) = __pyx_v_tmp; /* "sklearn/_tree.pyx":1660 * features[n_drawn_constants] = tmp * * n_drawn_constants += 1 # <<<<<<<<<<<<<< * * else: */ __pyx_v_n_drawn_constants = (__pyx_v_n_drawn_constants + 1); goto __pyx_L8; } /*else*/ { /* "sklearn/_tree.pyx":1664 * else: * # f_j in the interval [n_known_constants, f_i - n_found_constants[ * f_j += n_found_constants # <<<<<<<<<<<<<< * # f_j in the interval [n_total_constants, f_i[ * */ __pyx_v_f_j = (__pyx_v_f_j + __pyx_v_n_found_constants); /* "sklearn/_tree.pyx":1667 * # f_j in the interval [n_total_constants, f_i[ * * current.feature = features[f_j] # <<<<<<<<<<<<<< * * # Find min, max */ __pyx_v_current.feature = (__pyx_v_features[__pyx_v_f_j]); /* "sklearn/_tree.pyx":1670 * * # Find min, max * min_feature_value = X[X_sample_stride * samples[start] + # <<<<<<<<<<<<<< * X_fx_stride * current.feature] * max_feature_value = min_feature_value */ __pyx_v_min_feature_value = (__pyx_v_X[((__pyx_v_X_sample_stride * (__pyx_v_samples[__pyx_v_start])) + (__pyx_v_X_fx_stride * __pyx_v_current.feature))]); /* "sklearn/_tree.pyx":1672 * min_feature_value = X[X_sample_stride * samples[start] + * X_fx_stride * current.feature] * max_feature_value = min_feature_value # <<<<<<<<<<<<<< * Xf[start] = min_feature_value * */ __pyx_v_max_feature_value = __pyx_v_min_feature_value; /* "sklearn/_tree.pyx":1673 * X_fx_stride * current.feature] * max_feature_value = min_feature_value * Xf[start] = min_feature_value # <<<<<<<<<<<<<< * * for p in range(start + 1, end): */ (__pyx_v_Xf[__pyx_v_start]) = __pyx_v_min_feature_value; /* "sklearn/_tree.pyx":1675 * Xf[start] = min_feature_value * * for p in range(start + 1, end): # <<<<<<<<<<<<<< * current_feature_value = X[X_sample_stride * samples[p] + * X_fx_stride * current.feature] */ __pyx_t_2 = __pyx_v_end; for (__pyx_t_7 = (__pyx_v_start + 1); __pyx_t_7 < __pyx_t_2; __pyx_t_7+=1) { __pyx_v_p = __pyx_t_7; /* "sklearn/_tree.pyx":1676 * * for p in range(start + 1, end): * current_feature_value = X[X_sample_stride * samples[p] + # <<<<<<<<<<<<<< * X_fx_stride * current.feature] * Xf[p] = current_feature_value */ __pyx_v_current_feature_value = (__pyx_v_X[((__pyx_v_X_sample_stride * (__pyx_v_samples[__pyx_v_p])) + (__pyx_v_X_fx_stride * __pyx_v_current.feature))]); /* "sklearn/_tree.pyx":1678 * current_feature_value = X[X_sample_stride * samples[p] + * X_fx_stride * current.feature] * Xf[p] = current_feature_value # <<<<<<<<<<<<<< * * if current_feature_value < min_feature_value: */ (__pyx_v_Xf[__pyx_v_p]) = __pyx_v_current_feature_value; /* "sklearn/_tree.pyx":1680 * Xf[p] = current_feature_value * * if current_feature_value < min_feature_value: # <<<<<<<<<<<<<< * min_feature_value = current_feature_value * elif current_feature_value > max_feature_value: */ __pyx_t_5 = ((__pyx_v_current_feature_value < __pyx_v_min_feature_value) != 0); if (__pyx_t_5) { /* "sklearn/_tree.pyx":1681 * * if current_feature_value < min_feature_value: * min_feature_value = current_feature_value # <<<<<<<<<<<<<< * elif current_feature_value > max_feature_value: * max_feature_value = current_feature_value */ __pyx_v_min_feature_value = __pyx_v_current_feature_value; goto __pyx_L11; } /* "sklearn/_tree.pyx":1682 * if current_feature_value < min_feature_value: * min_feature_value = current_feature_value * elif current_feature_value > max_feature_value: # <<<<<<<<<<<<<< * max_feature_value = current_feature_value * */ __pyx_t_5 = ((__pyx_v_current_feature_value > __pyx_v_max_feature_value) != 0); if (__pyx_t_5) { /* "sklearn/_tree.pyx":1683 * min_feature_value = current_feature_value * elif current_feature_value > max_feature_value: * max_feature_value = current_feature_value # <<<<<<<<<<<<<< * * if max_feature_value <= min_feature_value + FEATURE_THRESHOLD: */ __pyx_v_max_feature_value = __pyx_v_current_feature_value; goto __pyx_L11; } __pyx_L11:; } /* "sklearn/_tree.pyx":1685 * max_feature_value = current_feature_value * * if max_feature_value <= min_feature_value + FEATURE_THRESHOLD: # <<<<<<<<<<<<<< * features[f_j] = features[n_total_constants] * features[n_total_constants] = current.feature */ __pyx_t_5 = ((__pyx_v_max_feature_value <= (__pyx_v_min_feature_value + __pyx_v_7sklearn_5_tree_FEATURE_THRESHOLD)) != 0); if (__pyx_t_5) { /* "sklearn/_tree.pyx":1686 * * if max_feature_value <= min_feature_value + FEATURE_THRESHOLD: * features[f_j] = features[n_total_constants] # <<<<<<<<<<<<<< * features[n_total_constants] = current.feature * */ (__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_total_constants]); /* "sklearn/_tree.pyx":1687 * if max_feature_value <= min_feature_value + FEATURE_THRESHOLD: * features[f_j] = features[n_total_constants] * features[n_total_constants] = current.feature # <<<<<<<<<<<<<< * * n_found_constants += 1 */ __pyx_t_2 = __pyx_v_current.feature; (__pyx_v_features[__pyx_v_n_total_constants]) = __pyx_t_2; /* "sklearn/_tree.pyx":1689 * features[n_total_constants] = current.feature * * n_found_constants += 1 # <<<<<<<<<<<<<< * n_total_constants += 1 * */ __pyx_v_n_found_constants = (__pyx_v_n_found_constants + 1); /* "sklearn/_tree.pyx":1690 * * n_found_constants += 1 * n_total_constants += 1 # <<<<<<<<<<<<<< * * else: */ __pyx_v_n_total_constants = (__pyx_v_n_total_constants + 1); goto __pyx_L12; } /*else*/ { /* "sklearn/_tree.pyx":1693 * * else: * f_i -= 1 # <<<<<<<<<<<<<< * features[f_i], features[f_j] = features[f_j], features[f_i] * */ __pyx_v_f_i = (__pyx_v_f_i - 1); /* "sklearn/_tree.pyx":1694 * else: * f_i -= 1 * features[f_i], features[f_j] = features[f_j], features[f_i] # <<<<<<<<<<<<<< * * # Draw a random threshold */ __pyx_t_2 = (__pyx_v_features[__pyx_v_f_j]); __pyx_t_7 = (__pyx_v_features[__pyx_v_f_i]); (__pyx_v_features[__pyx_v_f_i]) = __pyx_t_2; (__pyx_v_features[__pyx_v_f_j]) = __pyx_t_7; /* "sklearn/_tree.pyx":1697 * * # Draw a random threshold * current.threshold = rand_uniform(min_feature_value, # <<<<<<<<<<<<<< * max_feature_value, * random_state) */ __pyx_v_current.threshold = __pyx_f_7sklearn_5_tree_rand_uniform(__pyx_v_min_feature_value, __pyx_v_max_feature_value, __pyx_v_random_state); /* "sklearn/_tree.pyx":1701 * random_state) * * if current.threshold == max_feature_value: # <<<<<<<<<<<<<< * current.threshold = min_feature_value * */ __pyx_t_5 = ((__pyx_v_current.threshold == __pyx_v_max_feature_value) != 0); if (__pyx_t_5) { /* "sklearn/_tree.pyx":1702 * * if current.threshold == max_feature_value: * current.threshold = min_feature_value # <<<<<<<<<<<<<< * * # Partition */ __pyx_v_current.threshold = __pyx_v_min_feature_value; goto __pyx_L13; } __pyx_L13:; /* "sklearn/_tree.pyx":1705 * * # Partition * partition_end = end # <<<<<<<<<<<<<< * p = start * while p < partition_end: */ __pyx_v_partition_end = __pyx_v_end; /* "sklearn/_tree.pyx":1706 * # Partition * partition_end = end * p = start # <<<<<<<<<<<<<< * while p < partition_end: * current_feature_value = Xf[p] */ __pyx_v_p = __pyx_v_start; /* "sklearn/_tree.pyx":1707 * partition_end = end * p = start * while p < partition_end: # <<<<<<<<<<<<<< * current_feature_value = Xf[p] * if current_feature_value <= current.threshold: */ while (1) { __pyx_t_5 = ((__pyx_v_p < __pyx_v_partition_end) != 0); if (!__pyx_t_5) break; /* "sklearn/_tree.pyx":1708 * p = start * while p < partition_end: * current_feature_value = Xf[p] # <<<<<<<<<<<<<< * if current_feature_value <= current.threshold: * p += 1 */ __pyx_v_current_feature_value = (__pyx_v_Xf[__pyx_v_p]); /* "sklearn/_tree.pyx":1709 * while p < partition_end: * current_feature_value = Xf[p] * if current_feature_value <= current.threshold: # <<<<<<<<<<<<<< * p += 1 * else: */ __pyx_t_5 = ((__pyx_v_current_feature_value <= __pyx_v_current.threshold) != 0); if (__pyx_t_5) { /* "sklearn/_tree.pyx":1710 * current_feature_value = Xf[p] * if current_feature_value <= current.threshold: * p += 1 # <<<<<<<<<<<<<< * else: * partition_end -= 1 */ __pyx_v_p = (__pyx_v_p + 1); goto __pyx_L16; } /*else*/ { /* "sklearn/_tree.pyx":1712 * p += 1 * else: * partition_end -= 1 # <<<<<<<<<<<<<< * * Xf[p] = Xf[partition_end] */ __pyx_v_partition_end = (__pyx_v_partition_end - 1); /* "sklearn/_tree.pyx":1714 * partition_end -= 1 * * Xf[p] = Xf[partition_end] # <<<<<<<<<<<<<< * Xf[partition_end] = current_feature_value * */ (__pyx_v_Xf[__pyx_v_p]) = (__pyx_v_Xf[__pyx_v_partition_end]); /* "sklearn/_tree.pyx":1715 * * Xf[p] = Xf[partition_end] * Xf[partition_end] = current_feature_value # <<<<<<<<<<<<<< * * tmp = samples[partition_end] */ (__pyx_v_Xf[__pyx_v_partition_end]) = __pyx_v_current_feature_value; /* "sklearn/_tree.pyx":1717 * Xf[partition_end] = current_feature_value * * tmp = samples[partition_end] # <<<<<<<<<<<<<< * samples[partition_end] = samples[p] * samples[p] = tmp */ __pyx_v_tmp = (__pyx_v_samples[__pyx_v_partition_end]); /* "sklearn/_tree.pyx":1718 * * tmp = samples[partition_end] * samples[partition_end] = samples[p] # <<<<<<<<<<<<<< * samples[p] = tmp * */ (__pyx_v_samples[__pyx_v_partition_end]) = (__pyx_v_samples[__pyx_v_p]); /* "sklearn/_tree.pyx":1719 * tmp = samples[partition_end] * samples[partition_end] = samples[p] * samples[p] = tmp # <<<<<<<<<<<<<< * * current.pos = partition_end */ (__pyx_v_samples[__pyx_v_p]) = __pyx_v_tmp; } __pyx_L16:; } /* "sklearn/_tree.pyx":1721 * samples[p] = tmp * * current.pos = partition_end # <<<<<<<<<<<<<< * * # Reject if min_samples_leaf is not guaranteed */ __pyx_v_current.pos = __pyx_v_partition_end; /* "sklearn/_tree.pyx":1724 * * # Reject if min_samples_leaf is not guaranteed * if (((current.pos - start) < min_samples_leaf) or # <<<<<<<<<<<<<< * ((end - current.pos) < min_samples_leaf)): * continue */ __pyx_t_6 = (((__pyx_v_current.pos - __pyx_v_start) < __pyx_v_min_samples_leaf) != 0); if (!__pyx_t_6) { } else { __pyx_t_5 = __pyx_t_6; goto __pyx_L18_bool_binop_done; } /* "sklearn/_tree.pyx":1725 * # Reject if min_samples_leaf is not guaranteed * if (((current.pos - start) < min_samples_leaf) or * ((end - current.pos) < min_samples_leaf)): # <<<<<<<<<<<<<< * continue * */ __pyx_t_6 = (((__pyx_v_end - __pyx_v_current.pos) < __pyx_v_min_samples_leaf) != 0); __pyx_t_5 = __pyx_t_6; __pyx_L18_bool_binop_done:; if (__pyx_t_5) { /* "sklearn/_tree.pyx":1726 * if (((current.pos - start) < min_samples_leaf) or * ((end - current.pos) < min_samples_leaf)): * continue # <<<<<<<<<<<<<< * * # Evaluate split */ goto __pyx_L3_continue; } /* "sklearn/_tree.pyx":1729 * * # Evaluate split * self.criterion.reset() # <<<<<<<<<<<<<< * self.criterion.update(current.pos) * */ ((struct __pyx_vtabstruct_7sklearn_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion); /* "sklearn/_tree.pyx":1730 * # Evaluate split * self.criterion.reset() * self.criterion.update(current.pos) # <<<<<<<<<<<<<< * * # Reject if min_weight_leaf is not satisfied */ ((struct __pyx_vtabstruct_7sklearn_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->update(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_current.pos); /* "sklearn/_tree.pyx":1733 * * # Reject if min_weight_leaf is not satisfied * if ((self.criterion.weighted_n_left < min_weight_leaf) or # <<<<<<<<<<<<<< * (self.criterion.weighted_n_right < min_weight_leaf)): * continue */ __pyx_t_6 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_left < __pyx_v_min_weight_leaf) != 0); if (!__pyx_t_6) { } else { __pyx_t_5 = __pyx_t_6; goto __pyx_L21_bool_binop_done; } /* "sklearn/_tree.pyx":1734 * # Reject if min_weight_leaf is not satisfied * if ((self.criterion.weighted_n_left < min_weight_leaf) or * (self.criterion.weighted_n_right < min_weight_leaf)): # <<<<<<<<<<<<<< * continue * */ __pyx_t_6 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_right < __pyx_v_min_weight_leaf) != 0); __pyx_t_5 = __pyx_t_6; __pyx_L21_bool_binop_done:; if (__pyx_t_5) { /* "sklearn/_tree.pyx":1735 * if ((self.criterion.weighted_n_left < min_weight_leaf) or * (self.criterion.weighted_n_right < min_weight_leaf)): * continue # <<<<<<<<<<<<<< * * current.improvement = self.criterion.impurity_improvement(impurity) */ goto __pyx_L3_continue; } /* "sklearn/_tree.pyx":1737 * continue * * current.improvement = self.criterion.impurity_improvement(impurity) # <<<<<<<<<<<<<< * * if current.improvement > best.improvement: */ __pyx_v_current.improvement = ((struct __pyx_vtabstruct_7sklearn_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->impurity_improvement(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_impurity); /* "sklearn/_tree.pyx":1739 * current.improvement = self.criterion.impurity_improvement(impurity) * * if current.improvement > best.improvement: # <<<<<<<<<<<<<< * self.criterion.children_impurity(¤t.impurity_left, * ¤t.impurity_right) */ __pyx_t_5 = ((__pyx_v_current.improvement > __pyx_v_best.improvement) != 0); if (__pyx_t_5) { /* "sklearn/_tree.pyx":1740 * * if current.improvement > best.improvement: * self.criterion.children_impurity(¤t.impurity_left, # <<<<<<<<<<<<<< * ¤t.impurity_right) * best = current # copy */ ((struct __pyx_vtabstruct_7sklearn_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->children_impurity(__pyx_v_self->__pyx_base.__pyx_base.criterion, (&__pyx_v_current.impurity_left), (&__pyx_v_current.impurity_right)); /* "sklearn/_tree.pyx":1742 * self.criterion.children_impurity(¤t.impurity_left, * ¤t.impurity_right) * best = current # copy # <<<<<<<<<<<<<< * * # Reorganize into samples[start:best.pos] + samples[best.pos:end] */ __pyx_v_best = __pyx_v_current; goto __pyx_L23; } __pyx_L23:; } __pyx_L12:; } __pyx_L8:; __pyx_L3_continue:; } /* "sklearn/_tree.pyx":1745 * * # Reorganize into samples[start:best.pos] + samples[best.pos:end] * if best.pos < end and current.feature != best.feature: # <<<<<<<<<<<<<< * partition_end = end * p = start */ __pyx_t_6 = ((__pyx_v_best.pos < __pyx_v_end) != 0); if (__pyx_t_6) { } else { __pyx_t_5 = __pyx_t_6; goto __pyx_L25_bool_binop_done; } __pyx_t_6 = ((__pyx_v_current.feature != __pyx_v_best.feature) != 0); __pyx_t_5 = __pyx_t_6; __pyx_L25_bool_binop_done:; if (__pyx_t_5) { /* "sklearn/_tree.pyx":1746 * # Reorganize into samples[start:best.pos] + samples[best.pos:end] * if best.pos < end and current.feature != best.feature: * partition_end = end # <<<<<<<<<<<<<< * p = start * */ __pyx_v_partition_end = __pyx_v_end; /* "sklearn/_tree.pyx":1747 * if best.pos < end and current.feature != best.feature: * partition_end = end * p = start # <<<<<<<<<<<<<< * * while p < partition_end: */ __pyx_v_p = __pyx_v_start; /* "sklearn/_tree.pyx":1749 * p = start * * while p < partition_end: # <<<<<<<<<<<<<< * if X[X_sample_stride * samples[p] + * X_fx_stride * best.feature] <= best.threshold: */ while (1) { __pyx_t_5 = ((__pyx_v_p < __pyx_v_partition_end) != 0); if (!__pyx_t_5) break; /* "sklearn/_tree.pyx":1751 * while p < partition_end: * if X[X_sample_stride * samples[p] + * X_fx_stride * best.feature] <= best.threshold: # <<<<<<<<<<<<<< * p += 1 * */ __pyx_t_5 = (((__pyx_v_X[((__pyx_v_X_sample_stride * (__pyx_v_samples[__pyx_v_p])) + (__pyx_v_X_fx_stride * __pyx_v_best.feature))]) <= __pyx_v_best.threshold) != 0); if (__pyx_t_5) { /* "sklearn/_tree.pyx":1752 * if X[X_sample_stride * samples[p] + * X_fx_stride * best.feature] <= best.threshold: * p += 1 # <<<<<<<<<<<<<< * * else: */ __pyx_v_p = (__pyx_v_p + 1); goto __pyx_L29; } /*else*/ { /* "sklearn/_tree.pyx":1755 * * else: * partition_end -= 1 # <<<<<<<<<<<<<< * * tmp = samples[partition_end] */ __pyx_v_partition_end = (__pyx_v_partition_end - 1); /* "sklearn/_tree.pyx":1757 * partition_end -= 1 * * tmp = samples[partition_end] # <<<<<<<<<<<<<< * samples[partition_end] = samples[p] * samples[p] = tmp */ __pyx_v_tmp = (__pyx_v_samples[__pyx_v_partition_end]); /* "sklearn/_tree.pyx":1758 * * tmp = samples[partition_end] * samples[partition_end] = samples[p] # <<<<<<<<<<<<<< * samples[p] = tmp * */ (__pyx_v_samples[__pyx_v_partition_end]) = (__pyx_v_samples[__pyx_v_p]); /* "sklearn/_tree.pyx":1759 * tmp = samples[partition_end] * samples[partition_end] = samples[p] * samples[p] = tmp # <<<<<<<<<<<<<< * * # Respect invariant for constant features: the original order of */ (__pyx_v_samples[__pyx_v_p]) = __pyx_v_tmp; } __pyx_L29:; } goto __pyx_L24; } __pyx_L24:; /* "sklearn/_tree.pyx":1764 * # element in features[:n_known_constants] must be preserved for sibling * # and child nodes * memcpy(features, constant_features, sizeof(SIZE_t) * n_known_constants) # <<<<<<<<<<<<<< * * # Copy newly found constant features */ memcpy(__pyx_v_features, __pyx_v_constant_features, ((sizeof(__pyx_t_7sklearn_5_tree_SIZE_t)) * __pyx_v_n_known_constants)); /* "sklearn/_tree.pyx":1767 * * # Copy newly found constant features * memcpy(constant_features + n_known_constants, # <<<<<<<<<<<<<< * features + n_known_constants, * sizeof(SIZE_t) * n_found_constants) */ memcpy((__pyx_v_constant_features + __pyx_v_n_known_constants), (__pyx_v_features + __pyx_v_n_known_constants), ((sizeof(__pyx_t_7sklearn_5_tree_SIZE_t)) * __pyx_v_n_found_constants)); /* "sklearn/_tree.pyx":1772 * * # Return values * split[0] = best # <<<<<<<<<<<<<< * n_constant_features[0] = n_total_constants * */ (__pyx_v_split[0]) = __pyx_v_best; /* "sklearn/_tree.pyx":1773 * # Return values * split[0] = best * n_constant_features[0] = n_total_constants # <<<<<<<<<<<<<< * * */ (__pyx_v_n_constant_features[0]) = __pyx_v_n_total_constants; /* "sklearn/_tree.pyx":1583 * self.random_state), self.__getstate__()) * * cdef void node_split(self, double impurity, SplitRecord* split, # <<<<<<<<<<<<<< * SIZE_t* n_constant_features) nogil: * """Find the best random split on node samples[start:end].""" */ /* function exit code */ } /* "sklearn/_tree.pyx":1786 * cdef unsigned char* sample_mask * * def __cinit__(self, Criterion criterion, SIZE_t max_features, # <<<<<<<<<<<<<< * SIZE_t min_samples_leaf, * double min_weight_leaf, */ /* Python wrapper */ static int __pyx_pw_7sklearn_5_tree_19PresortBestSplitter_1__cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static int __pyx_pw_7sklearn_5_tree_19PresortBestSplitter_1__cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { CYTHON_UNUSED struct __pyx_obj_7sklearn_5_tree_Criterion *__pyx_v_criterion = 0; CYTHON_UNUSED __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_max_features; CYTHON_UNUSED __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_min_samples_leaf; CYTHON_UNUSED double __pyx_v_min_weight_leaf; CYTHON_UNUSED PyObject *__pyx_v_random_state = 0; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__cinit__ (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_criterion,&__pyx_n_s_max_features,&__pyx_n_s_min_samples_leaf,&__pyx_n_s_min_weight_leaf,&__pyx_n_s_random_state,0}; PyObject* values[5] = {0,0,0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_criterion)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; case 1: if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_max_features)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__cinit__", 1, 5, 5, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1786; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } case 2: if (likely((values[2] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_min_samples_leaf)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__cinit__", 1, 5, 5, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1786; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } case 3: if (likely((values[3] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_min_weight_leaf)) != 0)) kw_args--; 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__pyx_v_X_argsorted_stride = __pyx_t_2; /* "sklearn/_tree.pyx":1849 * cdef INT32_t* X_argsorted = self.X_argsorted_ptr * cdef SIZE_t X_argsorted_stride = self.X_argsorted_stride * cdef SIZE_t n_total_samples = self.n_total_samples # <<<<<<<<<<<<<< * cdef unsigned char* sample_mask = self.sample_mask * */ __pyx_t_2 = __pyx_v_self->n_total_samples; __pyx_v_n_total_samples = __pyx_t_2; /* "sklearn/_tree.pyx":1850 * cdef SIZE_t X_argsorted_stride = self.X_argsorted_stride * cdef SIZE_t n_total_samples = self.n_total_samples * cdef unsigned char* sample_mask = self.sample_mask # <<<<<<<<<<<<<< * * cdef SIZE_t max_features = self.max_features */ __pyx_t_5 = __pyx_v_self->sample_mask; __pyx_v_sample_mask = __pyx_t_5; /* "sklearn/_tree.pyx":1852 * cdef unsigned char* sample_mask = self.sample_mask * * cdef SIZE_t max_features = self.max_features # <<<<<<<<<<<<<< * cdef SIZE_t min_samples_leaf = self.min_samples_leaf * cdef double min_weight_leaf = self.min_weight_leaf */ __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.max_features; __pyx_v_max_features = __pyx_t_2; /* "sklearn/_tree.pyx":1853 * * cdef SIZE_t max_features = self.max_features * cdef SIZE_t min_samples_leaf = self.min_samples_leaf # <<<<<<<<<<<<<< * cdef double min_weight_leaf = self.min_weight_leaf * cdef UINT32_t* random_state = &self.rand_r_state */ __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.min_samples_leaf; __pyx_v_min_samples_leaf = __pyx_t_2; /* "sklearn/_tree.pyx":1854 * cdef SIZE_t max_features = self.max_features * cdef SIZE_t min_samples_leaf = self.min_samples_leaf * cdef double min_weight_leaf = self.min_weight_leaf # <<<<<<<<<<<<<< * cdef UINT32_t* random_state = &self.rand_r_state * */ __pyx_t_6 = __pyx_v_self->__pyx_base.__pyx_base.min_weight_leaf; __pyx_v_min_weight_leaf = __pyx_t_6; /* "sklearn/_tree.pyx":1855 * cdef SIZE_t min_samples_leaf = self.min_samples_leaf * cdef double min_weight_leaf = self.min_weight_leaf * cdef UINT32_t* random_state = &self.rand_r_state # <<<<<<<<<<<<<< * * cdef SplitRecord best, current */ __pyx_v_random_state = (&__pyx_v_self->__pyx_base.__pyx_base.rand_r_state); /* "sklearn/_tree.pyx":1859 * cdef SplitRecord best, current * * cdef SIZE_t f_i = n_features # <<<<<<<<<<<<<< * cdef SIZE_t f_j, p * # Number of features discovered to be constant during the split search */ __pyx_v_f_i = __pyx_v_n_features; /* "sklearn/_tree.pyx":1862 * cdef SIZE_t f_j, p * # Number of features discovered to be constant during the split search * cdef SIZE_t n_found_constants = 0 # <<<<<<<<<<<<<< * # Number of features known to be constant and drawn without replacement * cdef SIZE_t n_drawn_constants = 0 */ __pyx_v_n_found_constants = 0; /* "sklearn/_tree.pyx":1864 * cdef SIZE_t n_found_constants = 0 * # Number of features known to be constant and drawn without replacement * cdef SIZE_t n_drawn_constants = 0 # <<<<<<<<<<<<<< * cdef SIZE_t n_known_constants = n_constant_features[0] * # n_total_constants = n_known_constants + n_found_constants */ __pyx_v_n_drawn_constants = 0; /* "sklearn/_tree.pyx":1865 * # Number of features known to be constant and drawn without replacement * cdef SIZE_t n_drawn_constants = 0 * cdef SIZE_t n_known_constants = n_constant_features[0] # <<<<<<<<<<<<<< * # n_total_constants = n_known_constants + n_found_constants * cdef SIZE_t n_total_constants = n_known_constants */ __pyx_v_n_known_constants = (__pyx_v_n_constant_features[0]); /* "sklearn/_tree.pyx":1867 * cdef SIZE_t n_known_constants = n_constant_features[0] * # n_total_constants = n_known_constants + n_found_constants * cdef SIZE_t n_total_constants = n_known_constants # <<<<<<<<<<<<<< * cdef SIZE_t n_visited_features = 0 * cdef SIZE_t partition_end */ __pyx_v_n_total_constants = __pyx_v_n_known_constants; /* "sklearn/_tree.pyx":1868 * # n_total_constants = n_known_constants + n_found_constants * cdef SIZE_t n_total_constants = n_known_constants * cdef SIZE_t n_visited_features = 0 # <<<<<<<<<<<<<< * cdef SIZE_t partition_end * cdef SIZE_t i, j */ __pyx_v_n_visited_features = 0; /* "sklearn/_tree.pyx":1872 * cdef SIZE_t i, j * * _init_split(&best, end) # <<<<<<<<<<<<<< * * # Set sample mask */ __pyx_f_7sklearn_5_tree__init_split((&__pyx_v_best), __pyx_v_end); /* "sklearn/_tree.pyx":1875 * * # Set sample mask * for p in range(start, end): # <<<<<<<<<<<<<< * sample_mask[samples[p]] = 1 * */ __pyx_t_2 = __pyx_v_end; for (__pyx_t_7 = __pyx_v_start; __pyx_t_7 < __pyx_t_2; __pyx_t_7+=1) { __pyx_v_p = __pyx_t_7; /* "sklearn/_tree.pyx":1876 * # Set sample mask * for p in range(start, end): * sample_mask[samples[p]] = 1 # <<<<<<<<<<<<<< * * # Sample up to max_features without replacement using a */ (__pyx_v_sample_mask[(__pyx_v_samples[__pyx_v_p])]) = 1; } /* "sklearn/_tree.pyx":1887 * # newly discovered constant features to spare computation on descendant * # nodes. * while (f_i > n_total_constants and # Stop early if remaining features # <<<<<<<<<<<<<< * # are constant * (n_visited_features < max_features or */ while (1) { __pyx_t_9 = ((__pyx_v_f_i > __pyx_v_n_total_constants) != 0); if (__pyx_t_9) { } else { __pyx_t_8 = __pyx_t_9; goto __pyx_L7_bool_binop_done; } /* "sklearn/_tree.pyx":1889 * while (f_i > n_total_constants and # Stop early if remaining features * # are constant * (n_visited_features < max_features or # <<<<<<<<<<<<<< * # At least one drawn features must be non constant * n_visited_features <= n_found_constants + n_drawn_constants)): */ __pyx_t_9 = ((__pyx_v_n_visited_features < __pyx_v_max_features) != 0); if (!__pyx_t_9) { } else { __pyx_t_8 = __pyx_t_9; goto __pyx_L7_bool_binop_done; } /* "sklearn/_tree.pyx":1891 * (n_visited_features < max_features or * # At least one drawn features must be non constant * n_visited_features <= n_found_constants + n_drawn_constants)): # <<<<<<<<<<<<<< * n_visited_features += 1 * */ __pyx_t_9 = ((__pyx_v_n_visited_features <= (__pyx_v_n_found_constants + __pyx_v_n_drawn_constants)) != 0); __pyx_t_8 = __pyx_t_9; __pyx_L7_bool_binop_done:; if (!__pyx_t_8) break; /* "sklearn/_tree.pyx":1892 * # At least one drawn features must be non constant * n_visited_features <= n_found_constants + n_drawn_constants)): * n_visited_features += 1 # <<<<<<<<<<<<<< * * # Loop invariant: elements of features in */ __pyx_v_n_visited_features = (__pyx_v_n_visited_features + 1); /* "sklearn/_tree.pyx":1906 * * # Draw a feature at random * f_j = rand_int(n_drawn_constants, f_i - n_found_constants, # <<<<<<<<<<<<<< * random_state) * */ __pyx_v_f_j = __pyx_f_7sklearn_5_tree_rand_int(__pyx_v_n_drawn_constants, (__pyx_v_f_i - __pyx_v_n_found_constants), __pyx_v_random_state); /* "sklearn/_tree.pyx":1909 * random_state) * * if f_j < n_known_constants: # <<<<<<<<<<<<<< * # f_j is in [n_drawn_constants, n_known_constants[ * tmp = features[f_j] */ __pyx_t_8 = ((__pyx_v_f_j < __pyx_v_n_known_constants) != 0); if (__pyx_t_8) { /* "sklearn/_tree.pyx":1911 * if f_j < n_known_constants: * # f_j is in [n_drawn_constants, n_known_constants[ * tmp = features[f_j] # <<<<<<<<<<<<<< * features[f_j] = features[n_drawn_constants] * features[n_drawn_constants] = tmp */ __pyx_v_tmp = (__pyx_v_features[__pyx_v_f_j]); /* "sklearn/_tree.pyx":1912 * # f_j is in [n_drawn_constants, n_known_constants[ * tmp = features[f_j] * features[f_j] = features[n_drawn_constants] # <<<<<<<<<<<<<< * features[n_drawn_constants] = tmp * */ (__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_drawn_constants]); /* "sklearn/_tree.pyx":1913 * tmp = features[f_j] * features[f_j] = features[n_drawn_constants] * features[n_drawn_constants] = tmp # <<<<<<<<<<<<<< * * n_drawn_constants += 1 */ (__pyx_v_features[__pyx_v_n_drawn_constants]) = __pyx_v_tmp; /* "sklearn/_tree.pyx":1915 * features[n_drawn_constants] = tmp * * n_drawn_constants += 1 # <<<<<<<<<<<<<< * * else: */ __pyx_v_n_drawn_constants = (__pyx_v_n_drawn_constants + 1); goto __pyx_L10; } /*else*/ { /* "sklearn/_tree.pyx":1919 * else: * # f_j in the interval [n_known_constants, f_i - n_found_constants[ * f_j += n_found_constants # <<<<<<<<<<<<<< * # f_j in the interval [n_total_constants, f_i[ * */ __pyx_v_f_j = (__pyx_v_f_j + __pyx_v_n_found_constants); /* "sklearn/_tree.pyx":1922 * # f_j in the interval [n_total_constants, f_i[ * * current.feature = features[f_j] # <<<<<<<<<<<<<< * * # Extract ordering from X_argsorted */ __pyx_v_current.feature = (__pyx_v_features[__pyx_v_f_j]); /* "sklearn/_tree.pyx":1925 * * # Extract ordering from X_argsorted * p = start # <<<<<<<<<<<<<< * * for i in range(n_total_samples): */ __pyx_v_p = __pyx_v_start; /* "sklearn/_tree.pyx":1927 * p = start * * for i in range(n_total_samples): # <<<<<<<<<<<<<< * j = X_argsorted[X_argsorted_stride * current.feature + i] * if sample_mask[j] == 1: */ __pyx_t_2 = __pyx_v_n_total_samples; for (__pyx_t_7 = 0; __pyx_t_7 < __pyx_t_2; __pyx_t_7+=1) { __pyx_v_i = __pyx_t_7; /* "sklearn/_tree.pyx":1928 * * for i in range(n_total_samples): * j = X_argsorted[X_argsorted_stride * current.feature + i] # <<<<<<<<<<<<<< * if sample_mask[j] == 1: * samples[p] = j */ __pyx_v_j = (__pyx_v_X_argsorted[((__pyx_v_X_argsorted_stride * __pyx_v_current.feature) + __pyx_v_i)]); /* "sklearn/_tree.pyx":1929 * for i in range(n_total_samples): * j = X_argsorted[X_argsorted_stride * current.feature + i] * if sample_mask[j] == 1: # <<<<<<<<<<<<<< * samples[p] = j * Xf[p] = X[X_sample_stride * j + */ __pyx_t_8 = (((__pyx_v_sample_mask[__pyx_v_j]) == 1) != 0); if (__pyx_t_8) { /* "sklearn/_tree.pyx":1930 * j = X_argsorted[X_argsorted_stride * current.feature + i] * if sample_mask[j] == 1: * samples[p] = j # <<<<<<<<<<<<<< * Xf[p] = X[X_sample_stride * j + * X_fx_stride * current.feature] */ (__pyx_v_samples[__pyx_v_p]) = __pyx_v_j; /* "sklearn/_tree.pyx":1931 * if sample_mask[j] == 1: * samples[p] = j * Xf[p] = X[X_sample_stride * j + # <<<<<<<<<<<<<< * X_fx_stride * current.feature] * p += 1 */ (__pyx_v_Xf[__pyx_v_p]) = (__pyx_v_X[((__pyx_v_X_sample_stride * __pyx_v_j) + (__pyx_v_X_fx_stride * __pyx_v_current.feature))]); /* "sklearn/_tree.pyx":1933 * Xf[p] = X[X_sample_stride * j + * X_fx_stride * current.feature] * p += 1 # <<<<<<<<<<<<<< * * # Evaluate all splits */ __pyx_v_p = (__pyx_v_p + 1); goto __pyx_L13; } __pyx_L13:; } /* "sklearn/_tree.pyx":1936 * * # Evaluate all splits * if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD: # <<<<<<<<<<<<<< * features[f_j] = features[n_total_constants] * features[n_total_constants] = current.feature */ __pyx_t_8 = (((__pyx_v_Xf[(__pyx_v_end - 1)]) <= ((__pyx_v_Xf[__pyx_v_start]) + __pyx_v_7sklearn_5_tree_FEATURE_THRESHOLD)) != 0); if (__pyx_t_8) { /* "sklearn/_tree.pyx":1937 * # Evaluate all splits * if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD: * features[f_j] = features[n_total_constants] # <<<<<<<<<<<<<< * features[n_total_constants] = current.feature * */ (__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_total_constants]); /* "sklearn/_tree.pyx":1938 * if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD: * features[f_j] = features[n_total_constants] * features[n_total_constants] = current.feature # <<<<<<<<<<<<<< * * n_found_constants += 1 */ __pyx_t_2 = __pyx_v_current.feature; (__pyx_v_features[__pyx_v_n_total_constants]) = __pyx_t_2; /* "sklearn/_tree.pyx":1940 * features[n_total_constants] = current.feature * * n_found_constants += 1 # <<<<<<<<<<<<<< * n_total_constants += 1 * */ __pyx_v_n_found_constants = (__pyx_v_n_found_constants + 1); /* "sklearn/_tree.pyx":1941 * * n_found_constants += 1 * n_total_constants += 1 # <<<<<<<<<<<<<< * * else: */ __pyx_v_n_total_constants = (__pyx_v_n_total_constants + 1); goto __pyx_L14; } /*else*/ { /* "sklearn/_tree.pyx":1944 * * else: * f_i -= 1 # <<<<<<<<<<<<<< * features[f_i], features[f_j] = features[f_j], features[f_i] * */ __pyx_v_f_i = (__pyx_v_f_i - 1); /* "sklearn/_tree.pyx":1945 * else: * f_i -= 1 * features[f_i], features[f_j] = features[f_j], features[f_i] # <<<<<<<<<<<<<< * * self.criterion.reset() */ __pyx_t_2 = (__pyx_v_features[__pyx_v_f_j]); __pyx_t_7 = (__pyx_v_features[__pyx_v_f_i]); (__pyx_v_features[__pyx_v_f_i]) = __pyx_t_2; (__pyx_v_features[__pyx_v_f_j]) = __pyx_t_7; /* "sklearn/_tree.pyx":1947 * features[f_i], features[f_j] = features[f_j], features[f_i] * * self.criterion.reset() # <<<<<<<<<<<<<< * p = start * */ ((struct __pyx_vtabstruct_7sklearn_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion); /* "sklearn/_tree.pyx":1948 * * self.criterion.reset() * p = start # <<<<<<<<<<<<<< * * while p < end: */ __pyx_v_p = __pyx_v_start; /* "sklearn/_tree.pyx":1950 * p = start * * while p < end: # <<<<<<<<<<<<<< * while (p + 1 < end and * Xf[p + 1] <= Xf[p] + FEATURE_THRESHOLD): */ while (1) { __pyx_t_8 = ((__pyx_v_p < __pyx_v_end) != 0); if (!__pyx_t_8) break; /* "sklearn/_tree.pyx":1951 * * while p < end: * while (p + 1 < end and # <<<<<<<<<<<<<< * Xf[p + 1] <= Xf[p] + FEATURE_THRESHOLD): * p += 1 */ while (1) { __pyx_t_9 = (((__pyx_v_p + 1) < __pyx_v_end) != 0); if (__pyx_t_9) { } else { __pyx_t_8 = __pyx_t_9; goto __pyx_L19_bool_binop_done; } /* "sklearn/_tree.pyx":1952 * while p < end: * while (p + 1 < end and * Xf[p + 1] <= Xf[p] + FEATURE_THRESHOLD): # <<<<<<<<<<<<<< * p += 1 * */ __pyx_t_9 = (((__pyx_v_Xf[(__pyx_v_p + 1)]) <= ((__pyx_v_Xf[__pyx_v_p]) + __pyx_v_7sklearn_5_tree_FEATURE_THRESHOLD)) != 0); __pyx_t_8 = __pyx_t_9; __pyx_L19_bool_binop_done:; if (!__pyx_t_8) break; /* "sklearn/_tree.pyx":1953 * while (p + 1 < end and * Xf[p + 1] <= Xf[p] + FEATURE_THRESHOLD): * p += 1 # <<<<<<<<<<<<<< * * # (p + 1 >= end) or (X[samples[p + 1], current.feature] > */ __pyx_v_p = (__pyx_v_p + 1); } /* "sklearn/_tree.pyx":1957 * # (p + 1 >= end) or (X[samples[p + 1], current.feature] > * # X[samples[p], current.feature]) * p += 1 # <<<<<<<<<<<<<< * # (p >= end) or (X[samples[p], current.feature] > * # X[samples[p - 1], current.feature]) */ __pyx_v_p = (__pyx_v_p + 1); /* "sklearn/_tree.pyx":1961 * # X[samples[p - 1], current.feature]) * * if p < end: # <<<<<<<<<<<<<< * current.pos = p * */ __pyx_t_8 = ((__pyx_v_p < __pyx_v_end) != 0); if (__pyx_t_8) { /* "sklearn/_tree.pyx":1962 * * if p < end: * current.pos = p # <<<<<<<<<<<<<< * * # Reject if min_samples_leaf is not guaranteed */ __pyx_v_current.pos = __pyx_v_p; /* "sklearn/_tree.pyx":1965 * * # Reject if min_samples_leaf is not guaranteed * if (((current.pos - start) < min_samples_leaf) or # <<<<<<<<<<<<<< * ((end - current.pos) < min_samples_leaf)): * continue */ __pyx_t_9 = (((__pyx_v_current.pos - __pyx_v_start) < __pyx_v_min_samples_leaf) != 0); if (!__pyx_t_9) { } else { __pyx_t_8 = __pyx_t_9; goto __pyx_L23_bool_binop_done; } /* "sklearn/_tree.pyx":1966 * # Reject if min_samples_leaf is not guaranteed * if (((current.pos - start) < min_samples_leaf) or * ((end - current.pos) < min_samples_leaf)): # <<<<<<<<<<<<<< * continue * */ __pyx_t_9 = (((__pyx_v_end - __pyx_v_current.pos) < __pyx_v_min_samples_leaf) != 0); __pyx_t_8 = __pyx_t_9; __pyx_L23_bool_binop_done:; if (__pyx_t_8) { /* "sklearn/_tree.pyx":1967 * if (((current.pos - start) < min_samples_leaf) or * ((end - current.pos) < min_samples_leaf)): * continue # <<<<<<<<<<<<<< * * self.criterion.update(current.pos) */ goto __pyx_L15_continue; } /* "sklearn/_tree.pyx":1969 * continue * * self.criterion.update(current.pos) # <<<<<<<<<<<<<< * * # Reject if min_weight_leaf is not satisfied */ ((struct __pyx_vtabstruct_7sklearn_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->update(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_current.pos); /* "sklearn/_tree.pyx":1972 * * # Reject if min_weight_leaf is not satisfied * if ((self.criterion.weighted_n_left < min_weight_leaf) or # <<<<<<<<<<<<<< * (self.criterion.weighted_n_right < min_weight_leaf)): * continue */ __pyx_t_9 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_left < __pyx_v_min_weight_leaf) != 0); if (!__pyx_t_9) { } else { __pyx_t_8 = __pyx_t_9; goto __pyx_L26_bool_binop_done; } /* "sklearn/_tree.pyx":1973 * # Reject if min_weight_leaf is not satisfied * if ((self.criterion.weighted_n_left < min_weight_leaf) or * (self.criterion.weighted_n_right < min_weight_leaf)): # <<<<<<<<<<<<<< * continue * */ __pyx_t_9 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_right < __pyx_v_min_weight_leaf) != 0); __pyx_t_8 = __pyx_t_9; __pyx_L26_bool_binop_done:; if (__pyx_t_8) { /* "sklearn/_tree.pyx":1974 * if ((self.criterion.weighted_n_left < min_weight_leaf) or * (self.criterion.weighted_n_right < min_weight_leaf)): * continue # <<<<<<<<<<<<<< * * current.improvement = self.criterion.impurity_improvement(impurity) */ goto __pyx_L15_continue; } /* "sklearn/_tree.pyx":1976 * continue * * current.improvement = self.criterion.impurity_improvement(impurity) # <<<<<<<<<<<<<< * * if current.improvement > best.improvement: */ __pyx_v_current.improvement = ((struct __pyx_vtabstruct_7sklearn_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->impurity_improvement(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_impurity); /* "sklearn/_tree.pyx":1978 * current.improvement = self.criterion.impurity_improvement(impurity) * * if current.improvement > best.improvement: # <<<<<<<<<<<<<< * self.criterion.children_impurity(¤t.impurity_left, * ¤t.impurity_right) */ __pyx_t_8 = ((__pyx_v_current.improvement > __pyx_v_best.improvement) != 0); if (__pyx_t_8) { /* "sklearn/_tree.pyx":1979 * * if current.improvement > best.improvement: * self.criterion.children_impurity(¤t.impurity_left, # <<<<<<<<<<<<<< * ¤t.impurity_right) * */ ((struct __pyx_vtabstruct_7sklearn_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->children_impurity(__pyx_v_self->__pyx_base.__pyx_base.criterion, (&__pyx_v_current.impurity_left), (&__pyx_v_current.impurity_right)); /* "sklearn/_tree.pyx":1982 * ¤t.impurity_right) * * current.threshold = (Xf[p - 1] + Xf[p]) / 2.0 # <<<<<<<<<<<<<< * if current.threshold == Xf[p]: * current.threshold = Xf[p - 1] */ __pyx_v_current.threshold = (((__pyx_v_Xf[(__pyx_v_p - 1)]) + (__pyx_v_Xf[__pyx_v_p])) / 2.0); /* "sklearn/_tree.pyx":1983 * * current.threshold = (Xf[p - 1] + Xf[p]) / 2.0 * if current.threshold == Xf[p]: # <<<<<<<<<<<<<< * current.threshold = Xf[p - 1] * */ __pyx_t_8 = ((__pyx_v_current.threshold == (__pyx_v_Xf[__pyx_v_p])) != 0); if (__pyx_t_8) { /* "sklearn/_tree.pyx":1984 * current.threshold = (Xf[p - 1] + Xf[p]) / 2.0 * if current.threshold == Xf[p]: * current.threshold = Xf[p - 1] # <<<<<<<<<<<<<< * * best = current # copy */ __pyx_v_current.threshold = (__pyx_v_Xf[(__pyx_v_p - 1)]); goto __pyx_L29; } __pyx_L29:; /* "sklearn/_tree.pyx":1986 * current.threshold = Xf[p - 1] * * best = current # copy # <<<<<<<<<<<<<< * * # Reorganize into samples[start:best.pos] + samples[best.pos:end] */ __pyx_v_best = __pyx_v_current; goto __pyx_L28; } __pyx_L28:; goto __pyx_L21; } __pyx_L21:; __pyx_L15_continue:; } } __pyx_L14:; } __pyx_L10:; } /* "sklearn/_tree.pyx":1989 * * # Reorganize into samples[start:best.pos] + samples[best.pos:end] * if best.pos < end: # <<<<<<<<<<<<<< * partition_end = end * p = start */ __pyx_t_8 = ((__pyx_v_best.pos < __pyx_v_end) != 0); 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/* "sklearn/_tree.pyx":2121 * partition_end -= 1 * * Xf[p] = Xf[partition_end] # <<<<<<<<<<<<<< * Xf[partition_end] = value * sparse_swap(index_to_samples, samples, p, partition_end) */ (__pyx_v_Xf[__pyx_v_p]) = (__pyx_v_Xf[__pyx_v_partition_end]); /* "sklearn/_tree.pyx":2122 * * Xf[p] = Xf[partition_end] * Xf[partition_end] = value # <<<<<<<<<<<<<< * sparse_swap(index_to_samples, samples, p, partition_end) * */ (__pyx_v_Xf[__pyx_v_partition_end]) = __pyx_v_value; /* "sklearn/_tree.pyx":2123 * Xf[p] = Xf[partition_end] * Xf[partition_end] = value * sparse_swap(index_to_samples, samples, p, partition_end) # <<<<<<<<<<<<<< * * return partition_end */ __pyx_f_7sklearn_5_tree_sparse_swap(__pyx_v_index_to_samples, __pyx_v_samples, __pyx_v_p, __pyx_v_partition_end); } __pyx_L6:; } /* "sklearn/_tree.pyx":2125 * sparse_swap(index_to_samples, samples, p, partition_end) * * return partition_end # <<<<<<<<<<<<<< * * cdef inline void extract_nnz(self, SIZE_t feature, */ __pyx_r = __pyx_v_partition_end; goto __pyx_L0; /* "sklearn/_tree.pyx":2089 * index_to_samples[samples[p]] = p * * cdef inline SIZE_t _partition(self, double threshold, # <<<<<<<<<<<<<< * SIZE_t end_negative, SIZE_t start_positive, * SIZE_t zero_pos) nogil: */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "sklearn/_tree.pyx":2127 * return partition_end * * cdef inline void extract_nnz(self, SIZE_t feature, # <<<<<<<<<<<<<< * SIZE_t* end_negative, SIZE_t* start_positive, * bint* is_samples_sorted) nogil: */ static CYTHON_INLINE void __pyx_f_7sklearn_5_tree_18BaseSparseSplitter_extract_nnz(struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter *__pyx_v_self, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_feature, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_end_negative, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_start_positive, int *__pyx_v_is_samples_sorted) { __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_indptr_start; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_indptr_end; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_indices; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_samples; int __pyx_t_1; /* "sklearn/_tree.pyx":2159 * * """ * cdef SIZE_t indptr_start = self.X_indptr[feature], # <<<<<<<<<<<<<< * cdef SIZE_t indptr_end = self.X_indptr[feature + 1] * cdef SIZE_t n_indices = (indptr_end - indptr_start) */ __pyx_v_indptr_start = (__pyx_v_self->X_indptr[__pyx_v_feature]); /* "sklearn/_tree.pyx":2160 * """ * cdef SIZE_t indptr_start = self.X_indptr[feature], * cdef SIZE_t indptr_end = self.X_indptr[feature + 1] # <<<<<<<<<<<<<< * cdef SIZE_t n_indices = (indptr_end - indptr_start) * cdef SIZE_t n_samples = self.end - self.start */ __pyx_v_indptr_end = (__pyx_v_self->X_indptr[(__pyx_v_feature + 1)]); /* "sklearn/_tree.pyx":2161 * cdef SIZE_t indptr_start = self.X_indptr[feature], * cdef SIZE_t indptr_end = self.X_indptr[feature + 1] * cdef SIZE_t n_indices = (indptr_end - indptr_start) # <<<<<<<<<<<<<< * cdef SIZE_t n_samples = self.end - self.start * */ __pyx_v_n_indices = ((__pyx_t_7sklearn_5_tree_SIZE_t)(__pyx_v_indptr_end - __pyx_v_indptr_start)); /* "sklearn/_tree.pyx":2162 * cdef SIZE_t indptr_end = self.X_indptr[feature + 1] * cdef SIZE_t n_indices = (indptr_end - indptr_start) * cdef SIZE_t n_samples = self.end - self.start # <<<<<<<<<<<<<< * * # Use binary search if n_samples * log(n_indices) < */ __pyx_v_n_samples = (__pyx_v_self->__pyx_base.end - __pyx_v_self->__pyx_base.start); /* "sklearn/_tree.pyx":2170 * # approach. * if ((1 - is_samples_sorted[0]) * n_samples * log(n_samples) + * n_samples * log(n_indices) < EXTRACT_NNZ_SWITCH * n_indices): # <<<<<<<<<<<<<< * extract_nnz_binary_search(self.X_indices, self.X_data, * indptr_start, indptr_end, */ __pyx_t_1 = ((((((1 - (__pyx_v_is_samples_sorted[0])) * __pyx_v_n_samples) * __pyx_f_7sklearn_5_tree_log(__pyx_v_n_samples)) + (__pyx_v_n_samples * __pyx_f_7sklearn_5_tree_log(__pyx_v_n_indices))) < (__pyx_v_7sklearn_5_tree_EXTRACT_NNZ_SWITCH * __pyx_v_n_indices)) != 0); if (__pyx_t_1) { /* "sklearn/_tree.pyx":2171 * if ((1 - is_samples_sorted[0]) * n_samples * log(n_samples) + * n_samples * log(n_indices) < EXTRACT_NNZ_SWITCH * n_indices): * extract_nnz_binary_search(self.X_indices, self.X_data, # <<<<<<<<<<<<<< * indptr_start, indptr_end, * self.samples, self.start, self.end, */ __pyx_f_7sklearn_5_tree_extract_nnz_binary_search(__pyx_v_self->X_indices, __pyx_v_self->X_data, __pyx_v_indptr_start, __pyx_v_indptr_end, __pyx_v_self->__pyx_base.samples, __pyx_v_self->__pyx_base.start, __pyx_v_self->__pyx_base.end, __pyx_v_self->index_to_samples, __pyx_v_self->__pyx_base.feature_values, __pyx_v_end_negative, __pyx_v_start_positive, __pyx_v_self->sorted_samples, __pyx_v_is_samples_sorted); goto __pyx_L3; } /*else*/ { /* "sklearn/_tree.pyx":2182 * # index_to_samples is a mapping from X_indices to samples * else: * extract_nnz_index_to_samples(self.X_indices, self.X_data, # <<<<<<<<<<<<<< * indptr_start, indptr_end, * self.samples, self.start, self.end, */ __pyx_f_7sklearn_5_tree_extract_nnz_index_to_samples(__pyx_v_self->X_indices, __pyx_v_self->X_data, __pyx_v_indptr_start, __pyx_v_indptr_end, __pyx_v_self->__pyx_base.samples, __pyx_v_self->__pyx_base.start, __pyx_v_self->__pyx_base.end, __pyx_v_self->index_to_samples, __pyx_v_self->__pyx_base.feature_values, __pyx_v_end_negative, __pyx_v_start_positive); } __pyx_L3:; /* "sklearn/_tree.pyx":2127 * return partition_end * * cdef inline void extract_nnz(self, SIZE_t feature, # <<<<<<<<<<<<<< * SIZE_t* end_negative, SIZE_t* start_positive, * bint* is_samples_sorted) nogil: */ /* function exit code */ } /* "sklearn/_tree.pyx":2190 * * * cdef int compare_SIZE_t(const void* a, const void* b) nogil: # <<<<<<<<<<<<<< * """Comparison function for sort""" * return ((a)[0] - (b)[0]) */ static int __pyx_f_7sklearn_5_tree_compare_SIZE_t(void const *__pyx_v_a, void const *__pyx_v_b) { int __pyx_r; /* "sklearn/_tree.pyx":2192 * cdef int compare_SIZE_t(const void* a, const void* b) nogil: * """Comparison function for sort""" * return ((a)[0] - (b)[0]) # <<<<<<<<<<<<<< * * */ __pyx_r = ((int)((((__pyx_t_7sklearn_5_tree_SIZE_t *)__pyx_v_a)[0]) - (((__pyx_t_7sklearn_5_tree_SIZE_t *)__pyx_v_b)[0]))); goto __pyx_L0; /* "sklearn/_tree.pyx":2190 * * * cdef int compare_SIZE_t(const void* a, const void* b) nogil: # <<<<<<<<<<<<<< * """Comparison function for sort""" * return ((a)[0] - (b)[0]) */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "sklearn/_tree.pyx":2195 * * * cdef inline void binary_search(INT32_t* sorted_array, # <<<<<<<<<<<<<< * INT32_t start, INT32_t end, * SIZE_t value, SIZE_t* index, */ static CYTHON_INLINE void __pyx_f_7sklearn_5_tree_binary_search(__pyx_t_7sklearn_5_tree_INT32_t *__pyx_v_sorted_array, __pyx_t_7sklearn_5_tree_INT32_t __pyx_v_start, __pyx_t_7sklearn_5_tree_INT32_t __pyx_v_end, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_value, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_index, __pyx_t_7sklearn_5_tree_INT32_t *__pyx_v_new_start) { __pyx_t_7sklearn_5_tree_INT32_t __pyx_v_pivot; int __pyx_t_1; /* "sklearn/_tree.pyx":2204 * """ * cdef INT32_t pivot * index[0] = -1 # <<<<<<<<<<<<<< * while start < end: * pivot = start + (end - start) / 2 */ (__pyx_v_index[0]) = -1; /* "sklearn/_tree.pyx":2205 * cdef INT32_t pivot * index[0] = -1 * while start < end: # <<<<<<<<<<<<<< * pivot = start + (end - start) / 2 * */ while (1) { __pyx_t_1 = ((__pyx_v_start < __pyx_v_end) != 0); if (!__pyx_t_1) break; /* "sklearn/_tree.pyx":2206 * index[0] = -1 * while start < end: * pivot = start + (end - start) / 2 # <<<<<<<<<<<<<< * * if sorted_array[pivot] == value: */ __pyx_v_pivot = (__pyx_v_start + ((__pyx_v_end - __pyx_v_start) / 2)); /* "sklearn/_tree.pyx":2208 * pivot = start + (end - start) / 2 * * if sorted_array[pivot] == value: # <<<<<<<<<<<<<< * index[0] = pivot * start = pivot + 1 */ __pyx_t_1 = (((__pyx_v_sorted_array[__pyx_v_pivot]) == __pyx_v_value) != 0); if (__pyx_t_1) { /* "sklearn/_tree.pyx":2209 * * if sorted_array[pivot] == value: * index[0] = pivot # <<<<<<<<<<<<<< * start = pivot + 1 * break */ (__pyx_v_index[0]) = __pyx_v_pivot; /* "sklearn/_tree.pyx":2210 * if sorted_array[pivot] == value: * index[0] = pivot * start = pivot + 1 # <<<<<<<<<<<<<< * break * */ __pyx_v_start = (__pyx_v_pivot + 1); /* "sklearn/_tree.pyx":2211 * index[0] = pivot * start = pivot + 1 * break # <<<<<<<<<<<<<< * * if sorted_array[pivot] < value: */ goto __pyx_L4_break; } /* "sklearn/_tree.pyx":2213 * break * * if sorted_array[pivot] < value: # <<<<<<<<<<<<<< * start = pivot + 1 * else: */ __pyx_t_1 = (((__pyx_v_sorted_array[__pyx_v_pivot]) < __pyx_v_value) != 0); if (__pyx_t_1) { /* "sklearn/_tree.pyx":2214 * * if sorted_array[pivot] < value: * start = pivot + 1 # <<<<<<<<<<<<<< * else: * end = pivot */ __pyx_v_start = (__pyx_v_pivot + 1); goto __pyx_L6; } /*else*/ { /* "sklearn/_tree.pyx":2216 * start = pivot + 1 * else: * end = pivot # <<<<<<<<<<<<<< * new_start[0] = start * */ __pyx_v_end = __pyx_v_pivot; } __pyx_L6:; } __pyx_L4_break:; /* "sklearn/_tree.pyx":2217 * else: * end = pivot * new_start[0] = start # <<<<<<<<<<<<<< * * */ (__pyx_v_new_start[0]) = __pyx_v_start; /* "sklearn/_tree.pyx":2195 * * * cdef inline void binary_search(INT32_t* sorted_array, # <<<<<<<<<<<<<< * INT32_t start, INT32_t end, * SIZE_t value, SIZE_t* index, */ /* function exit code */ } /* "sklearn/_tree.pyx":2220 * * * cdef inline void extract_nnz_index_to_samples(INT32_t* X_indices, # <<<<<<<<<<<<<< * DTYPE_t* X_data, * INT32_t indptr_start, */ static CYTHON_INLINE void __pyx_f_7sklearn_5_tree_extract_nnz_index_to_samples(__pyx_t_7sklearn_5_tree_INT32_t *__pyx_v_X_indices, __pyx_t_7sklearn_5_tree_DTYPE_t *__pyx_v_X_data, __pyx_t_7sklearn_5_tree_INT32_t __pyx_v_indptr_start, __pyx_t_7sklearn_5_tree_INT32_t __pyx_v_indptr_end, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_start, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_end, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_index_to_samples, __pyx_t_7sklearn_5_tree_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_end_negative, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_start_positive) { __pyx_t_7sklearn_5_tree_INT32_t __pyx_v_k; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_index; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_end_negative_; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_start_positive_; __pyx_t_7sklearn_5_tree_INT32_t __pyx_t_1; __pyx_t_7sklearn_5_tree_INT32_t __pyx_t_2; int __pyx_t_3; int __pyx_t_4; /* "sklearn/_tree.pyx":2237 * cdef INT32_t k * cdef SIZE_t index * cdef SIZE_t end_negative_ = start # <<<<<<<<<<<<<< * cdef SIZE_t start_positive_ = end * */ __pyx_v_end_negative_ = __pyx_v_start; /* "sklearn/_tree.pyx":2238 * cdef SIZE_t index * cdef SIZE_t end_negative_ = start * cdef SIZE_t start_positive_ = end # <<<<<<<<<<<<<< * * for k in range(indptr_start, indptr_end): */ __pyx_v_start_positive_ = __pyx_v_end; /* "sklearn/_tree.pyx":2240 * cdef SIZE_t start_positive_ = end * * for k in range(indptr_start, indptr_end): # <<<<<<<<<<<<<< * if start <= index_to_samples[X_indices[k]] < end: * if X_data[k] > 0: */ __pyx_t_1 = __pyx_v_indptr_end; for (__pyx_t_2 = __pyx_v_indptr_start; __pyx_t_2 < __pyx_t_1; __pyx_t_2+=1) { __pyx_v_k = __pyx_t_2; /* "sklearn/_tree.pyx":2241 * * for k in range(indptr_start, indptr_end): * if start <= index_to_samples[X_indices[k]] < end: # <<<<<<<<<<<<<< * if X_data[k] > 0: * start_positive_ -= 1 */ __pyx_t_3 = (__pyx_v_start <= (__pyx_v_index_to_samples[(__pyx_v_X_indices[__pyx_v_k])])); if (__pyx_t_3) { __pyx_t_3 = ((__pyx_v_index_to_samples[(__pyx_v_X_indices[__pyx_v_k])]) < __pyx_v_end); } __pyx_t_4 = (__pyx_t_3 != 0); if (__pyx_t_4) { /* "sklearn/_tree.pyx":2242 * for k in range(indptr_start, indptr_end): * if start <= index_to_samples[X_indices[k]] < end: * if X_data[k] > 0: # <<<<<<<<<<<<<< * start_positive_ -= 1 * Xf[start_positive_] = X_data[k] */ __pyx_t_4 = (((__pyx_v_X_data[__pyx_v_k]) > 0.0) != 0); if (__pyx_t_4) { /* "sklearn/_tree.pyx":2243 * if start <= index_to_samples[X_indices[k]] < end: * if X_data[k] > 0: * start_positive_ -= 1 # <<<<<<<<<<<<<< * Xf[start_positive_] = X_data[k] * index = index_to_samples[X_indices[k]] */ __pyx_v_start_positive_ = (__pyx_v_start_positive_ - 1); /* "sklearn/_tree.pyx":2244 * if X_data[k] > 0: * start_positive_ -= 1 * Xf[start_positive_] = X_data[k] # <<<<<<<<<<<<<< * index = index_to_samples[X_indices[k]] * sparse_swap(index_to_samples, samples, index, start_positive_) */ (__pyx_v_Xf[__pyx_v_start_positive_]) = (__pyx_v_X_data[__pyx_v_k]); /* "sklearn/_tree.pyx":2245 * start_positive_ -= 1 * Xf[start_positive_] = X_data[k] * index = index_to_samples[X_indices[k]] # <<<<<<<<<<<<<< * sparse_swap(index_to_samples, samples, index, start_positive_) * */ __pyx_v_index = (__pyx_v_index_to_samples[(__pyx_v_X_indices[__pyx_v_k])]); /* "sklearn/_tree.pyx":2246 * Xf[start_positive_] = X_data[k] * index = index_to_samples[X_indices[k]] * sparse_swap(index_to_samples, samples, index, start_positive_) # <<<<<<<<<<<<<< * * */ __pyx_f_7sklearn_5_tree_sparse_swap(__pyx_v_index_to_samples, __pyx_v_samples, __pyx_v_index, __pyx_v_start_positive_); goto __pyx_L6; } /* "sklearn/_tree.pyx":2249 * * * elif X_data[k] < 0: # <<<<<<<<<<<<<< * Xf[end_negative_] = X_data[k] * index = index_to_samples[X_indices[k]] */ __pyx_t_4 = (((__pyx_v_X_data[__pyx_v_k]) < 0.0) != 0); if (__pyx_t_4) { /* "sklearn/_tree.pyx":2250 * * elif X_data[k] < 0: * Xf[end_negative_] = X_data[k] # <<<<<<<<<<<<<< * index = index_to_samples[X_indices[k]] * sparse_swap(index_to_samples, samples, index, end_negative_) */ (__pyx_v_Xf[__pyx_v_end_negative_]) = (__pyx_v_X_data[__pyx_v_k]); /* "sklearn/_tree.pyx":2251 * elif X_data[k] < 0: * Xf[end_negative_] = X_data[k] * index = index_to_samples[X_indices[k]] # <<<<<<<<<<<<<< * sparse_swap(index_to_samples, samples, index, end_negative_) * end_negative_ += 1 */ __pyx_v_index = (__pyx_v_index_to_samples[(__pyx_v_X_indices[__pyx_v_k])]); /* "sklearn/_tree.pyx":2252 * Xf[end_negative_] = X_data[k] * index = index_to_samples[X_indices[k]] * sparse_swap(index_to_samples, samples, index, end_negative_) # <<<<<<<<<<<<<< * end_negative_ += 1 * */ __pyx_f_7sklearn_5_tree_sparse_swap(__pyx_v_index_to_samples, __pyx_v_samples, __pyx_v_index, __pyx_v_end_negative_); /* "sklearn/_tree.pyx":2253 * index = index_to_samples[X_indices[k]] * sparse_swap(index_to_samples, samples, index, end_negative_) * end_negative_ += 1 # <<<<<<<<<<<<<< * * # Returned values */ __pyx_v_end_negative_ = (__pyx_v_end_negative_ + 1); goto __pyx_L6; } __pyx_L6:; goto __pyx_L5; } __pyx_L5:; } /* "sklearn/_tree.pyx":2256 * * # Returned values * end_negative[0] = end_negative_ # <<<<<<<<<<<<<< * start_positive[0] = start_positive_ * */ (__pyx_v_end_negative[0]) = __pyx_v_end_negative_; /* "sklearn/_tree.pyx":2257 * # Returned values * end_negative[0] = end_negative_ * start_positive[0] = start_positive_ # <<<<<<<<<<<<<< * * */ (__pyx_v_start_positive[0]) = __pyx_v_start_positive_; /* "sklearn/_tree.pyx":2220 * * * cdef inline void extract_nnz_index_to_samples(INT32_t* X_indices, # <<<<<<<<<<<<<< * DTYPE_t* X_data, * INT32_t indptr_start, */ /* function exit code */ } /* "sklearn/_tree.pyx":2260 * * * cdef inline void extract_nnz_binary_search(INT32_t* X_indices, # <<<<<<<<<<<<<< * DTYPE_t* X_data, * INT32_t indptr_start, */ static CYTHON_INLINE void __pyx_f_7sklearn_5_tree_extract_nnz_binary_search(__pyx_t_7sklearn_5_tree_INT32_t *__pyx_v_X_indices, __pyx_t_7sklearn_5_tree_DTYPE_t *__pyx_v_X_data, __pyx_t_7sklearn_5_tree_INT32_t __pyx_v_indptr_start, __pyx_t_7sklearn_5_tree_INT32_t __pyx_v_indptr_end, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_start, __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_end, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_index_to_samples, __pyx_t_7sklearn_5_tree_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_end_negative, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_start_positive, __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_sorted_samples, int *__pyx_v_is_samples_sorted) { __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_samples; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_p; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_index; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_k; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_end_negative_; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_start_positive_; int __pyx_t_1; int __pyx_t_2; /* "sklearn/_tree.pyx":2283 * cdef SIZE_t n_samples * * if not is_samples_sorted[0]: # <<<<<<<<<<<<<< * n_samples = end - start * memcpy(sorted_samples + start, samples + start, */ __pyx_t_1 = ((!((__pyx_v_is_samples_sorted[0]) != 0)) != 0); if (__pyx_t_1) { /* "sklearn/_tree.pyx":2284 * * if not is_samples_sorted[0]: * n_samples = end - start # <<<<<<<<<<<<<< * memcpy(sorted_samples + start, samples + start, * n_samples * sizeof(SIZE_t)) */ __pyx_v_n_samples = (__pyx_v_end - __pyx_v_start); /* "sklearn/_tree.pyx":2285 * if not is_samples_sorted[0]: * n_samples = end - start * memcpy(sorted_samples + start, samples + start, # <<<<<<<<<<<<<< * n_samples * sizeof(SIZE_t)) * qsort(sorted_samples + start, n_samples, sizeof(SIZE_t), */ memcpy((__pyx_v_sorted_samples + __pyx_v_start), (__pyx_v_samples + __pyx_v_start), (__pyx_v_n_samples * (sizeof(__pyx_t_7sklearn_5_tree_SIZE_t)))); /* "sklearn/_tree.pyx":2287 * memcpy(sorted_samples + start, samples + start, * n_samples * sizeof(SIZE_t)) * qsort(sorted_samples + start, n_samples, sizeof(SIZE_t), # <<<<<<<<<<<<<< * compare_SIZE_t) * is_samples_sorted[0] = 1 */ qsort((__pyx_v_sorted_samples + __pyx_v_start), __pyx_v_n_samples, (sizeof(__pyx_t_7sklearn_5_tree_SIZE_t)), __pyx_f_7sklearn_5_tree_compare_SIZE_t); /* "sklearn/_tree.pyx":2289 * qsort(sorted_samples + start, n_samples, sizeof(SIZE_t), * compare_SIZE_t) * is_samples_sorted[0] = 1 # <<<<<<<<<<<<<< * * while (indptr_start < indptr_end and */ (__pyx_v_is_samples_sorted[0]) = 1; goto __pyx_L3; } __pyx_L3:; /* "sklearn/_tree.pyx":2291 * is_samples_sorted[0] = 1 * * while (indptr_start < indptr_end and # <<<<<<<<<<<<<< * sorted_samples[start] > X_indices[indptr_start]): * indptr_start += 1 */ while (1) { __pyx_t_2 = ((__pyx_v_indptr_start < __pyx_v_indptr_end) != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L6_bool_binop_done; } /* "sklearn/_tree.pyx":2292 * * while (indptr_start < indptr_end and * sorted_samples[start] > X_indices[indptr_start]): # <<<<<<<<<<<<<< * indptr_start += 1 * */ __pyx_t_2 = (((__pyx_v_sorted_samples[__pyx_v_start]) > (__pyx_v_X_indices[__pyx_v_indptr_start])) != 0); 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CYTHON_UNUSED __pyx_t_7sklearn_5_tree_DTYPE_t *__pyx_v_X_data; __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_features; __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_constant_features; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_features; __pyx_t_7sklearn_5_tree_DTYPE_t *__pyx_v_Xf; CYTHON_UNUSED __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_sorted_samples; __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_v_index_to_samples; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_max_features; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_min_samples_leaf; double __pyx_v_min_weight_leaf; __pyx_t_7sklearn_5_tree_UINT32_t *__pyx_v_random_state; struct __pyx_t_7sklearn_5_tree_SplitRecord __pyx_v_best; struct __pyx_t_7sklearn_5_tree_SplitRecord __pyx_v_current; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_f_i; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_f_j; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_p; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_tmp; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_visited_features; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_found_constants; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_drawn_constants; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_known_constants; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_n_total_constants; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_p_next; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_p_prev; int __pyx_v_is_samples_sorted; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_start_positive; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_v_end_negative; __pyx_t_7sklearn_5_tree_SIZE_t *__pyx_t_1; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_2; __pyx_t_7sklearn_5_tree_INT32_t *__pyx_t_3; __pyx_t_7sklearn_5_tree_DTYPE_t *__pyx_t_4; double __pyx_t_5; int __pyx_t_6; int __pyx_t_7; __pyx_t_7sklearn_5_tree_SIZE_t __pyx_t_8; /* "sklearn/_tree.pyx":2356 * """ * # Find the best split * cdef SIZE_t* samples = self.samples # <<<<<<<<<<<<<< * cdef SIZE_t start = self.start * cdef SIZE_t end = self.end */ __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.samples; __pyx_v_samples = __pyx_t_1; /* "sklearn/_tree.pyx":2357 * # Find the best split * cdef SIZE_t* samples = self.samples * cdef SIZE_t start = self.start # <<<<<<<<<<<<<< * cdef SIZE_t end = self.end * */ __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.start; __pyx_v_start = __pyx_t_2; /* "sklearn/_tree.pyx":2358 * cdef SIZE_t* samples = self.samples * cdef SIZE_t start = self.start * cdef SIZE_t end = self.end # <<<<<<<<<<<<<< * * cdef INT32_t* X_indices = self.X_indices */ __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.end; __pyx_v_end = __pyx_t_2; /* "sklearn/_tree.pyx":2360 * cdef SIZE_t end = self.end * * cdef INT32_t* X_indices = self.X_indices # <<<<<<<<<<<<<< * cdef INT32_t* X_indptr = self.X_indptr * cdef DTYPE_t* X_data = self.X_data */ __pyx_t_3 = __pyx_v_self->__pyx_base.X_indices; __pyx_v_X_indices = __pyx_t_3; /* "sklearn/_tree.pyx":2361 * * cdef INT32_t* X_indices = self.X_indices * cdef INT32_t* X_indptr = self.X_indptr # <<<<<<<<<<<<<< * cdef DTYPE_t* X_data = self.X_data * */ __pyx_t_3 = __pyx_v_self->__pyx_base.X_indptr; __pyx_v_X_indptr = __pyx_t_3; /* "sklearn/_tree.pyx":2362 * cdef INT32_t* X_indices = self.X_indices * cdef INT32_t* X_indptr = self.X_indptr * cdef DTYPE_t* X_data = self.X_data # <<<<<<<<<<<<<< * * cdef SIZE_t* features = self.features */ __pyx_t_4 = __pyx_v_self->__pyx_base.X_data; __pyx_v_X_data = __pyx_t_4; /* "sklearn/_tree.pyx":2364 * cdef DTYPE_t* X_data = self.X_data * * cdef SIZE_t* features = self.features # <<<<<<<<<<<<<< * cdef SIZE_t* constant_features = self.constant_features * cdef SIZE_t n_features = self.n_features */ __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.features; __pyx_v_features = __pyx_t_1; /* "sklearn/_tree.pyx":2365 * * cdef SIZE_t* features = self.features * cdef SIZE_t* constant_features = self.constant_features # <<<<<<<<<<<<<< * cdef SIZE_t n_features = self.n_features * */ __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.constant_features; __pyx_v_constant_features = __pyx_t_1; /* "sklearn/_tree.pyx":2366 * cdef SIZE_t* features = self.features * cdef SIZE_t* constant_features = self.constant_features * cdef SIZE_t n_features = self.n_features # <<<<<<<<<<<<<< * * cdef DTYPE_t* Xf = self.feature_values */ __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.n_features; __pyx_v_n_features = __pyx_t_2; /* "sklearn/_tree.pyx":2368 * cdef SIZE_t n_features = self.n_features * * cdef DTYPE_t* Xf = self.feature_values # <<<<<<<<<<<<<< * cdef SIZE_t* sorted_samples = self.sorted_samples * cdef SIZE_t* index_to_samples = self.index_to_samples */ __pyx_t_4 = __pyx_v_self->__pyx_base.__pyx_base.feature_values; __pyx_v_Xf = __pyx_t_4; /* "sklearn/_tree.pyx":2369 * * cdef DTYPE_t* Xf = self.feature_values * cdef SIZE_t* sorted_samples = self.sorted_samples # <<<<<<<<<<<<<< * cdef SIZE_t* index_to_samples = self.index_to_samples * cdef SIZE_t max_features = self.max_features */ __pyx_t_1 = __pyx_v_self->__pyx_base.sorted_samples; __pyx_v_sorted_samples = __pyx_t_1; /* "sklearn/_tree.pyx":2370 * cdef DTYPE_t* Xf = self.feature_values * cdef SIZE_t* sorted_samples = self.sorted_samples * cdef SIZE_t* index_to_samples = self.index_to_samples # <<<<<<<<<<<<<< * cdef SIZE_t max_features = self.max_features * cdef SIZE_t min_samples_leaf = self.min_samples_leaf */ __pyx_t_1 = __pyx_v_self->__pyx_base.index_to_samples; __pyx_v_index_to_samples = __pyx_t_1; /* "sklearn/_tree.pyx":2371 * cdef SIZE_t* sorted_samples = self.sorted_samples * cdef SIZE_t* index_to_samples = self.index_to_samples * cdef SIZE_t max_features = self.max_features # <<<<<<<<<<<<<< * cdef SIZE_t min_samples_leaf = self.min_samples_leaf * cdef double min_weight_leaf = self.min_weight_leaf */ __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.max_features; __pyx_v_max_features = __pyx_t_2; /* "sklearn/_tree.pyx":2372 * cdef SIZE_t* index_to_samples = self.index_to_samples * cdef SIZE_t max_features = self.max_features * cdef SIZE_t min_samples_leaf = self.min_samples_leaf # <<<<<<<<<<<<<< * cdef double min_weight_leaf = self.min_weight_leaf * cdef UINT32_t* random_state = &self.rand_r_state */ __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.min_samples_leaf; __pyx_v_min_samples_leaf = __pyx_t_2; /* "sklearn/_tree.pyx":2373 * cdef SIZE_t max_features = self.max_features * cdef SIZE_t min_samples_leaf = self.min_samples_leaf * cdef double min_weight_leaf = self.min_weight_leaf # <<<<<<<<<<<<<< * cdef UINT32_t* random_state = &self.rand_r_state * */ __pyx_t_5 = __pyx_v_self->__pyx_base.__pyx_base.min_weight_leaf; __pyx_v_min_weight_leaf = __pyx_t_5; /* "sklearn/_tree.pyx":2374 * cdef SIZE_t min_samples_leaf = self.min_samples_leaf * cdef double min_weight_leaf = self.min_weight_leaf * cdef UINT32_t* random_state = &self.rand_r_state # <<<<<<<<<<<<<< * * cdef SplitRecord best, current */ __pyx_v_random_state = (&__pyx_v_self->__pyx_base.__pyx_base.rand_r_state); /* "sklearn/_tree.pyx":2377 * * cdef SplitRecord best, current * _init_split(&best, end) # <<<<<<<<<<<<<< * * cdef SIZE_t f_i = n_features */ __pyx_f_7sklearn_5_tree__init_split((&__pyx_v_best), __pyx_v_end); /* "sklearn/_tree.pyx":2379 * _init_split(&best, end) * * cdef SIZE_t f_i = n_features # <<<<<<<<<<<<<< * cdef SIZE_t f_j, p, tmp * cdef SIZE_t n_visited_features = 0 */ __pyx_v_f_i = __pyx_v_n_features; /* "sklearn/_tree.pyx":2381 * cdef SIZE_t f_i = n_features * cdef SIZE_t f_j, p, tmp * cdef SIZE_t n_visited_features = 0 # <<<<<<<<<<<<<< * # Number of features discovered to be constant during the split search * cdef SIZE_t n_found_constants = 0 */ __pyx_v_n_visited_features = 0; /* "sklearn/_tree.pyx":2383 * cdef SIZE_t n_visited_features = 0 * # Number of features discovered to be constant during the split search * cdef SIZE_t n_found_constants = 0 # <<<<<<<<<<<<<< * # Number of features known to be constant and drawn without replacement * cdef SIZE_t n_drawn_constants = 0 */ __pyx_v_n_found_constants = 0; /* "sklearn/_tree.pyx":2385 * cdef SIZE_t n_found_constants = 0 * # Number of features known to be constant and drawn without replacement * cdef SIZE_t n_drawn_constants = 0 # <<<<<<<<<<<<<< * cdef SIZE_t n_known_constants = n_constant_features[0] * # n_total_constants = n_known_constants + n_found_constants */ __pyx_v_n_drawn_constants = 0; /* "sklearn/_tree.pyx":2386 * # Number of features known to be constant and drawn without replacement * cdef SIZE_t n_drawn_constants = 0 * cdef SIZE_t n_known_constants = n_constant_features[0] # <<<<<<<<<<<<<< * # n_total_constants = n_known_constants + n_found_constants * cdef SIZE_t n_total_constants = n_known_constants */ __pyx_v_n_known_constants = (__pyx_v_n_constant_features[0]); /* "sklearn/_tree.pyx":2388 * cdef SIZE_t n_known_constants = n_constant_features[0] * # n_total_constants = n_known_constants + n_found_constants * cdef SIZE_t n_total_constants = n_known_constants # <<<<<<<<<<<<<< * cdef DTYPE_t current_feature_value * */ __pyx_v_n_total_constants = __pyx_v_n_known_constants; /* "sklearn/_tree.pyx":2393 * cdef SIZE_t p_next * cdef SIZE_t p_prev * cdef bint is_samples_sorted = 0 # indicate is sorted_samples is # <<<<<<<<<<<<<< * # inititialized * */ __pyx_v_is_samples_sorted = 0; /* "sklearn/_tree.pyx":2410 * # newly discovered constant features to spare computation on descendant * # nodes. * while (f_i > n_total_constants and # Stop early if remaining features # <<<<<<<<<<<<<< * # are constant * (n_visited_features < max_features or */ while (1) { __pyx_t_7 = ((__pyx_v_f_i > __pyx_v_n_total_constants) != 0); if (__pyx_t_7) { } else { __pyx_t_6 = __pyx_t_7; goto __pyx_L5_bool_binop_done; } /* "sklearn/_tree.pyx":2412 * while (f_i > n_total_constants and # Stop early if remaining features * # are constant * (n_visited_features < max_features or # <<<<<<<<<<<<<< * # At least one drawn features must be non constant * n_visited_features <= n_found_constants + n_drawn_constants)): */ __pyx_t_7 = ((__pyx_v_n_visited_features < __pyx_v_max_features) != 0); if (!__pyx_t_7) { } else { __pyx_t_6 = __pyx_t_7; goto __pyx_L5_bool_binop_done; } /* "sklearn/_tree.pyx":2414 * (n_visited_features < max_features or * # At least one drawn features must be non constant * n_visited_features <= n_found_constants + n_drawn_constants)): # <<<<<<<<<<<<<< * * n_visited_features += 1 */ __pyx_t_7 = ((__pyx_v_n_visited_features <= (__pyx_v_n_found_constants + __pyx_v_n_drawn_constants)) != 0); __pyx_t_6 = __pyx_t_7; __pyx_L5_bool_binop_done:; if (!__pyx_t_6) break; /* "sklearn/_tree.pyx":2416 * n_visited_features <= n_found_constants + n_drawn_constants)): * * n_visited_features += 1 # <<<<<<<<<<<<<< * * # Loop invariant: elements of features in */ __pyx_v_n_visited_features = (__pyx_v_n_visited_features + 1); /* "sklearn/_tree.pyx":2430 * * # Draw a feature at random * f_j = rand_int(n_drawn_constants, f_i - n_found_constants, # <<<<<<<<<<<<<< * random_state) * */ __pyx_v_f_j = __pyx_f_7sklearn_5_tree_rand_int(__pyx_v_n_drawn_constants, (__pyx_v_f_i - __pyx_v_n_found_constants), __pyx_v_random_state); /* "sklearn/_tree.pyx":2433 * random_state) * * if f_j < n_known_constants: # <<<<<<<<<<<<<< * # f_j in the interval [n_drawn_constants, n_known_constants[ * tmp = features[f_j] */ __pyx_t_6 = ((__pyx_v_f_j < __pyx_v_n_known_constants) != 0); if (__pyx_t_6) { /* "sklearn/_tree.pyx":2435 * if f_j < n_known_constants: * # f_j in the interval [n_drawn_constants, n_known_constants[ * tmp = features[f_j] # <<<<<<<<<<<<<< * features[f_j] = features[n_drawn_constants] * features[n_drawn_constants] = tmp */ __pyx_v_tmp = (__pyx_v_features[__pyx_v_f_j]); /* "sklearn/_tree.pyx":2436 * # f_j in the interval [n_drawn_constants, n_known_constants[ * tmp = features[f_j] * features[f_j] = features[n_drawn_constants] # <<<<<<<<<<<<<< * features[n_drawn_constants] = tmp * */ (__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_drawn_constants]); /* "sklearn/_tree.pyx":2437 * tmp = features[f_j] * features[f_j] = features[n_drawn_constants] * features[n_drawn_constants] = tmp # <<<<<<<<<<<<<< * * n_drawn_constants += 1 */ (__pyx_v_features[__pyx_v_n_drawn_constants]) = __pyx_v_tmp; /* "sklearn/_tree.pyx":2439 * features[n_drawn_constants] = tmp * * n_drawn_constants += 1 # <<<<<<<<<<<<<< * * else: */ __pyx_v_n_drawn_constants = (__pyx_v_n_drawn_constants + 1); goto __pyx_L8; } /*else*/ { /* "sklearn/_tree.pyx":2443 * else: * # f_j in the interval [n_known_constants, f_i - n_found_constants[ * f_j += n_found_constants # <<<<<<<<<<<<<< * # f_j in the interval [n_total_constants, f_i[ * */ __pyx_v_f_j = (__pyx_v_f_j + __pyx_v_n_found_constants); /* "sklearn/_tree.pyx":2446 * # f_j in the interval [n_total_constants, f_i[ * * current.feature = features[f_j] # <<<<<<<<<<<<<< * self.extract_nnz(current.feature, * &end_negative, &start_positive, */ __pyx_v_current.feature = (__pyx_v_features[__pyx_v_f_j]); /* "sklearn/_tree.pyx":2447 * * current.feature = features[f_j] * self.extract_nnz(current.feature, # <<<<<<<<<<<<<< * &end_negative, &start_positive, * &is_samples_sorted) */ __pyx_f_7sklearn_5_tree_18BaseSparseSplitter_extract_nnz(((struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter *)__pyx_v_self), __pyx_v_current.feature, (&__pyx_v_end_negative), (&__pyx_v_start_positive), (&__pyx_v_is_samples_sorted)); /* "sklearn/_tree.pyx":2452 * * # Sort the positive and negative parts of `Xf` * sort(Xf + start, samples + start, end_negative - start) # <<<<<<<<<<<<<< * sort(Xf + start_positive, samples + start_positive, * end - start_positive) */ __pyx_f_7sklearn_5_tree_sort((__pyx_v_Xf + __pyx_v_start), (__pyx_v_samples + __pyx_v_start), (__pyx_v_end_negative - __pyx_v_start)); /* "sklearn/_tree.pyx":2453 * # Sort the positive and negative parts of `Xf` * sort(Xf + start, samples + start, end_negative - start) * sort(Xf + start_positive, samples + start_positive, # <<<<<<<<<<<<<< * end - start_positive) * */ __pyx_f_7sklearn_5_tree_sort((__pyx_v_Xf + __pyx_v_start_positive), (__pyx_v_samples + __pyx_v_start_positive), (__pyx_v_end - __pyx_v_start_positive)); /* "sklearn/_tree.pyx":2457 * * # Update index_to_samples to take into account the sort * for p in range(start, end_negative): # <<<<<<<<<<<<<< * index_to_samples[samples[p]] = p * for p in range(start_positive, end): */ __pyx_t_2 = __pyx_v_end_negative; for (__pyx_t_8 = __pyx_v_start; __pyx_t_8 < __pyx_t_2; __pyx_t_8+=1) { __pyx_v_p = __pyx_t_8; /* "sklearn/_tree.pyx":2458 * # Update index_to_samples to take into account the sort * for p in range(start, end_negative): * index_to_samples[samples[p]] = p # <<<<<<<<<<<<<< * for p in range(start_positive, end): * index_to_samples[samples[p]] = p */ (__pyx_v_index_to_samples[(__pyx_v_samples[__pyx_v_p])]) = __pyx_v_p; } /* "sklearn/_tree.pyx":2459 * for p in range(start, end_negative): * index_to_samples[samples[p]] = p * for p in range(start_positive, end): # <<<<<<<<<<<<<< * index_to_samples[samples[p]] = p * */ __pyx_t_2 = __pyx_v_end; for (__pyx_t_8 = __pyx_v_start_positive; __pyx_t_8 < __pyx_t_2; __pyx_t_8+=1) { __pyx_v_p = __pyx_t_8; /* "sklearn/_tree.pyx":2460 * index_to_samples[samples[p]] = p * for p in range(start_positive, end): * index_to_samples[samples[p]] = p # <<<<<<<<<<<<<< * * # Add one or two zeros in Xf, if there is any */ (__pyx_v_index_to_samples[(__pyx_v_samples[__pyx_v_p])]) = __pyx_v_p; } /* "sklearn/_tree.pyx":2463 * * # Add one or two zeros in Xf, if there is any * if end_negative < start_positive: # <<<<<<<<<<<<<< * start_positive -= 1 * Xf[start_positive] = 0. */ __pyx_t_6 = ((__pyx_v_end_negative < __pyx_v_start_positive) != 0); if (__pyx_t_6) { /* "sklearn/_tree.pyx":2464 * # Add one or two zeros in Xf, if there is any * if end_negative < start_positive: * start_positive -= 1 # <<<<<<<<<<<<<< * Xf[start_positive] = 0. * */ __pyx_v_start_positive = (__pyx_v_start_positive - 1); /* "sklearn/_tree.pyx":2465 * if end_negative < start_positive: * start_positive -= 1 * Xf[start_positive] = 0. # <<<<<<<<<<<<<< * * if end_negative != start_positive: */ (__pyx_v_Xf[__pyx_v_start_positive]) = 0.; /* "sklearn/_tree.pyx":2467 * Xf[start_positive] = 0. * * if end_negative != start_positive: # <<<<<<<<<<<<<< * Xf[end_negative] = 0. * end_negative += 1 */ __pyx_t_6 = ((__pyx_v_end_negative != __pyx_v_start_positive) != 0); if (__pyx_t_6) { /* "sklearn/_tree.pyx":2468 * * if end_negative != start_positive: * Xf[end_negative] = 0. # <<<<<<<<<<<<<< * end_negative += 1 * */ (__pyx_v_Xf[__pyx_v_end_negative]) = 0.; /* "sklearn/_tree.pyx":2469 * if end_negative != start_positive: * Xf[end_negative] = 0. * end_negative += 1 # <<<<<<<<<<<<<< * * if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD: */ __pyx_v_end_negative = (__pyx_v_end_negative + 1); goto __pyx_L14; } __pyx_L14:; goto __pyx_L13; } __pyx_L13:; /* "sklearn/_tree.pyx":2471 * end_negative += 1 * * if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD: # <<<<<<<<<<<<<< * features[f_j] = features[n_total_constants] * features[n_total_constants] = current.feature */ __pyx_t_6 = (((__pyx_v_Xf[(__pyx_v_end - 1)]) <= ((__pyx_v_Xf[__pyx_v_start]) + __pyx_v_7sklearn_5_tree_FEATURE_THRESHOLD)) != 0); if (__pyx_t_6) { /* "sklearn/_tree.pyx":2472 * * if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD: * features[f_j] = features[n_total_constants] # <<<<<<<<<<<<<< * features[n_total_constants] = current.feature * */ (__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_total_constants]); /* "sklearn/_tree.pyx":2473 * if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD: * features[f_j] = features[n_total_constants] * features[n_total_constants] = current.feature # <<<<<<<<<<<<<< * * n_found_constants += 1 */ __pyx_t_2 = __pyx_v_current.feature; (__pyx_v_features[__pyx_v_n_total_constants]) = __pyx_t_2; /* "sklearn/_tree.pyx":2475 * features[n_total_constants] = current.feature * * n_found_constants += 1 # <<<<<<<<<<<<<< * n_total_constants += 1 * */ __pyx_v_n_found_constants = (__pyx_v_n_found_constants + 1); /* "sklearn/_tree.pyx":2476 * * n_found_constants += 1 * n_total_constants += 1 # <<<<<<<<<<<<<< * * else: */ __pyx_v_n_total_constants = (__pyx_v_n_total_constants + 1); goto __pyx_L15; } /*else*/ { /* "sklearn/_tree.pyx":2479 * * else: * f_i -= 1 # <<<<<<<<<<<<<< * features[f_i], features[f_j] = features[f_j], features[f_i] * */ __pyx_v_f_i = (__pyx_v_f_i - 1); /* "sklearn/_tree.pyx":2480 * else: * f_i -= 1 * features[f_i], features[f_j] = features[f_j], features[f_i] # <<<<<<<<<<<<<< * * # Evaluate all splits */ __pyx_t_2 = (__pyx_v_features[__pyx_v_f_j]); __pyx_t_8 = (__pyx_v_features[__pyx_v_f_i]); (__pyx_v_features[__pyx_v_f_i]) = __pyx_t_2; (__pyx_v_features[__pyx_v_f_j]) = __pyx_t_8; /* "sklearn/_tree.pyx":2483 * * # Evaluate all splits * self.criterion.reset() # <<<<<<<<<<<<<< * p = start * */ ((struct __pyx_vtabstruct_7sklearn_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion); /* "sklearn/_tree.pyx":2484 * # Evaluate all splits * self.criterion.reset() * p = start # <<<<<<<<<<<<<< * * while p < end: */ __pyx_v_p = __pyx_v_start; /* "sklearn/_tree.pyx":2486 * p = start * * while p < end: # <<<<<<<<<<<<<< * if p + 1 != end_negative: * p_next = p + 1 */ while (1) { __pyx_t_6 = ((__pyx_v_p < __pyx_v_end) != 0); if (!__pyx_t_6) break; /* "sklearn/_tree.pyx":2487 * * while p < end: * if p + 1 != end_negative: # <<<<<<<<<<<<<< * p_next = p + 1 * else: */ __pyx_t_6 = (((__pyx_v_p + 1) != __pyx_v_end_negative) != 0); if (__pyx_t_6) { /* "sklearn/_tree.pyx":2488 * while p < end: * if p + 1 != end_negative: * p_next = p + 1 # <<<<<<<<<<<<<< * else: * p_next = start_positive */ __pyx_v_p_next = (__pyx_v_p + 1); goto __pyx_L18; } /*else*/ { /* "sklearn/_tree.pyx":2490 * p_next = p + 1 * else: * p_next = start_positive # <<<<<<<<<<<<<< * * while (p_next < end and */ __pyx_v_p_next = __pyx_v_start_positive; } __pyx_L18:; /* "sklearn/_tree.pyx":2492 * p_next = start_positive * * while (p_next < end and # <<<<<<<<<<<<<< * Xf[p_next] <= Xf[p] + FEATURE_THRESHOLD): * p = p_next */ while (1) { __pyx_t_7 = ((__pyx_v_p_next < __pyx_v_end) != 0); if (__pyx_t_7) { } else { __pyx_t_6 = __pyx_t_7; goto __pyx_L21_bool_binop_done; } /* "sklearn/_tree.pyx":2493 * * while (p_next < end and * Xf[p_next] <= Xf[p] + FEATURE_THRESHOLD): # <<<<<<<<<<<<<< * p = p_next * if p + 1 != end_negative: */ __pyx_t_7 = (((__pyx_v_Xf[__pyx_v_p_next]) <= ((__pyx_v_Xf[__pyx_v_p]) + __pyx_v_7sklearn_5_tree_FEATURE_THRESHOLD)) != 0); __pyx_t_6 = __pyx_t_7; __pyx_L21_bool_binop_done:; if (!__pyx_t_6) break; /* "sklearn/_tree.pyx":2494 * while (p_next < end and * Xf[p_next] <= Xf[p] + FEATURE_THRESHOLD): * p = p_next # <<<<<<<<<<<<<< * if p + 1 != end_negative: * p_next = p + 1 */ __pyx_v_p = __pyx_v_p_next; /* "sklearn/_tree.pyx":2495 * Xf[p_next] <= Xf[p] + FEATURE_THRESHOLD): * p = p_next * if p + 1 != end_negative: # <<<<<<<<<<<<<< * p_next = p + 1 * else: */ __pyx_t_6 = (((__pyx_v_p + 1) != __pyx_v_end_negative) != 0); if (__pyx_t_6) { /* "sklearn/_tree.pyx":2496 * p = p_next * if p + 1 != end_negative: * p_next = p + 1 # <<<<<<<<<<<<<< * else: * p_next = start_positive */ __pyx_v_p_next = (__pyx_v_p + 1); goto __pyx_L23; } /*else*/ { /* "sklearn/_tree.pyx":2498 * p_next = p + 1 * else: * p_next = start_positive # <<<<<<<<<<<<<< * * */ __pyx_v_p_next = __pyx_v_start_positive; } __pyx_L23:; } /* "sklearn/_tree.pyx":2503 * # (p_next >= end) or (X[samples[p_next], current.feature] > * # X[samples[p], current.feature]) * p_prev = p # <<<<<<<<<<<<<< * p = p_next * # (p >= end) or (X[samples[p], current.feature] > */ __pyx_v_p_prev = __pyx_v_p; /* "sklearn/_tree.pyx":2504 * # X[samples[p], current.feature]) * p_prev = p * p = p_next # <<<<<<<<<<<<<< * # (p >= end) or (X[samples[p], current.feature] > * # X[samples[p_prev], current.feature]) */ __pyx_v_p = __pyx_v_p_next; /* "sklearn/_tree.pyx":2509 * * * if p < end: # <<<<<<<<<<<<<< * current.pos = p * */ __pyx_t_6 = ((__pyx_v_p < __pyx_v_end) != 0); if (__pyx_t_6) { /* "sklearn/_tree.pyx":2510 * * if p < end: * current.pos = p # <<<<<<<<<<<<<< * * # Reject if min_samples_leaf is not guaranteed */ __pyx_v_current.pos = __pyx_v_p; /* "sklearn/_tree.pyx":2513 * * # Reject if min_samples_leaf is not guaranteed * if (((current.pos - start) < min_samples_leaf) or # <<<<<<<<<<<<<< * ((end - current.pos) < min_samples_leaf)): * continue */ __pyx_t_7 = (((__pyx_v_current.pos - __pyx_v_start) < __pyx_v_min_samples_leaf) != 0); if (!__pyx_t_7) { } else { __pyx_t_6 = __pyx_t_7; goto __pyx_L26_bool_binop_done; } /* "sklearn/_tree.pyx":2514 * # Reject if min_samples_leaf is not guaranteed * if (((current.pos - start) < min_samples_leaf) or * ((end - current.pos) < min_samples_leaf)): # <<<<<<<<<<<<<< * continue * */ __pyx_t_7 = (((__pyx_v_end - __pyx_v_current.pos) < __pyx_v_min_samples_leaf) != 0); 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goto __pyx_L29_bool_binop_done; } /* "sklearn/_tree.pyx":2521 * # Reject if min_weight_leaf is not satisfied * if ((self.criterion.weighted_n_left < min_weight_leaf) or * (self.criterion.weighted_n_right < min_weight_leaf)): # <<<<<<<<<<<<<< * continue * */ __pyx_t_7 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_right < __pyx_v_min_weight_leaf) != 0); __pyx_t_6 = __pyx_t_7; __pyx_L29_bool_binop_done:; if (__pyx_t_6) { /* "sklearn/_tree.pyx":2522 * if ((self.criterion.weighted_n_left < min_weight_leaf) or * (self.criterion.weighted_n_right < min_weight_leaf)): * continue # <<<<<<<<<<<<<< * * current.improvement = self.criterion.impurity_improvement(impurity) */ goto __pyx_L16_continue; } /* "sklearn/_tree.pyx":2524 * continue * * current.improvement = self.criterion.impurity_improvement(impurity) # <<<<<<<<<<<<<< * if current.improvement > best.improvement: * self.criterion.children_impurity(¤t.impurity_left, */ __pyx_v_current.improvement = ((struct __pyx_vtabstruct_7sklearn_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->impurity_improvement(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_impurity); 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__pyx_v_n_features = __pyx_t_2; /* "sklearn/_tree.pyx":2586 * cdef SIZE_t n_features = self.n_features * * cdef DTYPE_t* Xf = self.feature_values # <<<<<<<<<<<<<< * cdef SIZE_t* sorted_samples = self.sorted_samples * cdef SIZE_t* index_to_samples = self.index_to_samples */ __pyx_t_4 = __pyx_v_self->__pyx_base.__pyx_base.feature_values; __pyx_v_Xf = __pyx_t_4; /* "sklearn/_tree.pyx":2587 * * cdef DTYPE_t* Xf = self.feature_values * cdef SIZE_t* sorted_samples = self.sorted_samples # <<<<<<<<<<<<<< * cdef SIZE_t* index_to_samples = self.index_to_samples * cdef SIZE_t max_features = self.max_features */ __pyx_t_1 = __pyx_v_self->__pyx_base.sorted_samples; __pyx_v_sorted_samples = __pyx_t_1; /* "sklearn/_tree.pyx":2588 * cdef DTYPE_t* Xf = self.feature_values * cdef SIZE_t* sorted_samples = self.sorted_samples * cdef SIZE_t* index_to_samples = self.index_to_samples # <<<<<<<<<<<<<< * cdef SIZE_t max_features = self.max_features * cdef SIZE_t min_samples_leaf = self.min_samples_leaf */ __pyx_t_1 = __pyx_v_self->__pyx_base.index_to_samples; 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__pyx_v_min_weight_leaf = __pyx_t_5; /* "sklearn/_tree.pyx":2592 * cdef SIZE_t min_samples_leaf = self.min_samples_leaf * cdef double min_weight_leaf = self.min_weight_leaf * cdef UINT32_t* random_state = &self.rand_r_state # <<<<<<<<<<<<<< * * cdef SplitRecord best, current */ __pyx_v_random_state = (&__pyx_v_self->__pyx_base.__pyx_base.rand_r_state); /* "sklearn/_tree.pyx":2595 * * cdef SplitRecord best, current * _init_split(&best, end) # <<<<<<<<<<<<<< * * cdef DTYPE_t current_feature_value */ __pyx_f_7sklearn_5_tree__init_split((&__pyx_v_best), __pyx_v_end); /* "sklearn/_tree.pyx":2599 * cdef DTYPE_t current_feature_value * * cdef SIZE_t f_i = n_features # <<<<<<<<<<<<<< * cdef SIZE_t f_j, p, tmp * cdef SIZE_t n_visited_features = 0 */ __pyx_v_f_i = __pyx_v_n_features; /* "sklearn/_tree.pyx":2601 * cdef SIZE_t f_i = n_features * cdef SIZE_t f_j, p, tmp * cdef SIZE_t n_visited_features = 0 # <<<<<<<<<<<<<< * # Number of features discovered to be constant during the split search * cdef SIZE_t n_found_constants = 0 */ __pyx_v_n_visited_features = 0; /* "sklearn/_tree.pyx":2603 * cdef SIZE_t n_visited_features = 0 * # Number of features discovered to be constant during the split search * cdef SIZE_t n_found_constants = 0 # <<<<<<<<<<<<<< * # Number of features known to be constant and drawn without replacement * cdef SIZE_t n_drawn_constants = 0 */ __pyx_v_n_found_constants = 0; /* "sklearn/_tree.pyx":2605 * cdef SIZE_t n_found_constants = 0 * # Number of features known to be constant and drawn without replacement * cdef SIZE_t n_drawn_constants = 0 # <<<<<<<<<<<<<< * cdef SIZE_t n_known_constants = n_constant_features[0] * # n_total_constants = n_known_constants + n_found_constants */ __pyx_v_n_drawn_constants = 0; /* "sklearn/_tree.pyx":2606 * # Number of features known to be constant and drawn without replacement * cdef SIZE_t n_drawn_constants = 0 * cdef SIZE_t n_known_constants = n_constant_features[0] # <<<<<<<<<<<<<< * # n_total_constants = n_known_constants + n_found_constants * cdef SIZE_t n_total_constants = n_known_constants */ __pyx_v_n_known_constants = (__pyx_v_n_constant_features[0]); /* "sklearn/_tree.pyx":2608 * cdef SIZE_t n_known_constants = n_constant_features[0] * # n_total_constants = n_known_constants + n_found_constants * cdef SIZE_t n_total_constants = n_known_constants # <<<<<<<<<<<<<< * cdef SIZE_t partition_end * */ __pyx_v_n_total_constants = __pyx_v_n_known_constants; /* "sklearn/_tree.pyx":2614 * cdef DTYPE_t max_feature_value * * cdef bint is_samples_sorted = 0 # indicate that sorted_samples is # <<<<<<<<<<<<<< * # inititialized * */ __pyx_v_is_samples_sorted = 0; /* "sklearn/_tree.pyx":2631 * # newly discovered constant features to spare computation on descendant * # nodes. * while (f_i > n_total_constants and # Stop early if remaining features # <<<<<<<<<<<<<< * # are constant * (n_visited_features < max_features or */ while (1) { __pyx_t_7 = ((__pyx_v_f_i > __pyx_v_n_total_constants) != 0); if (__pyx_t_7) { } else { __pyx_t_6 = __pyx_t_7; goto __pyx_L5_bool_binop_done; } /* "sklearn/_tree.pyx":2633 * while (f_i > n_total_constants and # Stop early if remaining features * # are constant * (n_visited_features < max_features or # <<<<<<<<<<<<<< * # At least one drawn features must be non constant * n_visited_features <= n_found_constants + n_drawn_constants)): */ __pyx_t_7 = ((__pyx_v_n_visited_features < __pyx_v_max_features) != 0); if (!__pyx_t_7) { } else { __pyx_t_6 = __pyx_t_7; goto __pyx_L5_bool_binop_done; } /* "sklearn/_tree.pyx":2635 * (n_visited_features < max_features or * # At least one drawn features must be non constant * n_visited_features <= n_found_constants + n_drawn_constants)): # <<<<<<<<<<<<<< * * n_visited_features += 1 */ __pyx_t_7 = ((__pyx_v_n_visited_features <= (__pyx_v_n_found_constants + __pyx_v_n_drawn_constants)) != 0); __pyx_t_6 = __pyx_t_7; __pyx_L5_bool_binop_done:; if (!__pyx_t_6) break; /* "sklearn/_tree.pyx":2637 * n_visited_features <= n_found_constants + n_drawn_constants)): * * n_visited_features += 1 # <<<<<<<<<<<<<< * * # Loop invariant: elements of features in */ __pyx_v_n_visited_features = (__pyx_v_n_visited_features + 1); /* "sklearn/_tree.pyx":2651 * * # Draw a feature at random * f_j = rand_int(n_drawn_constants, f_i - n_found_constants, # <<<<<<<<<<<<<< * random_state) * */ __pyx_v_f_j = __pyx_f_7sklearn_5_tree_rand_int(__pyx_v_n_drawn_constants, (__pyx_v_f_i - __pyx_v_n_found_constants), __pyx_v_random_state); /* "sklearn/_tree.pyx":2654 * random_state) * * if f_j < n_known_constants: # <<<<<<<<<<<<<< * # f_j in the interval [n_drawn_constants, n_known_constants[ * tmp = features[f_j] */ __pyx_t_6 = ((__pyx_v_f_j < __pyx_v_n_known_constants) != 0); if (__pyx_t_6) { /* "sklearn/_tree.pyx":2656 * if f_j < n_known_constants: * # f_j in the interval [n_drawn_constants, n_known_constants[ * tmp = features[f_j] # <<<<<<<<<<<<<< * features[f_j] = features[n_drawn_constants] * features[n_drawn_constants] = tmp */ __pyx_v_tmp = (__pyx_v_features[__pyx_v_f_j]); /* "sklearn/_tree.pyx":2657 * # f_j in the interval [n_drawn_constants, n_known_constants[ * tmp = features[f_j] * features[f_j] = features[n_drawn_constants] # <<<<<<<<<<<<<< * features[n_drawn_constants] = tmp * */ (__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_drawn_constants]); /* "sklearn/_tree.pyx":2658 * tmp = features[f_j] * features[f_j] = features[n_drawn_constants] * features[n_drawn_constants] = tmp # <<<<<<<<<<<<<< * * n_drawn_constants += 1 */ (__pyx_v_features[__pyx_v_n_drawn_constants]) = __pyx_v_tmp; /* "sklearn/_tree.pyx":2660 * features[n_drawn_constants] = tmp * * n_drawn_constants += 1 # <<<<<<<<<<<<<< * * else: */ __pyx_v_n_drawn_constants = (__pyx_v_n_drawn_constants + 1); goto __pyx_L8; } /*else*/ { /* "sklearn/_tree.pyx":2664 * else: * # f_j in the interval [n_known_constants, f_i - n_found_constants[ * f_j += n_found_constants # <<<<<<<<<<<<<< * # f_j in the interval [n_total_constants, f_i[ * */ __pyx_v_f_j = (__pyx_v_f_j + __pyx_v_n_found_constants); /* "sklearn/_tree.pyx":2667 * # f_j in the interval [n_total_constants, f_i[ * * current.feature = features[f_j] # <<<<<<<<<<<<<< * * self.extract_nnz(current.feature, */ __pyx_v_current.feature = (__pyx_v_features[__pyx_v_f_j]); /* "sklearn/_tree.pyx":2669 * current.feature = features[f_j] * * self.extract_nnz(current.feature, # <<<<<<<<<<<<<< * &end_negative, &start_positive, * &is_samples_sorted) */ __pyx_f_7sklearn_5_tree_18BaseSparseSplitter_extract_nnz(((struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter *)__pyx_v_self), __pyx_v_current.feature, (&__pyx_v_end_negative), (&__pyx_v_start_positive), (&__pyx_v_is_samples_sorted)); /* "sklearn/_tree.pyx":2674 * * # Add one or two zeros in Xf, if there is any * if end_negative < start_positive: # <<<<<<<<<<<<<< * start_positive -= 1 * Xf[start_positive] = 0. */ __pyx_t_6 = ((__pyx_v_end_negative < __pyx_v_start_positive) != 0); if (__pyx_t_6) { /* "sklearn/_tree.pyx":2675 * # Add one or two zeros in Xf, if there is any * if end_negative < start_positive: * start_positive -= 1 # <<<<<<<<<<<<<< * Xf[start_positive] = 0. * */ __pyx_v_start_positive = (__pyx_v_start_positive - 1); /* "sklearn/_tree.pyx":2676 * if end_negative < start_positive: * start_positive -= 1 * Xf[start_positive] = 0. # <<<<<<<<<<<<<< * * if end_negative != start_positive: */ (__pyx_v_Xf[__pyx_v_start_positive]) = 0.; /* "sklearn/_tree.pyx":2678 * Xf[start_positive] = 0. * * if end_negative != start_positive: # <<<<<<<<<<<<<< * Xf[end_negative] = 0. * end_negative += 1 */ __pyx_t_6 = ((__pyx_v_end_negative != __pyx_v_start_positive) != 0); if (__pyx_t_6) { /* "sklearn/_tree.pyx":2679 * * if end_negative != start_positive: * Xf[end_negative] = 0. # <<<<<<<<<<<<<< * end_negative += 1 * */ (__pyx_v_Xf[__pyx_v_end_negative]) = 0.; /* "sklearn/_tree.pyx":2680 * if end_negative != start_positive: * Xf[end_negative] = 0. * end_negative += 1 # <<<<<<<<<<<<<< * * # Find min, max in Xf[start:end_negative] */ __pyx_v_end_negative = (__pyx_v_end_negative + 1); goto __pyx_L10; } __pyx_L10:; goto __pyx_L9; } __pyx_L9:; /* "sklearn/_tree.pyx":2683 * * # Find min, max in Xf[start:end_negative] * min_feature_value = Xf[start] # <<<<<<<<<<<<<< * max_feature_value = min_feature_value * */ __pyx_v_min_feature_value = (__pyx_v_Xf[__pyx_v_start]); /* "sklearn/_tree.pyx":2684 * # Find min, max in Xf[start:end_negative] * min_feature_value = Xf[start] * max_feature_value = min_feature_value # <<<<<<<<<<<<<< * * for p in range(start, end_negative): */ __pyx_v_max_feature_value = __pyx_v_min_feature_value; /* "sklearn/_tree.pyx":2686 * max_feature_value = min_feature_value * * for p in range(start, end_negative): # <<<<<<<<<<<<<< * current_feature_value = Xf[p] * */ __pyx_t_2 = __pyx_v_end_negative; for (__pyx_t_8 = __pyx_v_start; __pyx_t_8 < __pyx_t_2; __pyx_t_8+=1) { __pyx_v_p = __pyx_t_8; /* "sklearn/_tree.pyx":2687 * * for p in range(start, end_negative): * current_feature_value = Xf[p] # <<<<<<<<<<<<<< * * if current_feature_value < min_feature_value: */ __pyx_v_current_feature_value = (__pyx_v_Xf[__pyx_v_p]); /* "sklearn/_tree.pyx":2689 * current_feature_value = Xf[p] * * if current_feature_value < min_feature_value: # <<<<<<<<<<<<<< * min_feature_value = current_feature_value * elif current_feature_value > max_feature_value: */ __pyx_t_6 = ((__pyx_v_current_feature_value < __pyx_v_min_feature_value) != 0); if (__pyx_t_6) { /* "sklearn/_tree.pyx":2690 * * if current_feature_value < min_feature_value: * min_feature_value = current_feature_value # <<<<<<<<<<<<<< * elif current_feature_value > max_feature_value: * max_feature_value = current_feature_value */ __pyx_v_min_feature_value = __pyx_v_current_feature_value; goto __pyx_L13; } /* "sklearn/_tree.pyx":2691 * if current_feature_value < min_feature_value: * min_feature_value = current_feature_value * elif current_feature_value > max_feature_value: # <<<<<<<<<<<<<< * max_feature_value = current_feature_value * */ __pyx_t_6 = ((__pyx_v_current_feature_value > __pyx_v_max_feature_value) != 0); if (__pyx_t_6) { /* "sklearn/_tree.pyx":2692 * min_feature_value = current_feature_value * elif current_feature_value > max_feature_value: * max_feature_value = current_feature_value # <<<<<<<<<<<<<< * * # Update min, max given Xf[start_positive:end] */ __pyx_v_max_feature_value = __pyx_v_current_feature_value; goto __pyx_L13; } __pyx_L13:; } /* "sklearn/_tree.pyx":2695 * * # Update min, max given Xf[start_positive:end] * for p in range(start_positive, end): # <<<<<<<<<<<<<< * current_feature_value = Xf[p] * */ __pyx_t_2 = __pyx_v_end; for (__pyx_t_8 = __pyx_v_start_positive; __pyx_t_8 < __pyx_t_2; __pyx_t_8+=1) { __pyx_v_p = __pyx_t_8; /* "sklearn/_tree.pyx":2696 * # Update min, max given Xf[start_positive:end] * for p in range(start_positive, end): * current_feature_value = Xf[p] # <<<<<<<<<<<<<< * * if current_feature_value < min_feature_value: */ __pyx_v_current_feature_value = (__pyx_v_Xf[__pyx_v_p]); /* "sklearn/_tree.pyx":2698 * current_feature_value = Xf[p] * * if current_feature_value < min_feature_value: # <<<<<<<<<<<<<< * min_feature_value = current_feature_value * elif current_feature_value > max_feature_value: */ __pyx_t_6 = ((__pyx_v_current_feature_value < __pyx_v_min_feature_value) != 0); if (__pyx_t_6) { /* "sklearn/_tree.pyx":2699 * * if current_feature_value < min_feature_value: * min_feature_value = current_feature_value # <<<<<<<<<<<<<< * elif current_feature_value > max_feature_value: * max_feature_value = current_feature_value */ __pyx_v_min_feature_value = __pyx_v_current_feature_value; goto __pyx_L16; } /* "sklearn/_tree.pyx":2700 * if current_feature_value < min_feature_value: * min_feature_value = current_feature_value * elif current_feature_value > max_feature_value: # <<<<<<<<<<<<<< * max_feature_value = current_feature_value * */ __pyx_t_6 = ((__pyx_v_current_feature_value > __pyx_v_max_feature_value) != 0); if (__pyx_t_6) { /* "sklearn/_tree.pyx":2701 * min_feature_value = current_feature_value * elif current_feature_value > max_feature_value: * max_feature_value = current_feature_value # <<<<<<<<<<<<<< * * if max_feature_value <= min_feature_value + FEATURE_THRESHOLD: */ __pyx_v_max_feature_value = __pyx_v_current_feature_value; goto __pyx_L16; } __pyx_L16:; } /* "sklearn/_tree.pyx":2703 * max_feature_value = current_feature_value * * if max_feature_value <= min_feature_value + FEATURE_THRESHOLD: # <<<<<<<<<<<<<< * features[f_j] = features[n_total_constants] * features[n_total_constants] = current.feature */ __pyx_t_6 = ((__pyx_v_max_feature_value <= (__pyx_v_min_feature_value + __pyx_v_7sklearn_5_tree_FEATURE_THRESHOLD)) != 0); if (__pyx_t_6) { /* "sklearn/_tree.pyx":2704 * * if max_feature_value <= min_feature_value + FEATURE_THRESHOLD: * features[f_j] = features[n_total_constants] # <<<<<<<<<<<<<< * features[n_total_constants] = current.feature * */ (__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_total_constants]); /* "sklearn/_tree.pyx":2705 * if max_feature_value <= min_feature_value + FEATURE_THRESHOLD: * features[f_j] = features[n_total_constants] * features[n_total_constants] = current.feature # <<<<<<<<<<<<<< * * n_found_constants += 1 */ __pyx_t_2 = __pyx_v_current.feature; (__pyx_v_features[__pyx_v_n_total_constants]) = __pyx_t_2; /* "sklearn/_tree.pyx":2707 * features[n_total_constants] = current.feature * * n_found_constants += 1 # <<<<<<<<<<<<<< * n_total_constants += 1 * */ __pyx_v_n_found_constants = (__pyx_v_n_found_constants + 1); /* "sklearn/_tree.pyx":2708 * * n_found_constants += 1 * n_total_constants += 1 # <<<<<<<<<<<<<< * * else: */ __pyx_v_n_total_constants = (__pyx_v_n_total_constants + 1); goto __pyx_L17; } /*else*/ { /* "sklearn/_tree.pyx":2711 * * else: * f_i -= 1 # <<<<<<<<<<<<<< * features[f_i], features[f_j] = features[f_j], features[f_i] * */ __pyx_v_f_i = (__pyx_v_f_i - 1); /* "sklearn/_tree.pyx":2712 * else: * f_i -= 1 * features[f_i], features[f_j] = features[f_j], features[f_i] # <<<<<<<<<<<<<< * * # Draw a random threshold */ __pyx_t_2 = (__pyx_v_features[__pyx_v_f_j]); __pyx_t_8 = (__pyx_v_features[__pyx_v_f_i]); (__pyx_v_features[__pyx_v_f_i]) = __pyx_t_2; (__pyx_v_features[__pyx_v_f_j]) = __pyx_t_8; /* "sklearn/_tree.pyx":2715 * * # Draw a random threshold * current.threshold = rand_uniform(min_feature_value, # <<<<<<<<<<<<<< * max_feature_value, * random_state) */ __pyx_v_current.threshold = __pyx_f_7sklearn_5_tree_rand_uniform(__pyx_v_min_feature_value, __pyx_v_max_feature_value, __pyx_v_random_state); /* "sklearn/_tree.pyx":2719 * random_state) * * if current.threshold == max_feature_value: # <<<<<<<<<<<<<< * current.threshold = min_feature_value * */ __pyx_t_6 = ((__pyx_v_current.threshold == __pyx_v_max_feature_value) != 0); if (__pyx_t_6) { /* "sklearn/_tree.pyx":2720 * * if current.threshold == max_feature_value: * current.threshold = min_feature_value # <<<<<<<<<<<<<< * * # Partition */ __pyx_v_current.threshold = __pyx_v_min_feature_value; goto __pyx_L18; } __pyx_L18:; /* "sklearn/_tree.pyx":2723 * * # Partition * current.pos = self._partition(current.threshold, # <<<<<<<<<<<<<< * end_negative, * start_positive, */ __pyx_v_current.pos = __pyx_f_7sklearn_5_tree_18BaseSparseSplitter__partition(((struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter *)__pyx_v_self), __pyx_v_current.threshold, __pyx_v_end_negative, __pyx_v_start_positive, (__pyx_v_start_positive + ((__pyx_v_Xf[__pyx_v_start_positive]) == 0.))); /* "sklearn/_tree.pyx":2730 * * # Reject if min_samples_leaf is not guaranteed * if (((current.pos - start) < min_samples_leaf) or # <<<<<<<<<<<<<< * ((end - current.pos) < min_samples_leaf)): * continue */ __pyx_t_7 = (((__pyx_v_current.pos - __pyx_v_start) < __pyx_v_min_samples_leaf) != 0); if (!__pyx_t_7) { } else { __pyx_t_6 = __pyx_t_7; goto __pyx_L20_bool_binop_done; } /* "sklearn/_tree.pyx":2731 * # Reject if min_samples_leaf is not guaranteed * if (((current.pos - start) < min_samples_leaf) or * ((end - current.pos) < min_samples_leaf)): # <<<<<<<<<<<<<< * continue * */ __pyx_t_7 = (((__pyx_v_end - __pyx_v_current.pos) < __pyx_v_min_samples_leaf) != 0); __pyx_t_6 = __pyx_t_7; __pyx_L20_bool_binop_done:; if (__pyx_t_6) { /* "sklearn/_tree.pyx":2732 * if (((current.pos - start) < min_samples_leaf) or * ((end - current.pos) < min_samples_leaf)): * continue # <<<<<<<<<<<<<< * * # Evaluate split */ goto __pyx_L3_continue; } /* "sklearn/_tree.pyx":2735 * * # Evaluate split * self.criterion.reset() # <<<<<<<<<<<<<< * self.criterion.update(current.pos) * */ ((struct __pyx_vtabstruct_7sklearn_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion); /* "sklearn/_tree.pyx":2736 * # Evaluate split * self.criterion.reset() * self.criterion.update(current.pos) # <<<<<<<<<<<<<< * * # Reject if min_weight_leaf is not satisfied */ ((struct __pyx_vtabstruct_7sklearn_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->update(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_current.pos); /* "sklearn/_tree.pyx":2739 * * # Reject if min_weight_leaf is not satisfied * if ((self.criterion.weighted_n_left < min_weight_leaf) or # <<<<<<<<<<<<<< * (self.criterion.weighted_n_right < min_weight_leaf)): * continue */ __pyx_t_7 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_left < __pyx_v_min_weight_leaf) != 0); if (!__pyx_t_7) { } else { __pyx_t_6 = __pyx_t_7; goto __pyx_L23_bool_binop_done; } /* "sklearn/_tree.pyx":2740 * # Reject if min_weight_leaf is not satisfied * if ((self.criterion.weighted_n_left < min_weight_leaf) or * (self.criterion.weighted_n_right < min_weight_leaf)): # <<<<<<<<<<<<<< * continue * */ __pyx_t_7 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_right < __pyx_v_min_weight_leaf) != 0); __pyx_t_6 = __pyx_t_7; __pyx_L23_bool_binop_done:; if (__pyx_t_6) { /* "sklearn/_tree.pyx":2741 * if ((self.criterion.weighted_n_left < min_weight_leaf) or * (self.criterion.weighted_n_right < min_weight_leaf)): * continue # <<<<<<<<<<<<<< * * current.improvement = self.criterion.impurity_improvement(impurity) */ goto __pyx_L3_continue; } /* "sklearn/_tree.pyx":2743 * continue * * current.improvement = self.criterion.impurity_improvement(impurity) # <<<<<<<<<<<<<< * * if current.improvement > best.improvement: */ __pyx_v_current.improvement = ((struct __pyx_vtabstruct_7sklearn_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->impurity_improvement(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_impurity); /* "sklearn/_tree.pyx":2745 * current.improvement = self.criterion.impurity_improvement(impurity) * * if current.improvement > best.improvement: # <<<<<<<<<<<<<< * self.criterion.children_impurity(¤t.impurity_left, * ¤t.impurity_right) */ __pyx_t_6 = ((__pyx_v_current.improvement > __pyx_v_best.improvement) != 0); if (__pyx_t_6) { /* "sklearn/_tree.pyx":2746 * * if current.improvement > best.improvement: * self.criterion.children_impurity(¤t.impurity_left, # <<<<<<<<<<<<<< * ¤t.impurity_right) * best = current */ ((struct __pyx_vtabstruct_7sklearn_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->children_impurity(__pyx_v_self->__pyx_base.__pyx_base.criterion, (&__pyx_v_current.impurity_left), (&__pyx_v_current.impurity_right)); /* "sklearn/_tree.pyx":2748 * self.criterion.children_impurity(¤t.impurity_left, * ¤t.impurity_right) * best = current # <<<<<<<<<<<<<< * * # Reorganize into samples[start:best.pos] + samples[best.pos:end] */ __pyx_v_best = __pyx_v_current; goto __pyx_L25; } __pyx_L25:; } __pyx_L17:; } __pyx_L8:; __pyx_L3_continue:; } /* "sklearn/_tree.pyx":2751 * * # Reorganize into samples[start:best.pos] + samples[best.pos:end] * if best.pos < end and current.feature != best.feature: # <<<<<<<<<<<<<< * self.extract_nnz(best.feature, &end_negative, &start_positive, * &is_samples_sorted) */ __pyx_t_7 = ((__pyx_v_best.pos < __pyx_v_end) != 0); if (__pyx_t_7) { } else { __pyx_t_6 = __pyx_t_7; goto __pyx_L27_bool_binop_done; } __pyx_t_7 = ((__pyx_v_current.feature != __pyx_v_best.feature) != 0); __pyx_t_6 = __pyx_t_7; __pyx_L27_bool_binop_done:; if (__pyx_t_6) { /* "sklearn/_tree.pyx":2752 * # Reorganize into samples[start:best.pos] + samples[best.pos:end] * if best.pos < end and current.feature != best.feature: * self.extract_nnz(best.feature, &end_negative, &start_positive, # <<<<<<<<<<<<<< * &is_samples_sorted) * */ __pyx_f_7sklearn_5_tree_18BaseSparseSplitter_extract_nnz(((struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter *)__pyx_v_self), __pyx_v_best.feature, (&__pyx_v_end_negative), (&__pyx_v_start_positive), (&__pyx_v_is_samples_sorted)); /* "sklearn/_tree.pyx":2755 * &is_samples_sorted) * * self._partition(best.threshold, end_negative, start_positive, # <<<<<<<<<<<<<< * best.pos) * */ __pyx_f_7sklearn_5_tree_18BaseSparseSplitter__partition(((struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter *)__pyx_v_self), __pyx_v_best.threshold, __pyx_v_end_negative, __pyx_v_start_positive, __pyx_v_best.pos); goto __pyx_L26; } __pyx_L26:; /* "sklearn/_tree.pyx":2761 * # element in features[:n_known_constants] must be preserved for sibling * # and child nodes * memcpy(features, constant_features, sizeof(SIZE_t) * n_known_constants) # <<<<<<<<<<<<<< * * # Copy newly found constant features */ memcpy(__pyx_v_features, __pyx_v_constant_features, ((sizeof(__pyx_t_7sklearn_5_tree_SIZE_t)) * __pyx_v_n_known_constants)); /* "sklearn/_tree.pyx":2764 * * # Copy newly found constant features * memcpy(constant_features + n_known_constants, # <<<<<<<<<<<<<< * features + n_known_constants, * sizeof(SIZE_t) * n_found_constants) */ memcpy((__pyx_v_constant_features + __pyx_v_n_known_constants), (__pyx_v_features + __pyx_v_n_known_constants), ((sizeof(__pyx_t_7sklearn_5_tree_SIZE_t)) * __pyx_v_n_found_constants)); /* "sklearn/_tree.pyx":2769 * * # Return values * split[0] = best # <<<<<<<<<<<<<< * n_constant_features[0] = n_total_constants * */ (__pyx_v_split[0]) = __pyx_v_best; /* "sklearn/_tree.pyx":2770 * # Return values * split[0] = best * n_constant_features[0] = n_total_constants # <<<<<<<<<<<<<< * * */ (__pyx_v_n_constant_features[0]) = __pyx_v_n_total_constants; /* "sklearn/_tree.pyx":2568 * self.random_state), self.__getstate__()) * * cdef void node_split(self, double impurity, SplitRecord* split, # <<<<<<<<<<<<<< * SIZE_t* n_constant_features) nogil: * """Find a random split on node samples[start:end], using sparse */ /* function exit code */ } /* "sklearn/_tree.pyx":2779 * """Interface for different tree building strategies. 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goto __pyx_L6; } /*else*/ { /* "sklearn/_tree.pyx":2833 * init_capacity = (2 ** (tree.max_depth + 1)) - 1 * else: * init_capacity = 2047 # <<<<<<<<<<<<<< * * tree._resize(init_capacity) */ __pyx_v_init_capacity = 2047; } __pyx_L6:; /* "sklearn/_tree.pyx":2835 * init_capacity = 2047 * * tree._resize(init_capacity) # <<<<<<<<<<<<<< * * # Parameters */ ((struct __pyx_vtabstruct_7sklearn_5_tree_Tree *)__pyx_v_tree->__pyx_vtab)->_resize(__pyx_v_tree, __pyx_v_init_capacity); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2835; __pyx_clineno = __LINE__; goto __pyx_L1_error;} /* "sklearn/_tree.pyx":2838 * * # Parameters * cdef Splitter splitter = self.splitter # <<<<<<<<<<<<<< * cdef SIZE_t max_depth = self.max_depth * cdef SIZE_t min_samples_leaf = self.min_samples_leaf */ __pyx_t_1 = ((PyObject *)__pyx_v_self->__pyx_base.splitter); __Pyx_INCREF(__pyx_t_1); __pyx_v_splitter = ((struct __pyx_obj_7sklearn_5_tree_Splitter *)__pyx_t_1); __pyx_t_1 = 0; /* "sklearn/_tree.pyx":2839 * # Parameters * cdef Splitter splitter = self.splitter * cdef SIZE_t max_depth = self.max_depth # <<<<<<<<<<<<<< * cdef SIZE_t min_samples_leaf = self.min_samples_leaf * cdef double min_weight_leaf = self.min_weight_leaf */ __pyx_t_10 = __pyx_v_self->__pyx_base.max_depth; __pyx_v_max_depth = __pyx_t_10; /* "sklearn/_tree.pyx":2840 * cdef Splitter splitter = self.splitter * cdef SIZE_t max_depth = self.max_depth * cdef SIZE_t min_samples_leaf = self.min_samples_leaf # <<<<<<<<<<<<<< * cdef double min_weight_leaf = self.min_weight_leaf * cdef SIZE_t min_samples_split = self.min_samples_split */ __pyx_t_10 = __pyx_v_self->__pyx_base.min_samples_leaf; __pyx_v_min_samples_leaf = __pyx_t_10; /* "sklearn/_tree.pyx":2841 * cdef SIZE_t max_depth = self.max_depth * cdef SIZE_t min_samples_leaf = self.min_samples_leaf * cdef double min_weight_leaf = self.min_weight_leaf # <<<<<<<<<<<<<< * cdef SIZE_t min_samples_split = self.min_samples_split * */ __pyx_t_11 = __pyx_v_self->__pyx_base.min_weight_leaf; __pyx_v_min_weight_leaf = __pyx_t_11; /* "sklearn/_tree.pyx":2842 * cdef SIZE_t min_samples_leaf = self.min_samples_leaf * cdef double min_weight_leaf = self.min_weight_leaf * cdef SIZE_t min_samples_split = self.min_samples_split # <<<<<<<<<<<<<< * * # Recursive partition (without actual recursion) */ __pyx_t_10 = __pyx_v_self->__pyx_base.min_samples_split; __pyx_v_min_samples_split = __pyx_t_10; /* "sklearn/_tree.pyx":2845 * * # Recursive partition (without actual recursion) * splitter.init(X, y, sample_weight_ptr) # <<<<<<<<<<<<<< * * cdef SIZE_t start */ ((struct __pyx_vtabstruct_7sklearn_5_tree_Splitter *)__pyx_v_splitter->__pyx_vtab)->init(__pyx_v_splitter, __pyx_v_X, __pyx_v_y, __pyx_v_sample_weight_ptr); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2845; __pyx_clineno = __LINE__; goto __pyx_L1_error;} /* "sklearn/_tree.pyx":2852 * cdef SIZE_t parent * cdef bint is_left * cdef SIZE_t n_node_samples = splitter.n_samples # <<<<<<<<<<<<<< * cdef double weighted_n_samples = splitter.weighted_n_samples * cdef double weighted_n_node_samples */ __pyx_t_10 = __pyx_v_splitter->n_samples; __pyx_v_n_node_samples = __pyx_t_10; /* "sklearn/_tree.pyx":2853 * cdef bint is_left * cdef SIZE_t n_node_samples = splitter.n_samples * cdef double weighted_n_samples = splitter.weighted_n_samples # <<<<<<<<<<<<<< * cdef double weighted_n_node_samples * cdef SplitRecord split */ __pyx_t_11 = __pyx_v_splitter->weighted_n_samples; __pyx_v_weighted_n_samples = __pyx_t_11; /* "sklearn/_tree.pyx":2859 * * cdef double threshold * cdef double impurity = INFINITY # <<<<<<<<<<<<<< * cdef SIZE_t n_constant_features * cdef bint is_leaf */ __pyx_v_impurity = __pyx_v_7sklearn_5_tree_INFINITY; /* "sklearn/_tree.pyx":2862 * cdef SIZE_t n_constant_features * cdef bint is_leaf * cdef bint first = 1 # <<<<<<<<<<<<<< * cdef SIZE_t max_depth_seen = -1 * cdef int rc = 0 */ __pyx_v_first = 1; /* "sklearn/_tree.pyx":2863 * cdef bint is_leaf * cdef bint first = 1 * cdef SIZE_t max_depth_seen = -1 # <<<<<<<<<<<<<< * cdef int rc = 0 * */ __pyx_v_max_depth_seen = -1; /* "sklearn/_tree.pyx":2864 * cdef bint first = 1 * cdef SIZE_t max_depth_seen = -1 * cdef int rc = 0 # <<<<<<<<<<<<<< * * cdef Stack stack = Stack(INITIAL_STACK_SIZE) */ __pyx_v_rc = 0; /* "sklearn/_tree.pyx":2866 * cdef int rc = 0 * * cdef Stack stack = Stack(INITIAL_STACK_SIZE) # <<<<<<<<<<<<<< * cdef StackRecord stack_record * */ __pyx_t_1 = __Pyx_PyInt_From_Py_intptr_t(__pyx_v_7sklearn_5_tree_INITIAL_STACK_SIZE); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2866; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_1); __pyx_t_6 = PyTuple_New(1); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2866; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_6, 0, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)((PyObject*)__pyx_ptype_7sklearn_5_tree_Stack)), __pyx_t_6, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2866; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_v_stack = ((struct __pyx_obj_7sklearn_5_tree_Stack *)__pyx_t_1); __pyx_t_1 = 0; /* "sklearn/_tree.pyx":2870 * * # push root node onto stack * rc = stack.push(0, n_node_samples, 0, _TREE_UNDEFINED, 0, INFINITY, 0) # <<<<<<<<<<<<<< * if rc == -1: * # got return code -1 - out-of-memory */ __pyx_v_rc = ((struct __pyx_vtabstruct_7sklearn_5_tree_Stack *)__pyx_v_stack->__pyx_vtab)->push(__pyx_v_stack, 0, __pyx_v_n_node_samples, 0, __pyx_v_7sklearn_5_tree__TREE_UNDEFINED, 0, __pyx_v_7sklearn_5_tree_INFINITY, 0); /* "sklearn/_tree.pyx":2871 * # push root node onto stack * rc = stack.push(0, n_node_samples, 0, _TREE_UNDEFINED, 0, INFINITY, 0) * if rc == -1: # <<<<<<<<<<<<<< * # got return code -1 - out-of-memory * raise MemoryError() */ __pyx_t_9 = ((__pyx_v_rc == -1) != 0); if (__pyx_t_9) { /* "sklearn/_tree.pyx":2873 * if rc == -1: * # got return code -1 - out-of-memory * raise MemoryError() # <<<<<<<<<<<<<< * * with nogil: */ PyErr_NoMemory(); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2873; __pyx_clineno = __LINE__; goto __pyx_L1_error;} } /* "sklearn/_tree.pyx":2875 * raise MemoryError() * * with nogil: # <<<<<<<<<<<<<< * while not stack.is_empty(): * stack.pop(&stack_record) */ { #ifdef WITH_THREAD PyThreadState *_save; Py_UNBLOCK_THREADS #endif /*try:*/ { /* "sklearn/_tree.pyx":2876 * * with nogil: * while not stack.is_empty(): # <<<<<<<<<<<<<< * stack.pop(&stack_record) * */ while (1) { __pyx_t_9 = ((!(((struct __pyx_vtabstruct_7sklearn_5_tree_Stack *)__pyx_v_stack->__pyx_vtab)->is_empty(__pyx_v_stack) != 0)) != 0); if (!__pyx_t_9) break; /* "sklearn/_tree.pyx":2877 * with nogil: * while not stack.is_empty(): * stack.pop(&stack_record) # <<<<<<<<<<<<<< * * start = stack_record.start */ ((struct __pyx_vtabstruct_7sklearn_5_tree_Stack *)__pyx_v_stack->__pyx_vtab)->pop(__pyx_v_stack, (&__pyx_v_stack_record)); /* "sklearn/_tree.pyx":2879 * stack.pop(&stack_record) * * start = stack_record.start # <<<<<<<<<<<<<< * end = stack_record.end * depth = stack_record.depth */ __pyx_t_10 = __pyx_v_stack_record.start; __pyx_v_start = __pyx_t_10; /* "sklearn/_tree.pyx":2880 * * start = stack_record.start * end = stack_record.end # <<<<<<<<<<<<<< * depth = stack_record.depth * parent = stack_record.parent */ __pyx_t_10 = __pyx_v_stack_record.end; __pyx_v_end = __pyx_t_10; /* "sklearn/_tree.pyx":2881 * start = stack_record.start * end = stack_record.end * depth = stack_record.depth # <<<<<<<<<<<<<< * parent = stack_record.parent * is_left = stack_record.is_left */ __pyx_t_10 = __pyx_v_stack_record.depth; __pyx_v_depth = __pyx_t_10; /* "sklearn/_tree.pyx":2882 * end = stack_record.end * depth = stack_record.depth * parent = stack_record.parent # <<<<<<<<<<<<<< * is_left = stack_record.is_left * impurity = stack_record.impurity */ __pyx_t_10 = __pyx_v_stack_record.parent; __pyx_v_parent = __pyx_t_10; /* "sklearn/_tree.pyx":2883 * depth = stack_record.depth * parent = stack_record.parent * is_left = stack_record.is_left # <<<<<<<<<<<<<< * impurity = stack_record.impurity * n_constant_features = stack_record.n_constant_features */ __pyx_t_9 = __pyx_v_stack_record.is_left; __pyx_v_is_left = __pyx_t_9; /* "sklearn/_tree.pyx":2884 * parent = stack_record.parent * is_left = stack_record.is_left * impurity = stack_record.impurity # <<<<<<<<<<<<<< * n_constant_features = stack_record.n_constant_features * */ __pyx_t_11 = __pyx_v_stack_record.impurity; __pyx_v_impurity = __pyx_t_11; /* "sklearn/_tree.pyx":2885 * is_left = stack_record.is_left * impurity = stack_record.impurity * n_constant_features = stack_record.n_constant_features # <<<<<<<<<<<<<< * * n_node_samples = end - start */ __pyx_t_10 = __pyx_v_stack_record.n_constant_features; __pyx_v_n_constant_features = __pyx_t_10; /* "sklearn/_tree.pyx":2887 * n_constant_features = stack_record.n_constant_features * * n_node_samples = end - start # <<<<<<<<<<<<<< * splitter.node_reset(start, end, &weighted_n_node_samples) * */ __pyx_v_n_node_samples = (__pyx_v_end - __pyx_v_start); /* "sklearn/_tree.pyx":2888 * * n_node_samples = end - start * splitter.node_reset(start, end, &weighted_n_node_samples) # <<<<<<<<<<<<<< * * is_leaf = ((depth >= max_depth) or */ ((struct __pyx_vtabstruct_7sklearn_5_tree_Splitter *)__pyx_v_splitter->__pyx_vtab)->node_reset(__pyx_v_splitter, __pyx_v_start, __pyx_v_end, (&__pyx_v_weighted_n_node_samples)); /* "sklearn/_tree.pyx":2890 * splitter.node_reset(start, end, &weighted_n_node_samples) * * is_leaf = ((depth >= max_depth) or # <<<<<<<<<<<<<< * (n_node_samples < min_samples_split) or * (n_node_samples < 2 * min_samples_leaf) or */ __pyx_t_8 = ((__pyx_v_depth >= __pyx_v_max_depth) != 0); if (!__pyx_t_8) { } else { __pyx_t_9 = __pyx_t_8; goto __pyx_L13_bool_binop_done; } /* "sklearn/_tree.pyx":2891 * * is_leaf = ((depth >= max_depth) or * (n_node_samples < min_samples_split) or # <<<<<<<<<<<<<< * (n_node_samples < 2 * min_samples_leaf) or * (weighted_n_node_samples < min_weight_leaf)) */ __pyx_t_8 = ((__pyx_v_n_node_samples < __pyx_v_min_samples_split) != 0); if (!__pyx_t_8) { } else { __pyx_t_9 = __pyx_t_8; goto __pyx_L13_bool_binop_done; } /* "sklearn/_tree.pyx":2892 * is_leaf = ((depth >= max_depth) or * (n_node_samples < min_samples_split) or * (n_node_samples < 2 * min_samples_leaf) or # <<<<<<<<<<<<<< * (weighted_n_node_samples < min_weight_leaf)) * */ __pyx_t_8 = ((__pyx_v_n_node_samples < (2 * __pyx_v_min_samples_leaf)) != 0); if (!__pyx_t_8) { } else { __pyx_t_9 = __pyx_t_8; goto __pyx_L13_bool_binop_done; } /* "sklearn/_tree.pyx":2893 * (n_node_samples < min_samples_split) or * (n_node_samples < 2 * min_samples_leaf) or * (weighted_n_node_samples < min_weight_leaf)) # <<<<<<<<<<<<<< * * if first: */ __pyx_t_8 = ((__pyx_v_weighted_n_node_samples < __pyx_v_min_weight_leaf) != 0); __pyx_t_9 = __pyx_t_8; __pyx_L13_bool_binop_done:; __pyx_v_is_leaf = __pyx_t_9; /* "sklearn/_tree.pyx":2895 * (weighted_n_node_samples < min_weight_leaf)) * * if first: # <<<<<<<<<<<<<< * impurity = splitter.node_impurity() * first = 0 */ __pyx_t_9 = (__pyx_v_first != 0); if (__pyx_t_9) { /* "sklearn/_tree.pyx":2896 * * if first: * impurity = splitter.node_impurity() # <<<<<<<<<<<<<< * first = 0 * */ __pyx_v_impurity = ((struct __pyx_vtabstruct_7sklearn_5_tree_Splitter *)__pyx_v_splitter->__pyx_vtab)->node_impurity(__pyx_v_splitter); /* "sklearn/_tree.pyx":2897 * if first: * impurity = splitter.node_impurity() * first = 0 # <<<<<<<<<<<<<< * * is_leaf = is_leaf or (impurity <= MIN_IMPURITY_SPLIT) */ __pyx_v_first = 0; goto __pyx_L17; } __pyx_L17:; /* "sklearn/_tree.pyx":2899 * first = 0 * * is_leaf = is_leaf or (impurity <= MIN_IMPURITY_SPLIT) # <<<<<<<<<<<<<< * * if not is_leaf: */ __pyx_t_8 = (__pyx_v_is_leaf != 0); if (!__pyx_t_8) { } else { __pyx_t_9 = __pyx_t_8; goto __pyx_L18_bool_binop_done; } __pyx_t_8 = ((__pyx_v_impurity <= __pyx_v_7sklearn_5_tree_MIN_IMPURITY_SPLIT) != 0); __pyx_t_9 = __pyx_t_8; __pyx_L18_bool_binop_done:; __pyx_v_is_leaf = __pyx_t_9; /* "sklearn/_tree.pyx":2901 * is_leaf = is_leaf or (impurity <= MIN_IMPURITY_SPLIT) * * if not is_leaf: # <<<<<<<<<<<<<< * splitter.node_split(impurity, &split, &n_constant_features) * is_leaf = is_leaf or (split.pos >= end) */ __pyx_t_9 = ((!(__pyx_v_is_leaf != 0)) != 0); if (__pyx_t_9) { /* "sklearn/_tree.pyx":2902 * * if not is_leaf: * splitter.node_split(impurity, &split, &n_constant_features) # <<<<<<<<<<<<<< * is_leaf = is_leaf or (split.pos >= end) * */ ((struct __pyx_vtabstruct_7sklearn_5_tree_Splitter *)__pyx_v_splitter->__pyx_vtab)->node_split(__pyx_v_splitter, __pyx_v_impurity, (&__pyx_v_split), (&__pyx_v_n_constant_features)); /* "sklearn/_tree.pyx":2903 * if not is_leaf: * splitter.node_split(impurity, &split, &n_constant_features) * is_leaf = is_leaf or (split.pos >= end) # <<<<<<<<<<<<<< * * node_id = tree._add_node(parent, is_left, is_leaf, split.feature, */ __pyx_t_8 = (__pyx_v_is_leaf != 0); if (!__pyx_t_8) { } else { __pyx_t_9 = __pyx_t_8; goto __pyx_L21_bool_binop_done; } __pyx_t_8 = ((__pyx_v_split.pos >= __pyx_v_end) != 0); __pyx_t_9 = __pyx_t_8; __pyx_L21_bool_binop_done:; __pyx_v_is_leaf = __pyx_t_9; goto __pyx_L20; } __pyx_L20:; /* "sklearn/_tree.pyx":2905 * is_leaf = is_leaf or (split.pos >= end) * * node_id = tree._add_node(parent, is_left, is_leaf, split.feature, # <<<<<<<<<<<<<< * split.threshold, impurity, n_node_samples, * weighted_n_node_samples) */ __pyx_v_node_id = ((struct __pyx_vtabstruct_7sklearn_5_tree_Tree *)__pyx_v_tree->__pyx_vtab)->_add_node(__pyx_v_tree, __pyx_v_parent, __pyx_v_is_left, __pyx_v_is_leaf, __pyx_v_split.feature, __pyx_v_split.threshold, __pyx_v_impurity, __pyx_v_n_node_samples, __pyx_v_weighted_n_node_samples); /* "sklearn/_tree.pyx":2909 * weighted_n_node_samples) * * if is_leaf: # <<<<<<<<<<<<<< * # Don't store value for internal nodes * splitter.node_value(tree.value + */ __pyx_t_9 = (__pyx_v_is_leaf != 0); if (__pyx_t_9) { /* "sklearn/_tree.pyx":2911 * if is_leaf: * # Don't store value for internal nodes * splitter.node_value(tree.value + # <<<<<<<<<<<<<< * node_id * tree.value_stride) * */ ((struct __pyx_vtabstruct_7sklearn_5_tree_Splitter *)__pyx_v_splitter->__pyx_vtab)->node_value(__pyx_v_splitter, (__pyx_v_tree->value + (__pyx_v_node_id * __pyx_v_tree->value_stride))); goto __pyx_L23; } /*else*/ { /* "sklearn/_tree.pyx":2916 * else: * # Push right child on stack * rc = stack.push(split.pos, end, depth + 1, node_id, 0, # <<<<<<<<<<<<<< * split.impurity_right, n_constant_features) * if rc == -1: */ __pyx_v_rc = ((struct __pyx_vtabstruct_7sklearn_5_tree_Stack *)__pyx_v_stack->__pyx_vtab)->push(__pyx_v_stack, __pyx_v_split.pos, __pyx_v_end, (__pyx_v_depth + 1), __pyx_v_node_id, 0, __pyx_v_split.impurity_right, __pyx_v_n_constant_features); /* "sklearn/_tree.pyx":2918 * rc = stack.push(split.pos, end, depth + 1, node_id, 0, * split.impurity_right, n_constant_features) * if rc == -1: # <<<<<<<<<<<<<< * break * */ __pyx_t_9 = ((__pyx_v_rc == -1) != 0); if (__pyx_t_9) { /* "sklearn/_tree.pyx":2919 * split.impurity_right, n_constant_features) * if rc == -1: * break # <<<<<<<<<<<<<< * * # Push left child on stack */ goto __pyx_L12_break; } /* "sklearn/_tree.pyx":2922 * * # Push left child on stack * rc = stack.push(start, split.pos, depth + 1, node_id, 1, # <<<<<<<<<<<<<< * split.impurity_left, n_constant_features) * if rc == -1: */ __pyx_v_rc = ((struct __pyx_vtabstruct_7sklearn_5_tree_Stack *)__pyx_v_stack->__pyx_vtab)->push(__pyx_v_stack, __pyx_v_start, __pyx_v_split.pos, (__pyx_v_depth + 1), __pyx_v_node_id, 1, __pyx_v_split.impurity_left, __pyx_v_n_constant_features); /* "sklearn/_tree.pyx":2924 * rc = stack.push(start, split.pos, depth + 1, node_id, 1, * split.impurity_left, n_constant_features) * if rc == -1: # <<<<<<<<<<<<<< * break * */ __pyx_t_9 = ((__pyx_v_rc == -1) != 0); if (__pyx_t_9) { /* "sklearn/_tree.pyx":2925 * split.impurity_left, n_constant_features) * if rc == -1: * break # <<<<<<<<<<<<<< * * if depth > max_depth_seen: */ goto __pyx_L12_break; } } __pyx_L23:; /* "sklearn/_tree.pyx":2927 * break * * if depth > max_depth_seen: # <<<<<<<<<<<<<< * max_depth_seen = depth * */ __pyx_t_9 = ((__pyx_v_depth > __pyx_v_max_depth_seen) != 0); if (__pyx_t_9) { /* "sklearn/_tree.pyx":2928 * * if depth > max_depth_seen: * max_depth_seen = depth # <<<<<<<<<<<<<< * * if rc >= 0: */ __pyx_v_max_depth_seen = __pyx_v_depth; goto __pyx_L26; } __pyx_L26:; } __pyx_L12_break:; /* "sklearn/_tree.pyx":2930 * max_depth_seen = depth * * if rc >= 0: # <<<<<<<<<<<<<< * rc = tree._resize_c(tree.node_count) * */ __pyx_t_9 = ((__pyx_v_rc >= 0) != 0); if (__pyx_t_9) { /* "sklearn/_tree.pyx":2931 * * if rc >= 0: * rc = tree._resize_c(tree.node_count) # <<<<<<<<<<<<<< * * if rc >= 0: */ __pyx_t_13.__pyx_n = 1; 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if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3005; __pyx_clineno = __LINE__; goto __pyx_L1_error;} /* "sklearn/_tree.pyx":3007 * tree._resize(init_capacity) * * with nogil: # <<<<<<<<<<<<<< * # add root to frontier * rc = self._add_split_node(splitter, tree, 0, n_node_samples, */ { #ifdef WITH_THREAD PyThreadState *_save; Py_UNBLOCK_THREADS #endif /*try:*/ { /* "sklearn/_tree.pyx":3009 * with nogil: * # add root to frontier * rc = self._add_split_node(splitter, tree, 0, n_node_samples, # <<<<<<<<<<<<<< * INFINITY, IS_FIRST, IS_LEFT, NULL, 0, * &split_node_left) */ __pyx_v_rc = __pyx_f_7sklearn_5_tree_20BestFirstTreeBuilder__add_split_node(__pyx_v_self, __pyx_v_splitter, __pyx_v_tree, 0, __pyx_v_n_node_samples, __pyx_v_7sklearn_5_tree_INFINITY, __pyx_v_7sklearn_5_tree_IS_FIRST, __pyx_v_7sklearn_5_tree_IS_LEFT, NULL, 0, (&__pyx_v_split_node_left)); /* "sklearn/_tree.pyx":3012 * INFINITY, IS_FIRST, IS_LEFT, NULL, 0, * &split_node_left) * if rc >= 0: # <<<<<<<<<<<<<< * rc = _add_to_frontier(&split_node_left, frontier) * if rc == -1: */ __pyx_t_9 = ((__pyx_v_rc >= 0) != 0); 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/* "sklearn/_tree.pyx":3028 * node.left_child = _TREE_LEAF * node.right_child = _TREE_LEAF * node.feature = _TREE_UNDEFINED # <<<<<<<<<<<<<< * node.threshold = _TREE_UNDEFINED * */ __pyx_v_node->feature = __pyx_v_7sklearn_5_tree__TREE_UNDEFINED; /* "sklearn/_tree.pyx":3029 * node.right_child = _TREE_LEAF * node.feature = _TREE_UNDEFINED * node.threshold = _TREE_UNDEFINED # <<<<<<<<<<<<<< * * else: */ __pyx_v_node->threshold = __pyx_v_7sklearn_5_tree__TREE_UNDEFINED; goto __pyx_L18; } /*else*/ { /* "sklearn/_tree.pyx":3035 * * # Decrement number of split nodes available * max_split_nodes -= 1 # <<<<<<<<<<<<<< * * # Compute left split node */ __pyx_v_max_split_nodes = (__pyx_v_max_split_nodes - 1); /* "sklearn/_tree.pyx":3038 * * # Compute left split node * rc = self._add_split_node(splitter, tree, # <<<<<<<<<<<<<< * record.start, record.pos, * record.impurity_left, */ __pyx_v_rc = __pyx_f_7sklearn_5_tree_20BestFirstTreeBuilder__add_split_node(__pyx_v_self, __pyx_v_splitter, __pyx_v_tree, __pyx_v_record.start, __pyx_v_record.pos, __pyx_v_record.impurity_left, __pyx_v_7sklearn_5_tree_IS_NOT_FIRST, __pyx_v_7sklearn_5_tree_IS_LEFT, __pyx_v_node, (__pyx_v_record.depth + 1), (&__pyx_v_split_node_left)); 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/* "sklearn/_tree.pyx":3346 * return -1 * self.nodes = ptr * ptr = realloc(self.value, # <<<<<<<<<<<<<< * capacity * self.value_stride * sizeof(double)) * if ptr == NULL: */ __pyx_v_ptr = realloc(__pyx_v_self->value, ((__pyx_v_capacity * __pyx_v_self->value_stride) * (sizeof(double)))); /* "sklearn/_tree.pyx":3348 * ptr = realloc(self.value, * capacity * self.value_stride * sizeof(double)) * if ptr == NULL: # <<<<<<<<<<<<<< * return -1 * self.value = ptr */ __pyx_t_1 = ((__pyx_v_ptr == NULL) != 0); if (__pyx_t_1) { /* "sklearn/_tree.pyx":3349 * capacity * self.value_stride * sizeof(double)) * if ptr == NULL: * return -1 # <<<<<<<<<<<<<< * self.value = ptr * */ __pyx_r = -1; goto __pyx_L0; } /* "sklearn/_tree.pyx":3350 * if ptr == NULL: * return -1 * self.value = ptr # <<<<<<<<<<<<<< * * # value memory is initialised to 0 to enable classifier argmax */ __pyx_v_self->value = ((double *)__pyx_v_ptr); /* "sklearn/_tree.pyx":3353 * * # value memory is initialised to 0 to enable classifier argmax * if capacity > self.capacity: # <<<<<<<<<<<<<< * memset((self.value + self.capacity * self.value_stride), 0, * (capacity - self.capacity) * self.value_stride * */ __pyx_t_1 = ((__pyx_v_capacity > __pyx_v_self->capacity) != 0); 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} static PyMethodDef __pyx_methods_7sklearn_5_tree_Stack[] = { {0, 0, 0, 0} }; static PyTypeObject __pyx_type_7sklearn_5_tree_Stack = { PyVarObject_HEAD_INIT(0, 0) "sklearn._tree.Stack", /*tp_name*/ sizeof(struct __pyx_obj_7sklearn_5_tree_Stack), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_7sklearn_5_tree_Stack, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/ "A LIFO data structure.\n\n Attributes\n ----------\n capacity : SIZE_t\n The elements the stack can hold; if more added then ``self.stack_``\n needs to be resized.\n\n top : SIZE_t\n The number of elements currently on the stack.\n\n stack : StackRecord pointer\n The stack of records (upward in the stack corresponds to the right).\n ", /*tp_doc*/ 0, /*tp_traverse*/ 0, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_7sklearn_5_tree_Stack, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_7sklearn_5_tree_Stack, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static struct __pyx_vtabstruct_7sklearn_5_tree_PriorityHeap __pyx_vtable_7sklearn_5_tree_PriorityHeap; static PyObject *__pyx_tp_new_7sklearn_5_tree_PriorityHeap(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_obj_7sklearn_5_tree_PriorityHeap *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_obj_7sklearn_5_tree_PriorityHeap *)o); p->__pyx_vtab = __pyx_vtabptr_7sklearn_5_tree_PriorityHeap; if (unlikely(__pyx_pw_7sklearn_5_tree_12PriorityHeap_1__cinit__(o, a, k) < 0)) { Py_DECREF(o); o = 0; } return o; } static void __pyx_tp_dealloc_7sklearn_5_tree_PriorityHeap(PyObject *o) { #if PY_VERSION_HEX >= 0x030400a1 if (unlikely(Py_TYPE(o)->tp_finalize) && (!PyType_IS_GC(Py_TYPE(o)) || !_PyGC_FINALIZED(o))) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); ++Py_REFCNT(o); __pyx_pw_7sklearn_5_tree_12PriorityHeap_3__dealloc__(o); --Py_REFCNT(o); PyErr_Restore(etype, eval, etb); } (*Py_TYPE(o)->tp_free)(o); } static PyMethodDef __pyx_methods_7sklearn_5_tree_PriorityHeap[] = { {0, 0, 0, 0} }; static PyTypeObject __pyx_type_7sklearn_5_tree_PriorityHeap = { PyVarObject_HEAD_INIT(0, 0) "sklearn._tree.PriorityHeap", /*tp_name*/ sizeof(struct __pyx_obj_7sklearn_5_tree_PriorityHeap), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_7sklearn_5_tree_PriorityHeap, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/ "A priority queue implemented as a binary heap.\n\n The heap invariant is that the impurity improvement of the parent record\n is larger then the impurity improvement of the children.\n\n Attributes\n ----------\n capacity : SIZE_t\n The capacity of the heap\n\n heap_ptr : SIZE_t\n The water mark of the heap; 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The following invariant holds ``heap_ptr < capacity``.\n\n heap_ : PriorityHeapRecord*\n The array of heap records. The maximum element is on the left;\n the heap grows from left to right\n ", /*tp_doc*/ 0, /*tp_traverse*/ 0, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_7sklearn_5_tree_PriorityHeap, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_7sklearn_5_tree_PriorityHeap, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static struct __pyx_vtabstruct_7sklearn_5_tree_Criterion __pyx_vtable_7sklearn_5_tree_Criterion; static PyObject *__pyx_tp_new_7sklearn_5_tree_Criterion(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { struct __pyx_obj_7sklearn_5_tree_Criterion *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_obj_7sklearn_5_tree_Criterion *)o); p->__pyx_vtab = __pyx_vtabptr_7sklearn_5_tree_Criterion; return o; } static void __pyx_tp_dealloc_7sklearn_5_tree_Criterion(PyObject *o) { #if PY_VERSION_HEX >= 0x030400a1 if (unlikely(Py_TYPE(o)->tp_finalize) && (!PyType_IS_GC(Py_TYPE(o)) || !_PyGC_FINALIZED(o))) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif (*Py_TYPE(o)->tp_free)(o); } static PyTypeObject __pyx_type_7sklearn_5_tree_Criterion = { PyVarObject_HEAD_INIT(0, 0) "sklearn._tree.Criterion", /*tp_name*/ sizeof(struct __pyx_obj_7sklearn_5_tree_Criterion), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_7sklearn_5_tree_Criterion, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/ "Interface for impurity criteria.", /*tp_doc*/ 0, /*tp_traverse*/ 0, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ 0, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_7sklearn_5_tree_Criterion, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static struct __pyx_vtabstruct_7sklearn_5_tree_Splitter __pyx_vtable_7sklearn_5_tree_Splitter; static PyObject *__pyx_tp_new_7sklearn_5_tree_Splitter(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_obj_7sklearn_5_tree_Splitter *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_obj_7sklearn_5_tree_Splitter *)o); p->__pyx_vtab = __pyx_vtabptr_7sklearn_5_tree_Splitter; p->criterion = ((struct __pyx_obj_7sklearn_5_tree_Criterion *)Py_None); Py_INCREF(Py_None); p->random_state = Py_None; Py_INCREF(Py_None); if (unlikely(__pyx_pw_7sklearn_5_tree_8Splitter_1__cinit__(o, a, k) < 0)) { Py_DECREF(o); o = 0; } return o; } static void __pyx_tp_dealloc_7sklearn_5_tree_Splitter(PyObject *o) { struct __pyx_obj_7sklearn_5_tree_Splitter *p = (struct __pyx_obj_7sklearn_5_tree_Splitter *)o; #if PY_VERSION_HEX >= 0x030400a1 if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif PyObject_GC_UnTrack(o); { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); ++Py_REFCNT(o); __pyx_pw_7sklearn_5_tree_8Splitter_3__dealloc__(o); --Py_REFCNT(o); PyErr_Restore(etype, eval, etb); } Py_CLEAR(p->criterion); Py_CLEAR(p->random_state); (*Py_TYPE(o)->tp_free)(o); } static int __pyx_tp_traverse_7sklearn_5_tree_Splitter(PyObject *o, visitproc v, void *a) { int e; struct __pyx_obj_7sklearn_5_tree_Splitter *p = (struct __pyx_obj_7sklearn_5_tree_Splitter *)o; if (p->criterion) { e = (*v)(((PyObject*)p->criterion), a); if (e) return e; } if (p->random_state) { e = (*v)(p->random_state, a); if (e) return e; } return 0; } static int __pyx_tp_clear_7sklearn_5_tree_Splitter(PyObject *o) { PyObject* tmp; struct __pyx_obj_7sklearn_5_tree_Splitter *p = (struct __pyx_obj_7sklearn_5_tree_Splitter *)o; tmp = ((PyObject*)p->criterion); p->criterion = ((struct __pyx_obj_7sklearn_5_tree_Criterion *)Py_None); Py_INCREF(Py_None); Py_XDECREF(tmp); tmp = ((PyObject*)p->random_state); p->random_state = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); return 0; } static PyObject *__pyx_getprop_7sklearn_5_tree_8Splitter_criterion(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_7sklearn_5_tree_8Splitter_9criterion_1__get__(o); } static int __pyx_setprop_7sklearn_5_tree_8Splitter_criterion(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) { if (v) { return __pyx_pw_7sklearn_5_tree_8Splitter_9criterion_3__set__(o, v); } else { return __pyx_pw_7sklearn_5_tree_8Splitter_9criterion_5__del__(o); } } static PyObject *__pyx_getprop_7sklearn_5_tree_8Splitter_max_features(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_7sklearn_5_tree_8Splitter_12max_features_1__get__(o); } static int __pyx_setprop_7sklearn_5_tree_8Splitter_max_features(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) { if (v) { return __pyx_pw_7sklearn_5_tree_8Splitter_12max_features_3__set__(o, v); } else { PyErr_SetString(PyExc_NotImplementedError, "__del__"); return -1; } } static PyObject *__pyx_getprop_7sklearn_5_tree_8Splitter_min_samples_leaf(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_7sklearn_5_tree_8Splitter_16min_samples_leaf_1__get__(o); } static int __pyx_setprop_7sklearn_5_tree_8Splitter_min_samples_leaf(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) { if (v) { return __pyx_pw_7sklearn_5_tree_8Splitter_16min_samples_leaf_3__set__(o, v); } else { PyErr_SetString(PyExc_NotImplementedError, "__del__"); return -1; } } static PyObject *__pyx_getprop_7sklearn_5_tree_8Splitter_min_weight_leaf(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_7sklearn_5_tree_8Splitter_15min_weight_leaf_1__get__(o); } static int __pyx_setprop_7sklearn_5_tree_8Splitter_min_weight_leaf(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) { if (v) { return __pyx_pw_7sklearn_5_tree_8Splitter_15min_weight_leaf_3__set__(o, v); } else { PyErr_SetString(PyExc_NotImplementedError, "__del__"); return -1; } } static PyMethodDef __pyx_methods_7sklearn_5_tree_Splitter[] = { {"__getstate__", (PyCFunction)__pyx_pw_7sklearn_5_tree_8Splitter_5__getstate__, METH_NOARGS, 0}, {"__setstate__", (PyCFunction)__pyx_pw_7sklearn_5_tree_8Splitter_7__setstate__, METH_O, 0}, {0, 0, 0, 0} }; static struct PyGetSetDef __pyx_getsets_7sklearn_5_tree_Splitter[] = { {(char *)"criterion", __pyx_getprop_7sklearn_5_tree_8Splitter_criterion, __pyx_setprop_7sklearn_5_tree_8Splitter_criterion, 0, 0}, {(char *)"max_features", __pyx_getprop_7sklearn_5_tree_8Splitter_max_features, __pyx_setprop_7sklearn_5_tree_8Splitter_max_features, 0, 0}, {(char *)"min_samples_leaf", __pyx_getprop_7sklearn_5_tree_8Splitter_min_samples_leaf, __pyx_setprop_7sklearn_5_tree_8Splitter_min_samples_leaf, 0, 0}, {(char *)"min_weight_leaf", __pyx_getprop_7sklearn_5_tree_8Splitter_min_weight_leaf, __pyx_setprop_7sklearn_5_tree_8Splitter_min_weight_leaf, 0, 0}, {0, 0, 0, 0, 0} }; static PyTypeObject __pyx_type_7sklearn_5_tree_Splitter = { PyVarObject_HEAD_INIT(0, 0) "sklearn._tree.Splitter", /*tp_name*/ sizeof(struct __pyx_obj_7sklearn_5_tree_Splitter), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_7sklearn_5_tree_Splitter, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ 0, /*tp_doc*/ __pyx_tp_traverse_7sklearn_5_tree_Splitter, /*tp_traverse*/ __pyx_tp_clear_7sklearn_5_tree_Splitter, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_7sklearn_5_tree_Splitter, /*tp_methods*/ 0, /*tp_members*/ __pyx_getsets_7sklearn_5_tree_Splitter, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_7sklearn_5_tree_Splitter, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static struct __pyx_vtabstruct_7sklearn_5_tree_Tree __pyx_vtable_7sklearn_5_tree_Tree; static PyObject *__pyx_tp_new_7sklearn_5_tree_Tree(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_obj_7sklearn_5_tree_Tree *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_obj_7sklearn_5_tree_Tree *)o); p->__pyx_vtab = __pyx_vtabptr_7sklearn_5_tree_Tree; if (unlikely(__pyx_pw_7sklearn_5_tree_4Tree_1__cinit__(o, a, k) < 0)) { Py_DECREF(o); o = 0; } return o; } static void __pyx_tp_dealloc_7sklearn_5_tree_Tree(PyObject *o) { #if PY_VERSION_HEX >= 0x030400a1 if (unlikely(Py_TYPE(o)->tp_finalize) && (!PyType_IS_GC(Py_TYPE(o)) || !_PyGC_FINALIZED(o))) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); ++Py_REFCNT(o); __pyx_pw_7sklearn_5_tree_4Tree_3__dealloc__(o); --Py_REFCNT(o); PyErr_Restore(etype, eval, etb); } (*Py_TYPE(o)->tp_free)(o); } static PyObject *__pyx_getprop_7sklearn_5_tree_4Tree_n_classes(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_7sklearn_5_tree_4Tree_9n_classes_1__get__(o); } static PyObject *__pyx_getprop_7sklearn_5_tree_4Tree_children_left(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_7sklearn_5_tree_4Tree_13children_left_1__get__(o); } static PyObject *__pyx_getprop_7sklearn_5_tree_4Tree_children_right(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_7sklearn_5_tree_4Tree_14children_right_1__get__(o); } static PyObject *__pyx_getprop_7sklearn_5_tree_4Tree_feature(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_7sklearn_5_tree_4Tree_7feature_1__get__(o); } static PyObject *__pyx_getprop_7sklearn_5_tree_4Tree_threshold(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_7sklearn_5_tree_4Tree_9threshold_1__get__(o); } static PyObject *__pyx_getprop_7sklearn_5_tree_4Tree_impurity(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_7sklearn_5_tree_4Tree_8impurity_1__get__(o); } static PyObject *__pyx_getprop_7sklearn_5_tree_4Tree_n_node_samples(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_7sklearn_5_tree_4Tree_14n_node_samples_1__get__(o); } static PyObject *__pyx_getprop_7sklearn_5_tree_4Tree_weighted_n_node_samples(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_7sklearn_5_tree_4Tree_23weighted_n_node_samples_1__get__(o); } static PyObject *__pyx_getprop_7sklearn_5_tree_4Tree_value(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_7sklearn_5_tree_4Tree_5value_1__get__(o); } static PyObject *__pyx_getprop_7sklearn_5_tree_4Tree_n_features(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_7sklearn_5_tree_4Tree_10n_features_1__get__(o); } static int __pyx_setprop_7sklearn_5_tree_4Tree_n_features(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) { if (v) { return __pyx_pw_7sklearn_5_tree_4Tree_10n_features_3__set__(o, v); } else { PyErr_SetString(PyExc_NotImplementedError, "__del__"); return -1; } } static PyObject *__pyx_getprop_7sklearn_5_tree_4Tree_n_outputs(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_7sklearn_5_tree_4Tree_9n_outputs_1__get__(o); } static int __pyx_setprop_7sklearn_5_tree_4Tree_n_outputs(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) { if (v) { return __pyx_pw_7sklearn_5_tree_4Tree_9n_outputs_3__set__(o, v); } else { PyErr_SetString(PyExc_NotImplementedError, "__del__"); return -1; } } static PyObject *__pyx_getprop_7sklearn_5_tree_4Tree_max_n_classes(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_7sklearn_5_tree_4Tree_13max_n_classes_1__get__(o); } static int __pyx_setprop_7sklearn_5_tree_4Tree_max_n_classes(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) { if (v) { return __pyx_pw_7sklearn_5_tree_4Tree_13max_n_classes_3__set__(o, v); } else { PyErr_SetString(PyExc_NotImplementedError, "__del__"); return -1; } } static PyObject *__pyx_getprop_7sklearn_5_tree_4Tree_max_depth(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_7sklearn_5_tree_4Tree_9max_depth_1__get__(o); } static int __pyx_setprop_7sklearn_5_tree_4Tree_max_depth(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) { if (v) { return __pyx_pw_7sklearn_5_tree_4Tree_9max_depth_3__set__(o, v); } else { PyErr_SetString(PyExc_NotImplementedError, "__del__"); return -1; } } static PyObject *__pyx_getprop_7sklearn_5_tree_4Tree_node_count(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_7sklearn_5_tree_4Tree_10node_count_1__get__(o); } static int __pyx_setprop_7sklearn_5_tree_4Tree_node_count(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) { if (v) { return __pyx_pw_7sklearn_5_tree_4Tree_10node_count_3__set__(o, v); } else { PyErr_SetString(PyExc_NotImplementedError, "__del__"); return -1; } } static PyObject *__pyx_getprop_7sklearn_5_tree_4Tree_capacity(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_7sklearn_5_tree_4Tree_8capacity_1__get__(o); } static int __pyx_setprop_7sklearn_5_tree_4Tree_capacity(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) { if (v) { return __pyx_pw_7sklearn_5_tree_4Tree_8capacity_3__set__(o, v); } else { PyErr_SetString(PyExc_NotImplementedError, "__del__"); return -1; } } static PyMethodDef __pyx_methods_7sklearn_5_tree_Tree[] = { {"__reduce__", (PyCFunction)__pyx_pw_7sklearn_5_tree_4Tree_5__reduce__, METH_NOARGS, __pyx_doc_7sklearn_5_tree_4Tree_4__reduce__}, {"__getstate__", (PyCFunction)__pyx_pw_7sklearn_5_tree_4Tree_7__getstate__, METH_NOARGS, __pyx_doc_7sklearn_5_tree_4Tree_6__getstate__}, {"__setstate__", (PyCFunction)__pyx_pw_7sklearn_5_tree_4Tree_9__setstate__, METH_O, __pyx_doc_7sklearn_5_tree_4Tree_8__setstate__}, {"predict", (PyCFunction)__pyx_pw_7sklearn_5_tree_4Tree_11predict, METH_O, __pyx_doc_7sklearn_5_tree_4Tree_10predict}, {"apply", (PyCFunction)__pyx_pw_7sklearn_5_tree_4Tree_13apply, METH_O, __pyx_doc_7sklearn_5_tree_4Tree_12apply}, {"compute_feature_importances", (PyCFunction)__pyx_pw_7sklearn_5_tree_4Tree_15compute_feature_importances, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7sklearn_5_tree_4Tree_14compute_feature_importances}, {0, 0, 0, 0} }; static struct PyGetSetDef __pyx_getsets_7sklearn_5_tree_Tree[] = { {(char *)"n_classes", __pyx_getprop_7sklearn_5_tree_4Tree_n_classes, 0, 0, 0}, {(char *)"children_left", __pyx_getprop_7sklearn_5_tree_4Tree_children_left, 0, 0, 0}, {(char *)"children_right", __pyx_getprop_7sklearn_5_tree_4Tree_children_right, 0, 0, 0}, {(char *)"feature", __pyx_getprop_7sklearn_5_tree_4Tree_feature, 0, 0, 0}, {(char *)"threshold", __pyx_getprop_7sklearn_5_tree_4Tree_threshold, 0, 0, 0}, {(char *)"impurity", __pyx_getprop_7sklearn_5_tree_4Tree_impurity, 0, 0, 0}, {(char *)"n_node_samples", __pyx_getprop_7sklearn_5_tree_4Tree_n_node_samples, 0, 0, 0}, {(char *)"weighted_n_node_samples", __pyx_getprop_7sklearn_5_tree_4Tree_weighted_n_node_samples, 0, 0, 0}, {(char *)"value", __pyx_getprop_7sklearn_5_tree_4Tree_value, 0, 0, 0}, {(char *)"n_features", __pyx_getprop_7sklearn_5_tree_4Tree_n_features, __pyx_setprop_7sklearn_5_tree_4Tree_n_features, 0, 0}, {(char *)"n_outputs", __pyx_getprop_7sklearn_5_tree_4Tree_n_outputs, __pyx_setprop_7sklearn_5_tree_4Tree_n_outputs, 0, 0}, {(char *)"max_n_classes", __pyx_getprop_7sklearn_5_tree_4Tree_max_n_classes, __pyx_setprop_7sklearn_5_tree_4Tree_max_n_classes, 0, 0}, {(char *)"max_depth", __pyx_getprop_7sklearn_5_tree_4Tree_max_depth, __pyx_setprop_7sklearn_5_tree_4Tree_max_depth, 0, 0}, {(char *)"node_count", __pyx_getprop_7sklearn_5_tree_4Tree_node_count, __pyx_setprop_7sklearn_5_tree_4Tree_node_count, 0, 0}, {(char *)"capacity", __pyx_getprop_7sklearn_5_tree_4Tree_capacity, __pyx_setprop_7sklearn_5_tree_4Tree_capacity, 0, 0}, {0, 0, 0, 0, 0} }; static PyTypeObject __pyx_type_7sklearn_5_tree_Tree = { PyVarObject_HEAD_INIT(0, 0) "sklearn._tree.Tree", /*tp_name*/ sizeof(struct __pyx_obj_7sklearn_5_tree_Tree), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_7sklearn_5_tree_Tree, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/ "Array-based representation of a binary decision tree.\n\n The binary tree is represented as a number of parallel arrays. The i-th\n element of each array holds information about the node `i`. Node 0 is the\n tree's root. You can find a detailed description of all arrays in\n `_tree.pxd`. NOTE: Some of the arrays only apply to either leaves or split\n nodes, resp. In this case the values of nodes of the other type are\n arbitrary!\n\n Attributes\n ----------\n node_count : int\n The number of nodes (internal nodes + leaves) in the tree.\n\n capacity : int\n The current capacity (i.e., size) of the arrays, which is at least as\n great as `node_count`.\n\n max_depth : int\n The maximal depth of the tree.\n\n children_left : array of int, shape [node_count]\n children_left[i] holds the node id of the left child of node i.\n For leaves, children_left[i] == TREE_LEAF. Otherwise,\n children_left[i] > i. This child handles the case where\n X[:, feature[i]] <= threshold[i].\n\n children_right : array of int, shape [node_count]\n children_right[i] holds the node id of the right child of node i.\n For leaves, children_right[i] == TREE_LEAF. Otherwise,\n children_right[i] > i. This child handles the case where\n X[:, feature[i]] > threshold[i].\n\n feature : array of int, shape [node_count]\n feature[i] holds the feature to split on, for the internal node i.\n\n threshold : array of double, shape [node_count]\n threshold[i] holds the threshold for the internal node i.\n\n value : array of double, shape [node_count, n_outputs, max_n_classes]\n Contains the constant prediction value of each node.\n\n impurity : array of double, shape [node_count]\n impurity[i] holds the impurity (i.e., the value of the splitting\n criterion) at node i.\n\n n_node_samples : array of int, shape [node_count]\n n_node_samples[i] holds the number of training samples reaching node i.\n\n weighted_n_node_samples : array of int, shape [node_count]\n weighted_n_node_samples[i] holds the weighted number of training samples\n reaching node i.\n ", /*tp_doc*/ 0, /*tp_traverse*/ 0, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_7sklearn_5_tree_Tree, /*tp_methods*/ 0, /*tp_members*/ __pyx_getsets_7sklearn_5_tree_Tree, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_7sklearn_5_tree_Tree, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static struct __pyx_vtabstruct_7sklearn_5_tree_TreeBuilder __pyx_vtable_7sklearn_5_tree_TreeBuilder; static PyObject *__pyx_tp_new_7sklearn_5_tree_TreeBuilder(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { struct __pyx_obj_7sklearn_5_tree_TreeBuilder *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_obj_7sklearn_5_tree_TreeBuilder *)o); p->__pyx_vtab = __pyx_vtabptr_7sklearn_5_tree_TreeBuilder; p->splitter = ((struct __pyx_obj_7sklearn_5_tree_Splitter *)Py_None); Py_INCREF(Py_None); return o; } static void __pyx_tp_dealloc_7sklearn_5_tree_TreeBuilder(PyObject *o) { struct __pyx_obj_7sklearn_5_tree_TreeBuilder *p = (struct __pyx_obj_7sklearn_5_tree_TreeBuilder *)o; #if PY_VERSION_HEX >= 0x030400a1 if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif PyObject_GC_UnTrack(o); Py_CLEAR(p->splitter); (*Py_TYPE(o)->tp_free)(o); } static int __pyx_tp_traverse_7sklearn_5_tree_TreeBuilder(PyObject *o, visitproc v, void *a) { int e; struct __pyx_obj_7sklearn_5_tree_TreeBuilder *p = (struct __pyx_obj_7sklearn_5_tree_TreeBuilder *)o; if (p->splitter) { e = (*v)(((PyObject*)p->splitter), a); if (e) return e; } return 0; } static int __pyx_tp_clear_7sklearn_5_tree_TreeBuilder(PyObject *o) { PyObject* tmp; struct __pyx_obj_7sklearn_5_tree_TreeBuilder *p = (struct __pyx_obj_7sklearn_5_tree_TreeBuilder *)o; tmp = ((PyObject*)p->splitter); p->splitter = ((struct __pyx_obj_7sklearn_5_tree_Splitter *)Py_None); Py_INCREF(Py_None); Py_XDECREF(tmp); return 0; } static PyMethodDef __pyx_methods_7sklearn_5_tree_TreeBuilder[] = { {"build", (PyCFunction)__pyx_pw_7sklearn_5_tree_11TreeBuilder_1build, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7sklearn_5_tree_11TreeBuilder_build}, {0, 0, 0, 0} }; static PyTypeObject __pyx_type_7sklearn_5_tree_TreeBuilder = { PyVarObject_HEAD_INIT(0, 0) "sklearn._tree.TreeBuilder", /*tp_name*/ sizeof(struct __pyx_obj_7sklearn_5_tree_TreeBuilder), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_7sklearn_5_tree_TreeBuilder, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ "Interface for different tree building strategies. ", /*tp_doc*/ __pyx_tp_traverse_7sklearn_5_tree_TreeBuilder, /*tp_traverse*/ __pyx_tp_clear_7sklearn_5_tree_TreeBuilder, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_7sklearn_5_tree_TreeBuilder, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_7sklearn_5_tree_TreeBuilder, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static struct __pyx_vtabstruct_7sklearn_5_tree_ClassificationCriterion __pyx_vtable_7sklearn_5_tree_ClassificationCriterion; static PyObject *__pyx_tp_new_7sklearn_5_tree_ClassificationCriterion(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_obj_7sklearn_5_tree_ClassificationCriterion *p; PyObject *o = __pyx_tp_new_7sklearn_5_tree_Criterion(t, a, k); if (unlikely(!o)) return 0; p = ((struct __pyx_obj_7sklearn_5_tree_ClassificationCriterion *)o); p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_5_tree_Criterion*)__pyx_vtabptr_7sklearn_5_tree_ClassificationCriterion; if (unlikely(__pyx_pw_7sklearn_5_tree_23ClassificationCriterion_1__cinit__(o, a, k) < 0)) { Py_DECREF(o); o = 0; } return o; } static void __pyx_tp_dealloc_7sklearn_5_tree_ClassificationCriterion(PyObject *o) { #if PY_VERSION_HEX >= 0x030400a1 if (unlikely(Py_TYPE(o)->tp_finalize) && (!PyType_IS_GC(Py_TYPE(o)) || !_PyGC_FINALIZED(o))) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); ++Py_REFCNT(o); __pyx_pw_7sklearn_5_tree_23ClassificationCriterion_3__dealloc__(o); --Py_REFCNT(o); PyErr_Restore(etype, eval, etb); } __pyx_tp_dealloc_7sklearn_5_tree_Criterion(o); } static PyMethodDef __pyx_methods_7sklearn_5_tree_ClassificationCriterion[] = { {"__reduce__", (PyCFunction)__pyx_pw_7sklearn_5_tree_23ClassificationCriterion_5__reduce__, METH_NOARGS, 0}, {"__getstate__", (PyCFunction)__pyx_pw_7sklearn_5_tree_23ClassificationCriterion_7__getstate__, METH_NOARGS, 0}, {"__setstate__", (PyCFunction)__pyx_pw_7sklearn_5_tree_23ClassificationCriterion_9__setstate__, METH_O, 0}, {0, 0, 0, 0} }; static PyTypeObject __pyx_type_7sklearn_5_tree_ClassificationCriterion = { PyVarObject_HEAD_INIT(0, 0) "sklearn._tree.ClassificationCriterion", /*tp_name*/ sizeof(struct __pyx_obj_7sklearn_5_tree_ClassificationCriterion), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_7sklearn_5_tree_ClassificationCriterion, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/ "Abstract criterion for classification.", /*tp_doc*/ 0, /*tp_traverse*/ 0, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_7sklearn_5_tree_ClassificationCriterion, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_7sklearn_5_tree_ClassificationCriterion, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static struct __pyx_vtabstruct_7sklearn_5_tree_Entropy __pyx_vtable_7sklearn_5_tree_Entropy; static PyObject *__pyx_tp_new_7sklearn_5_tree_Entropy(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_obj_7sklearn_5_tree_Entropy *p; PyObject *o = __pyx_tp_new_7sklearn_5_tree_ClassificationCriterion(t, a, k); if (unlikely(!o)) return 0; p = ((struct __pyx_obj_7sklearn_5_tree_Entropy *)o); p->__pyx_base.__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_5_tree_Criterion*)__pyx_vtabptr_7sklearn_5_tree_Entropy; return o; } static PyTypeObject __pyx_type_7sklearn_5_tree_Entropy = { PyVarObject_HEAD_INIT(0, 0) "sklearn._tree.Entropy", /*tp_name*/ sizeof(struct __pyx_obj_7sklearn_5_tree_Entropy), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_7sklearn_5_tree_ClassificationCriterion, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/ "Cross Entropy impurity criteria.\n\n Let the target be a classification outcome taking values in 0, 1, ..., K-1.\n If node m represents a region Rm with Nm observations, then let\n\n pmk = 1/ Nm \\sum_{x_i in Rm} I(yi = k)\n\n be the proportion of class k observations in node m.\n\n The cross-entropy is then defined as\n\n cross-entropy = - \\sum_{k=0}^{K-1} pmk log(pmk)\n ", /*tp_doc*/ 0, /*tp_traverse*/ 0, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ 0, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_7sklearn_5_tree_Entropy, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static struct __pyx_vtabstruct_7sklearn_5_tree_Gini __pyx_vtable_7sklearn_5_tree_Gini; static PyObject *__pyx_tp_new_7sklearn_5_tree_Gini(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_obj_7sklearn_5_tree_Gini *p; PyObject *o = __pyx_tp_new_7sklearn_5_tree_ClassificationCriterion(t, a, k); if (unlikely(!o)) return 0; p = ((struct __pyx_obj_7sklearn_5_tree_Gini *)o); p->__pyx_base.__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_5_tree_Criterion*)__pyx_vtabptr_7sklearn_5_tree_Gini; return o; } static PyTypeObject __pyx_type_7sklearn_5_tree_Gini = { PyVarObject_HEAD_INIT(0, 0) "sklearn._tree.Gini", /*tp_name*/ sizeof(struct __pyx_obj_7sklearn_5_tree_Gini), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_7sklearn_5_tree_ClassificationCriterion, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/ "Gini Index impurity criteria.\n\n Let the target be a classification outcome taking values in 0, 1, ..., K-1.\n If node m represents a region Rm with Nm observations, then let\n\n pmk = 1/ Nm \\sum_{x_i in Rm} I(yi = k)\n\n be the proportion of class k observations in node m.\n\n The Gini Index is then defined as:\n\n index = \\sum_{k=0}^{K-1} pmk (1 - pmk)\n = 1 - \\sum_{k=0}^{K-1} pmk ** 2\n ", /*tp_doc*/ 0, /*tp_traverse*/ 0, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ 0, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_7sklearn_5_tree_Gini, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static struct __pyx_vtabstruct_7sklearn_5_tree_RegressionCriterion __pyx_vtable_7sklearn_5_tree_RegressionCriterion; static PyObject *__pyx_tp_new_7sklearn_5_tree_RegressionCriterion(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_obj_7sklearn_5_tree_RegressionCriterion *p; PyObject *o = __pyx_tp_new_7sklearn_5_tree_Criterion(t, a, k); if (unlikely(!o)) return 0; p = ((struct __pyx_obj_7sklearn_5_tree_RegressionCriterion *)o); p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_5_tree_Criterion*)__pyx_vtabptr_7sklearn_5_tree_RegressionCriterion; if (unlikely(__pyx_pw_7sklearn_5_tree_19RegressionCriterion_1__cinit__(o, a, k) < 0)) { Py_DECREF(o); o = 0; } return o; } static void __pyx_tp_dealloc_7sklearn_5_tree_RegressionCriterion(PyObject *o) { #if PY_VERSION_HEX >= 0x030400a1 if (unlikely(Py_TYPE(o)->tp_finalize) && (!PyType_IS_GC(Py_TYPE(o)) || !_PyGC_FINALIZED(o))) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); ++Py_REFCNT(o); __pyx_pw_7sklearn_5_tree_19RegressionCriterion_3__dealloc__(o); --Py_REFCNT(o); PyErr_Restore(etype, eval, etb); } __pyx_tp_dealloc_7sklearn_5_tree_Criterion(o); } static PyMethodDef __pyx_methods_7sklearn_5_tree_RegressionCriterion[] = { {"__reduce__", (PyCFunction)__pyx_pw_7sklearn_5_tree_19RegressionCriterion_5__reduce__, METH_NOARGS, 0}, {"__getstate__", (PyCFunction)__pyx_pw_7sklearn_5_tree_19RegressionCriterion_7__getstate__, METH_NOARGS, 0}, {"__setstate__", (PyCFunction)__pyx_pw_7sklearn_5_tree_19RegressionCriterion_9__setstate__, METH_O, 0}, {0, 0, 0, 0} }; static PyTypeObject __pyx_type_7sklearn_5_tree_RegressionCriterion = { PyVarObject_HEAD_INIT(0, 0) "sklearn._tree.RegressionCriterion", /*tp_name*/ sizeof(struct __pyx_obj_7sklearn_5_tree_RegressionCriterion), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_7sklearn_5_tree_RegressionCriterion, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/ "Abstract criterion for regression.\n\n Computes variance of the target values left and right of the split point.\n Computation is linear in `n_samples` by using ::\n\n var = \\sum_i^n (y_i - y_bar) ** 2\n = (\\sum_i^n y_i ** 2) - n_samples y_bar ** 2\n ", /*tp_doc*/ 0, /*tp_traverse*/ 0, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_7sklearn_5_tree_RegressionCriterion, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_7sklearn_5_tree_RegressionCriterion, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static struct __pyx_vtabstruct_7sklearn_5_tree_MSE __pyx_vtable_7sklearn_5_tree_MSE; static PyObject *__pyx_tp_new_7sklearn_5_tree_MSE(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_obj_7sklearn_5_tree_MSE *p; PyObject *o = __pyx_tp_new_7sklearn_5_tree_RegressionCriterion(t, a, k); if (unlikely(!o)) return 0; p = ((struct __pyx_obj_7sklearn_5_tree_MSE *)o); p->__pyx_base.__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_5_tree_Criterion*)__pyx_vtabptr_7sklearn_5_tree_MSE; return o; } static PyTypeObject __pyx_type_7sklearn_5_tree_MSE = { PyVarObject_HEAD_INIT(0, 0) "sklearn._tree.MSE", /*tp_name*/ sizeof(struct __pyx_obj_7sklearn_5_tree_MSE), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_7sklearn_5_tree_RegressionCriterion, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/ "Mean squared error impurity criterion.\n\n MSE = var_left + var_right\n ", /*tp_doc*/ 0, /*tp_traverse*/ 0, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ 0, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_7sklearn_5_tree_MSE, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static struct __pyx_vtabstruct_7sklearn_5_tree_FriedmanMSE __pyx_vtable_7sklearn_5_tree_FriedmanMSE; static PyObject *__pyx_tp_new_7sklearn_5_tree_FriedmanMSE(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_obj_7sklearn_5_tree_FriedmanMSE *p; PyObject *o = __pyx_tp_new_7sklearn_5_tree_MSE(t, a, k); if (unlikely(!o)) return 0; p = ((struct __pyx_obj_7sklearn_5_tree_FriedmanMSE *)o); p->__pyx_base.__pyx_base.__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_5_tree_Criterion*)__pyx_vtabptr_7sklearn_5_tree_FriedmanMSE; return o; } static PyTypeObject __pyx_type_7sklearn_5_tree_FriedmanMSE = { PyVarObject_HEAD_INIT(0, 0) "sklearn._tree.FriedmanMSE", /*tp_name*/ sizeof(struct __pyx_obj_7sklearn_5_tree_FriedmanMSE), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_7sklearn_5_tree_RegressionCriterion, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/ "Mean squared error impurity criterion with improvement score by Friedman\n\n Uses the formula (35) in Friedmans original Gradient Boosting paper:\n\n diff = mean_left - mean_right\n improvement = n_left * n_right * diff^2 / (n_left + n_right)\n ", /*tp_doc*/ 0, /*tp_traverse*/ 0, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ 0, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_7sklearn_5_tree_FriedmanMSE, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static struct __pyx_vtabstruct_7sklearn_5_tree_BaseDenseSplitter __pyx_vtable_7sklearn_5_tree_BaseDenseSplitter; static PyObject *__pyx_tp_new_7sklearn_5_tree_BaseDenseSplitter(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_obj_7sklearn_5_tree_BaseDenseSplitter *p; PyObject *o = __pyx_tp_new_7sklearn_5_tree_Splitter(t, a, k); if (unlikely(!o)) return 0; p = ((struct __pyx_obj_7sklearn_5_tree_BaseDenseSplitter *)o); p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_5_tree_Splitter*)__pyx_vtabptr_7sklearn_5_tree_BaseDenseSplitter; if (unlikely(__pyx_pw_7sklearn_5_tree_17BaseDenseSplitter_1__cinit__(o, a, k) < 0)) { Py_DECREF(o); o = 0; } return o; } static PyMethodDef __pyx_methods_7sklearn_5_tree_BaseDenseSplitter[] = { {0, 0, 0, 0} }; static PyTypeObject __pyx_type_7sklearn_5_tree_BaseDenseSplitter = { PyVarObject_HEAD_INIT(0, 0) "sklearn._tree.BaseDenseSplitter", /*tp_name*/ sizeof(struct __pyx_obj_7sklearn_5_tree_BaseDenseSplitter), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_7sklearn_5_tree_Splitter, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ 0, /*tp_doc*/ __pyx_tp_traverse_7sklearn_5_tree_Splitter, /*tp_traverse*/ __pyx_tp_clear_7sklearn_5_tree_Splitter, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_7sklearn_5_tree_BaseDenseSplitter, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_7sklearn_5_tree_BaseDenseSplitter, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static struct __pyx_vtabstruct_7sklearn_5_tree_BestSplitter __pyx_vtable_7sklearn_5_tree_BestSplitter; static PyObject *__pyx_tp_new_7sklearn_5_tree_BestSplitter(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_obj_7sklearn_5_tree_BestSplitter *p; PyObject *o = __pyx_tp_new_7sklearn_5_tree_BaseDenseSplitter(t, a, k); if (unlikely(!o)) return 0; p = ((struct __pyx_obj_7sklearn_5_tree_BestSplitter *)o); p->__pyx_base.__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_5_tree_Splitter*)__pyx_vtabptr_7sklearn_5_tree_BestSplitter; return o; } static PyMethodDef __pyx_methods_7sklearn_5_tree_BestSplitter[] = { {"__reduce__", (PyCFunction)__pyx_pw_7sklearn_5_tree_12BestSplitter_1__reduce__, METH_NOARGS, 0}, {0, 0, 0, 0} }; static PyTypeObject __pyx_type_7sklearn_5_tree_BestSplitter = { PyVarObject_HEAD_INIT(0, 0) "sklearn._tree.BestSplitter", /*tp_name*/ sizeof(struct __pyx_obj_7sklearn_5_tree_BestSplitter), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_7sklearn_5_tree_Splitter, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ "Splitter for finding the best split.", /*tp_doc*/ __pyx_tp_traverse_7sklearn_5_tree_Splitter, /*tp_traverse*/ __pyx_tp_clear_7sklearn_5_tree_Splitter, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_7sklearn_5_tree_BestSplitter, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_7sklearn_5_tree_BestSplitter, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static struct __pyx_vtabstruct_7sklearn_5_tree_RandomSplitter __pyx_vtable_7sklearn_5_tree_RandomSplitter; static PyObject *__pyx_tp_new_7sklearn_5_tree_RandomSplitter(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_obj_7sklearn_5_tree_RandomSplitter *p; PyObject *o = __pyx_tp_new_7sklearn_5_tree_BaseDenseSplitter(t, a, k); if (unlikely(!o)) return 0; p = ((struct __pyx_obj_7sklearn_5_tree_RandomSplitter *)o); p->__pyx_base.__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_5_tree_Splitter*)__pyx_vtabptr_7sklearn_5_tree_RandomSplitter; return o; } static PyMethodDef __pyx_methods_7sklearn_5_tree_RandomSplitter[] = { {"__reduce__", (PyCFunction)__pyx_pw_7sklearn_5_tree_14RandomSplitter_1__reduce__, METH_NOARGS, 0}, {0, 0, 0, 0} }; static PyTypeObject __pyx_type_7sklearn_5_tree_RandomSplitter = { PyVarObject_HEAD_INIT(0, 0) "sklearn._tree.RandomSplitter", /*tp_name*/ sizeof(struct __pyx_obj_7sklearn_5_tree_RandomSplitter), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_7sklearn_5_tree_Splitter, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ "Splitter for finding the best random split.", /*tp_doc*/ __pyx_tp_traverse_7sklearn_5_tree_Splitter, /*tp_traverse*/ __pyx_tp_clear_7sklearn_5_tree_Splitter, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_7sklearn_5_tree_RandomSplitter, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_7sklearn_5_tree_RandomSplitter, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static struct __pyx_vtabstruct_7sklearn_5_tree_PresortBestSplitter __pyx_vtable_7sklearn_5_tree_PresortBestSplitter; static PyObject *__pyx_tp_new_7sklearn_5_tree_PresortBestSplitter(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_obj_7sklearn_5_tree_PresortBestSplitter *p; PyObject *o = __pyx_tp_new_7sklearn_5_tree_BaseDenseSplitter(t, a, k); if (unlikely(!o)) return 0; p = ((struct __pyx_obj_7sklearn_5_tree_PresortBestSplitter *)o); p->__pyx_base.__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_5_tree_Splitter*)__pyx_vtabptr_7sklearn_5_tree_PresortBestSplitter; p->X_argsorted = ((PyArrayObject *)Py_None); Py_INCREF(Py_None); if (unlikely(__pyx_pw_7sklearn_5_tree_19PresortBestSplitter_1__cinit__(o, a, k) < 0)) { Py_DECREF(o); o = 0; } return o; } static void __pyx_tp_dealloc_7sklearn_5_tree_PresortBestSplitter(PyObject *o) { struct __pyx_obj_7sklearn_5_tree_PresortBestSplitter *p = (struct __pyx_obj_7sklearn_5_tree_PresortBestSplitter *)o; #if PY_VERSION_HEX >= 0x030400a1 if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif PyObject_GC_UnTrack(o); { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); ++Py_REFCNT(o); __pyx_pw_7sklearn_5_tree_19PresortBestSplitter_3__dealloc__(o); --Py_REFCNT(o); PyErr_Restore(etype, eval, etb); } Py_CLEAR(p->X_argsorted); PyObject_GC_Track(o); __pyx_tp_dealloc_7sklearn_5_tree_Splitter(o); } static int __pyx_tp_traverse_7sklearn_5_tree_PresortBestSplitter(PyObject *o, visitproc v, void *a) { int e; struct __pyx_obj_7sklearn_5_tree_PresortBestSplitter *p = (struct __pyx_obj_7sklearn_5_tree_PresortBestSplitter *)o; e = __pyx_tp_traverse_7sklearn_5_tree_Splitter(o, v, a); if (e) return e; if (p->X_argsorted) { e = (*v)(((PyObject*)p->X_argsorted), a); if (e) return e; } return 0; } static int __pyx_tp_clear_7sklearn_5_tree_PresortBestSplitter(PyObject *o) { PyObject* tmp; struct __pyx_obj_7sklearn_5_tree_PresortBestSplitter *p = (struct __pyx_obj_7sklearn_5_tree_PresortBestSplitter *)o; __pyx_tp_clear_7sklearn_5_tree_Splitter(o); tmp = ((PyObject*)p->X_argsorted); p->X_argsorted = ((PyArrayObject *)Py_None); Py_INCREF(Py_None); Py_XDECREF(tmp); return 0; } static PyMethodDef __pyx_methods_7sklearn_5_tree_PresortBestSplitter[] = { {"__reduce__", (PyCFunction)__pyx_pw_7sklearn_5_tree_19PresortBestSplitter_5__reduce__, METH_NOARGS, 0}, {0, 0, 0, 0} }; static PyTypeObject __pyx_type_7sklearn_5_tree_PresortBestSplitter = { PyVarObject_HEAD_INIT(0, 0) "sklearn._tree.PresortBestSplitter", /*tp_name*/ sizeof(struct __pyx_obj_7sklearn_5_tree_PresortBestSplitter), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_7sklearn_5_tree_PresortBestSplitter, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ "Splitter for finding the best split, using presorting.", /*tp_doc*/ __pyx_tp_traverse_7sklearn_5_tree_PresortBestSplitter, /*tp_traverse*/ __pyx_tp_clear_7sklearn_5_tree_PresortBestSplitter, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_7sklearn_5_tree_PresortBestSplitter, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_7sklearn_5_tree_PresortBestSplitter, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static struct __pyx_vtabstruct_7sklearn_5_tree_BaseSparseSplitter __pyx_vtable_7sklearn_5_tree_BaseSparseSplitter; static PyObject *__pyx_tp_new_7sklearn_5_tree_BaseSparseSplitter(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter *p; PyObject *o = __pyx_tp_new_7sklearn_5_tree_Splitter(t, a, k); if (unlikely(!o)) return 0; p = ((struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter *)o); p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_5_tree_Splitter*)__pyx_vtabptr_7sklearn_5_tree_BaseSparseSplitter; if (unlikely(__pyx_pw_7sklearn_5_tree_18BaseSparseSplitter_1__cinit__(o, a, k) < 0)) { Py_DECREF(o); o = 0; } return o; } static void __pyx_tp_dealloc_7sklearn_5_tree_BaseSparseSplitter(PyObject *o) { #if PY_VERSION_HEX >= 0x030400a1 if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif PyObject_GC_UnTrack(o); { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); ++Py_REFCNT(o); __pyx_pw_7sklearn_5_tree_18BaseSparseSplitter_3__dealloc__(o); --Py_REFCNT(o); PyErr_Restore(etype, eval, etb); } PyObject_GC_Track(o); __pyx_tp_dealloc_7sklearn_5_tree_Splitter(o); } static PyMethodDef __pyx_methods_7sklearn_5_tree_BaseSparseSplitter[] = { {0, 0, 0, 0} }; static PyTypeObject __pyx_type_7sklearn_5_tree_BaseSparseSplitter = { PyVarObject_HEAD_INIT(0, 0) "sklearn._tree.BaseSparseSplitter", /*tp_name*/ sizeof(struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_7sklearn_5_tree_BaseSparseSplitter, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ 0, /*tp_doc*/ __pyx_tp_traverse_7sklearn_5_tree_Splitter, /*tp_traverse*/ __pyx_tp_clear_7sklearn_5_tree_Splitter, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_7sklearn_5_tree_BaseSparseSplitter, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_7sklearn_5_tree_BaseSparseSplitter, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static struct __pyx_vtabstruct_7sklearn_5_tree_BestSparseSplitter __pyx_vtable_7sklearn_5_tree_BestSparseSplitter; static PyObject *__pyx_tp_new_7sklearn_5_tree_BestSparseSplitter(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_obj_7sklearn_5_tree_BestSparseSplitter *p; PyObject *o = __pyx_tp_new_7sklearn_5_tree_BaseSparseSplitter(t, a, k); if (unlikely(!o)) return 0; p = ((struct __pyx_obj_7sklearn_5_tree_BestSparseSplitter *)o); p->__pyx_base.__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_5_tree_Splitter*)__pyx_vtabptr_7sklearn_5_tree_BestSparseSplitter; return o; } static PyMethodDef __pyx_methods_7sklearn_5_tree_BestSparseSplitter[] = { {"__reduce__", (PyCFunction)__pyx_pw_7sklearn_5_tree_18BestSparseSplitter_1__reduce__, METH_NOARGS, 0}, {0, 0, 0, 0} }; static PyTypeObject __pyx_type_7sklearn_5_tree_BestSparseSplitter = { PyVarObject_HEAD_INIT(0, 0) "sklearn._tree.BestSparseSplitter", /*tp_name*/ sizeof(struct __pyx_obj_7sklearn_5_tree_BestSparseSplitter), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_7sklearn_5_tree_BaseSparseSplitter, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ "Splitter for finding the best split, using the sparse data.", /*tp_doc*/ __pyx_tp_traverse_7sklearn_5_tree_Splitter, /*tp_traverse*/ __pyx_tp_clear_7sklearn_5_tree_Splitter, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_7sklearn_5_tree_BestSparseSplitter, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_7sklearn_5_tree_BestSparseSplitter, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static struct __pyx_vtabstruct_7sklearn_5_tree_RandomSparseSplitter __pyx_vtable_7sklearn_5_tree_RandomSparseSplitter; static PyObject *__pyx_tp_new_7sklearn_5_tree_RandomSparseSplitter(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_obj_7sklearn_5_tree_RandomSparseSplitter *p; PyObject *o = __pyx_tp_new_7sklearn_5_tree_BaseSparseSplitter(t, a, k); if (unlikely(!o)) return 0; p = ((struct __pyx_obj_7sklearn_5_tree_RandomSparseSplitter *)o); p->__pyx_base.__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_5_tree_Splitter*)__pyx_vtabptr_7sklearn_5_tree_RandomSparseSplitter; return o; } static PyMethodDef __pyx_methods_7sklearn_5_tree_RandomSparseSplitter[] = { {"__reduce__", (PyCFunction)__pyx_pw_7sklearn_5_tree_20RandomSparseSplitter_1__reduce__, METH_NOARGS, 0}, {0, 0, 0, 0} }; static PyTypeObject __pyx_type_7sklearn_5_tree_RandomSparseSplitter = { PyVarObject_HEAD_INIT(0, 0) "sklearn._tree.RandomSparseSplitter", /*tp_name*/ sizeof(struct __pyx_obj_7sklearn_5_tree_RandomSparseSplitter), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_7sklearn_5_tree_BaseSparseSplitter, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ "Splitter for finding a random split, using the sparse data.", /*tp_doc*/ __pyx_tp_traverse_7sklearn_5_tree_Splitter, /*tp_traverse*/ __pyx_tp_clear_7sklearn_5_tree_Splitter, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_7sklearn_5_tree_RandomSparseSplitter, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_7sklearn_5_tree_RandomSparseSplitter, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static struct __pyx_vtabstruct_7sklearn_5_tree_DepthFirstTreeBuilder __pyx_vtable_7sklearn_5_tree_DepthFirstTreeBuilder; static PyObject *__pyx_tp_new_7sklearn_5_tree_DepthFirstTreeBuilder(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_obj_7sklearn_5_tree_DepthFirstTreeBuilder *p; PyObject *o = __pyx_tp_new_7sklearn_5_tree_TreeBuilder(t, a, k); if (unlikely(!o)) return 0; p = ((struct __pyx_obj_7sklearn_5_tree_DepthFirstTreeBuilder *)o); p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_5_tree_TreeBuilder*)__pyx_vtabptr_7sklearn_5_tree_DepthFirstTreeBuilder; if (unlikely(__pyx_pw_7sklearn_5_tree_21DepthFirstTreeBuilder_1__cinit__(o, a, k) < 0)) { Py_DECREF(o); o = 0; } return o; } static PyMethodDef __pyx_methods_7sklearn_5_tree_DepthFirstTreeBuilder[] = { {"build", (PyCFunction)__pyx_pw_7sklearn_5_tree_21DepthFirstTreeBuilder_3build, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7sklearn_5_tree_21DepthFirstTreeBuilder_2build}, {0, 0, 0, 0} }; static PyTypeObject __pyx_type_7sklearn_5_tree_DepthFirstTreeBuilder = { PyVarObject_HEAD_INIT(0, 0) "sklearn._tree.DepthFirstTreeBuilder", /*tp_name*/ sizeof(struct __pyx_obj_7sklearn_5_tree_DepthFirstTreeBuilder), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_7sklearn_5_tree_TreeBuilder, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ "Build a decision tree in depth-first fashion.", /*tp_doc*/ __pyx_tp_traverse_7sklearn_5_tree_TreeBuilder, /*tp_traverse*/ __pyx_tp_clear_7sklearn_5_tree_TreeBuilder, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_7sklearn_5_tree_DepthFirstTreeBuilder, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_7sklearn_5_tree_DepthFirstTreeBuilder, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static struct __pyx_vtabstruct_7sklearn_5_tree_BestFirstTreeBuilder __pyx_vtable_7sklearn_5_tree_BestFirstTreeBuilder; static PyObject *__pyx_tp_new_7sklearn_5_tree_BestFirstTreeBuilder(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_obj_7sklearn_5_tree_BestFirstTreeBuilder *p; PyObject *o = __pyx_tp_new_7sklearn_5_tree_TreeBuilder(t, a, k); if (unlikely(!o)) return 0; p = ((struct __pyx_obj_7sklearn_5_tree_BestFirstTreeBuilder *)o); p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_5_tree_TreeBuilder*)__pyx_vtabptr_7sklearn_5_tree_BestFirstTreeBuilder; if (unlikely(__pyx_pw_7sklearn_5_tree_20BestFirstTreeBuilder_1__cinit__(o, a, k) < 0)) { Py_DECREF(o); o = 0; } return o; } static PyMethodDef __pyx_methods_7sklearn_5_tree_BestFirstTreeBuilder[] = { {"build", (PyCFunction)__pyx_pw_7sklearn_5_tree_20BestFirstTreeBuilder_3build, METH_VARARGS|METH_KEYWORDS, __pyx_doc_7sklearn_5_tree_20BestFirstTreeBuilder_2build}, {0, 0, 0, 0} }; static PyTypeObject __pyx_type_7sklearn_5_tree_BestFirstTreeBuilder = { PyVarObject_HEAD_INIT(0, 0) "sklearn._tree.BestFirstTreeBuilder", /*tp_name*/ sizeof(struct __pyx_obj_7sklearn_5_tree_BestFirstTreeBuilder), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_7sklearn_5_tree_TreeBuilder, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 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__pyx_clineno = __LINE__; goto __pyx_L1_error;} /*--- Global init code ---*/ /*--- Variable export code ---*/ /*--- Function export code ---*/ /*--- Type init code ---*/ __pyx_vtabptr_7sklearn_5_tree_Stack = &__pyx_vtable_7sklearn_5_tree_Stack; __pyx_vtable_7sklearn_5_tree_Stack.is_empty = (int (*)(struct __pyx_obj_7sklearn_5_tree_Stack *))__pyx_f_7sklearn_5_tree_5Stack_is_empty; __pyx_vtable_7sklearn_5_tree_Stack.push = (int (*)(struct __pyx_obj_7sklearn_5_tree_Stack *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, int, double, __pyx_t_7sklearn_5_tree_SIZE_t))__pyx_f_7sklearn_5_tree_5Stack_push; __pyx_vtable_7sklearn_5_tree_Stack.pop = (int (*)(struct __pyx_obj_7sklearn_5_tree_Stack *, struct __pyx_t_7sklearn_5_tree_StackRecord *))__pyx_f_7sklearn_5_tree_5Stack_pop; if (PyType_Ready(&__pyx_type_7sklearn_5_tree_Stack) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 90; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_type_7sklearn_5_tree_Stack.tp_print = 0; if (__Pyx_SetVtable(__pyx_type_7sklearn_5_tree_Stack.tp_dict, __pyx_vtabptr_7sklearn_5_tree_Stack) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 90; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (PyObject_SetAttrString(__pyx_m, "Stack", (PyObject *)&__pyx_type_7sklearn_5_tree_Stack) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 90; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_7sklearn_5_tree_Stack = &__pyx_type_7sklearn_5_tree_Stack; __pyx_vtabptr_7sklearn_5_tree_PriorityHeap = &__pyx_vtable_7sklearn_5_tree_PriorityHeap; __pyx_vtable_7sklearn_5_tree_PriorityHeap.is_empty = (int (*)(struct __pyx_obj_7sklearn_5_tree_PriorityHeap *))__pyx_f_7sklearn_5_tree_12PriorityHeap_is_empty; __pyx_vtable_7sklearn_5_tree_PriorityHeap.push = (int (*)(struct __pyx_obj_7sklearn_5_tree_PriorityHeap *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, int, double, double, double, double))__pyx_f_7sklearn_5_tree_12PriorityHeap_push; __pyx_vtable_7sklearn_5_tree_PriorityHeap.pop = (int (*)(struct __pyx_obj_7sklearn_5_tree_PriorityHeap *, struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord *))__pyx_f_7sklearn_5_tree_12PriorityHeap_pop; if (PyType_Ready(&__pyx_type_7sklearn_5_tree_PriorityHeap) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 208; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_type_7sklearn_5_tree_PriorityHeap.tp_print = 0; if (__Pyx_SetVtable(__pyx_type_7sklearn_5_tree_PriorityHeap.tp_dict, __pyx_vtabptr_7sklearn_5_tree_PriorityHeap) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 208; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (PyObject_SetAttrString(__pyx_m, "PriorityHeap", (PyObject *)&__pyx_type_7sklearn_5_tree_PriorityHeap) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 208; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_7sklearn_5_tree_PriorityHeap = &__pyx_type_7sklearn_5_tree_PriorityHeap; __pyx_vtabptr_7sklearn_5_tree_Criterion = &__pyx_vtable_7sklearn_5_tree_Criterion; __pyx_vtable_7sklearn_5_tree_Criterion.init = (void (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *, __pyx_t_7sklearn_5_tree_DOUBLE_t *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_DOUBLE_t *, double, __pyx_t_7sklearn_5_tree_SIZE_t *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t))__pyx_f_7sklearn_5_tree_9Criterion_init; __pyx_vtable_7sklearn_5_tree_Criterion.reset = (void (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *))__pyx_f_7sklearn_5_tree_9Criterion_reset; __pyx_vtable_7sklearn_5_tree_Criterion.update = (void (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *, __pyx_t_7sklearn_5_tree_SIZE_t))__pyx_f_7sklearn_5_tree_9Criterion_update; __pyx_vtable_7sklearn_5_tree_Criterion.node_impurity = (double (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *))__pyx_f_7sklearn_5_tree_9Criterion_node_impurity; __pyx_vtable_7sklearn_5_tree_Criterion.children_impurity = (void (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *, double *, double *))__pyx_f_7sklearn_5_tree_9Criterion_children_impurity; __pyx_vtable_7sklearn_5_tree_Criterion.node_value = (void (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *, double *))__pyx_f_7sklearn_5_tree_9Criterion_node_value; __pyx_vtable_7sklearn_5_tree_Criterion.impurity_improvement = (double (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *, double))__pyx_f_7sklearn_5_tree_9Criterion_impurity_improvement; if (PyType_Ready(&__pyx_type_7sklearn_5_tree_Criterion) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 310; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_type_7sklearn_5_tree_Criterion.tp_print = 0; if (__Pyx_SetVtable(__pyx_type_7sklearn_5_tree_Criterion.tp_dict, __pyx_vtabptr_7sklearn_5_tree_Criterion) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 310; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (PyObject_SetAttrString(__pyx_m, "Criterion", (PyObject *)&__pyx_type_7sklearn_5_tree_Criterion) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 310; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_7sklearn_5_tree_Criterion = &__pyx_type_7sklearn_5_tree_Criterion; __pyx_vtabptr_7sklearn_5_tree_Splitter = &__pyx_vtable_7sklearn_5_tree_Splitter; __pyx_vtable_7sklearn_5_tree_Splitter.init = (void (*)(struct __pyx_obj_7sklearn_5_tree_Splitter *, PyObject *, PyArrayObject *, __pyx_t_7sklearn_5_tree_DOUBLE_t *))__pyx_f_7sklearn_5_tree_8Splitter_init; __pyx_vtable_7sklearn_5_tree_Splitter.node_reset = (void (*)(struct __pyx_obj_7sklearn_5_tree_Splitter *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, double *))__pyx_f_7sklearn_5_tree_8Splitter_node_reset; __pyx_vtable_7sklearn_5_tree_Splitter.node_split = (void (*)(struct __pyx_obj_7sklearn_5_tree_Splitter *, double, struct __pyx_t_7sklearn_5_tree_SplitRecord *, __pyx_t_7sklearn_5_tree_SIZE_t *))__pyx_f_7sklearn_5_tree_8Splitter_node_split; __pyx_vtable_7sklearn_5_tree_Splitter.node_value = (void (*)(struct __pyx_obj_7sklearn_5_tree_Splitter *, double *))__pyx_f_7sklearn_5_tree_8Splitter_node_value; __pyx_vtable_7sklearn_5_tree_Splitter.node_impurity = (double (*)(struct __pyx_obj_7sklearn_5_tree_Splitter *))__pyx_f_7sklearn_5_tree_8Splitter_node_impurity; if (PyType_Ready(&__pyx_type_7sklearn_5_tree_Splitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1135; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_type_7sklearn_5_tree_Splitter.tp_print = 0; if (__Pyx_SetVtable(__pyx_type_7sklearn_5_tree_Splitter.tp_dict, __pyx_vtabptr_7sklearn_5_tree_Splitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1135; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (PyObject_SetAttrString(__pyx_m, "Splitter", (PyObject *)&__pyx_type_7sklearn_5_tree_Splitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1135; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_7sklearn_5_tree_Splitter = &__pyx_type_7sklearn_5_tree_Splitter; __pyx_vtabptr_7sklearn_5_tree_Tree = &__pyx_vtable_7sklearn_5_tree_Tree; __pyx_vtable_7sklearn_5_tree_Tree._add_node = (__pyx_t_7sklearn_5_tree_SIZE_t (*)(struct __pyx_obj_7sklearn_5_tree_Tree *, __pyx_t_7sklearn_5_tree_SIZE_t, int, int, __pyx_t_7sklearn_5_tree_SIZE_t, double, double, __pyx_t_7sklearn_5_tree_SIZE_t, double))__pyx_f_7sklearn_5_tree_4Tree__add_node; __pyx_vtable_7sklearn_5_tree_Tree._resize = (void (*)(struct __pyx_obj_7sklearn_5_tree_Tree *, __pyx_t_7sklearn_5_tree_SIZE_t))__pyx_f_7sklearn_5_tree_4Tree__resize; __pyx_vtable_7sklearn_5_tree_Tree._resize_c = (int (*)(struct __pyx_obj_7sklearn_5_tree_Tree *, struct __pyx_opt_args_7sklearn_5_tree_4Tree__resize_c *__pyx_optional_args))__pyx_f_7sklearn_5_tree_4Tree__resize_c; __pyx_vtable_7sklearn_5_tree_Tree._get_value_ndarray = (PyArrayObject *(*)(struct __pyx_obj_7sklearn_5_tree_Tree *))__pyx_f_7sklearn_5_tree_4Tree__get_value_ndarray; __pyx_vtable_7sklearn_5_tree_Tree._get_node_ndarray = (PyArrayObject *(*)(struct __pyx_obj_7sklearn_5_tree_Tree *))__pyx_f_7sklearn_5_tree_4Tree__get_node_ndarray; __pyx_vtable_7sklearn_5_tree_Tree.predict = (PyArrayObject *(*)(struct __pyx_obj_7sklearn_5_tree_Tree *, PyObject *, int __pyx_skip_dispatch))__pyx_f_7sklearn_5_tree_4Tree_predict; __pyx_vtable_7sklearn_5_tree_Tree.apply = (PyArrayObject *(*)(struct __pyx_obj_7sklearn_5_tree_Tree *, PyObject *, int __pyx_skip_dispatch))__pyx_f_7sklearn_5_tree_4Tree_apply; __pyx_vtable_7sklearn_5_tree_Tree._apply_dense = (PyArrayObject *(*)(struct __pyx_obj_7sklearn_5_tree_Tree *, PyObject *))__pyx_f_7sklearn_5_tree_4Tree__apply_dense; __pyx_vtable_7sklearn_5_tree_Tree._apply_sparse_csr = (PyArrayObject *(*)(struct __pyx_obj_7sklearn_5_tree_Tree *, PyObject *))__pyx_f_7sklearn_5_tree_4Tree__apply_sparse_csr; __pyx_vtable_7sklearn_5_tree_Tree.compute_feature_importances = (PyObject *(*)(struct __pyx_obj_7sklearn_5_tree_Tree *, int __pyx_skip_dispatch, struct __pyx_opt_args_7sklearn_5_tree_4Tree_compute_feature_importances *__pyx_optional_args))__pyx_f_7sklearn_5_tree_4Tree_compute_feature_importances; if (PyType_Ready(&__pyx_type_7sklearn_5_tree_Tree) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3154; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_type_7sklearn_5_tree_Tree.tp_print = 0; if (__Pyx_SetVtable(__pyx_type_7sklearn_5_tree_Tree.tp_dict, __pyx_vtabptr_7sklearn_5_tree_Tree) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3154; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (PyObject_SetAttrString(__pyx_m, "Tree", (PyObject *)&__pyx_type_7sklearn_5_tree_Tree) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3154; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_7sklearn_5_tree_Tree = &__pyx_type_7sklearn_5_tree_Tree; __pyx_vtabptr_7sklearn_5_tree_TreeBuilder = &__pyx_vtable_7sklearn_5_tree_TreeBuilder; __pyx_vtable_7sklearn_5_tree_TreeBuilder.build = (PyObject *(*)(struct __pyx_obj_7sklearn_5_tree_TreeBuilder *, struct __pyx_obj_7sklearn_5_tree_Tree *, PyObject *, PyArrayObject *, int __pyx_skip_dispatch, struct __pyx_opt_args_7sklearn_5_tree_11TreeBuilder_build *__pyx_optional_args))__pyx_f_7sklearn_5_tree_11TreeBuilder_build; __pyx_vtable_7sklearn_5_tree_TreeBuilder._check_input = (PyObject *(*)(struct __pyx_obj_7sklearn_5_tree_TreeBuilder *, PyObject *, PyArrayObject *, PyArrayObject *))__pyx_f_7sklearn_5_tree_11TreeBuilder__check_input; if (PyType_Ready(&__pyx_type_7sklearn_5_tree_TreeBuilder) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2776; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_type_7sklearn_5_tree_TreeBuilder.tp_print = 0; if (__Pyx_SetVtable(__pyx_type_7sklearn_5_tree_TreeBuilder.tp_dict, __pyx_vtabptr_7sklearn_5_tree_TreeBuilder) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2776; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (PyObject_SetAttrString(__pyx_m, "TreeBuilder", (PyObject *)&__pyx_type_7sklearn_5_tree_TreeBuilder) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2776; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_7sklearn_5_tree_TreeBuilder = &__pyx_type_7sklearn_5_tree_TreeBuilder; __pyx_vtabptr_7sklearn_5_tree_ClassificationCriterion = &__pyx_vtable_7sklearn_5_tree_ClassificationCriterion; __pyx_vtable_7sklearn_5_tree_ClassificationCriterion.__pyx_base = *__pyx_vtabptr_7sklearn_5_tree_Criterion; __pyx_vtable_7sklearn_5_tree_ClassificationCriterion.__pyx_base.init = (void (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *, __pyx_t_7sklearn_5_tree_DOUBLE_t *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_DOUBLE_t *, double, __pyx_t_7sklearn_5_tree_SIZE_t *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t))__pyx_f_7sklearn_5_tree_23ClassificationCriterion_init; __pyx_vtable_7sklearn_5_tree_ClassificationCriterion.__pyx_base.reset = (void (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *))__pyx_f_7sklearn_5_tree_23ClassificationCriterion_reset; __pyx_vtable_7sklearn_5_tree_ClassificationCriterion.__pyx_base.update = (void (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *, __pyx_t_7sklearn_5_tree_SIZE_t))__pyx_f_7sklearn_5_tree_23ClassificationCriterion_update; __pyx_vtable_7sklearn_5_tree_ClassificationCriterion.__pyx_base.node_impurity = (double (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *))__pyx_f_7sklearn_5_tree_23ClassificationCriterion_node_impurity; __pyx_vtable_7sklearn_5_tree_ClassificationCriterion.__pyx_base.children_impurity = (void (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *, double *, double *))__pyx_f_7sklearn_5_tree_23ClassificationCriterion_children_impurity; __pyx_vtable_7sklearn_5_tree_ClassificationCriterion.__pyx_base.node_value = (void (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *, double *))__pyx_f_7sklearn_5_tree_23ClassificationCriterion_node_value; __pyx_type_7sklearn_5_tree_ClassificationCriterion.tp_base = __pyx_ptype_7sklearn_5_tree_Criterion; if (PyType_Ready(&__pyx_type_7sklearn_5_tree_ClassificationCriterion) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 363; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_type_7sklearn_5_tree_ClassificationCriterion.tp_print = 0; if (__Pyx_SetVtable(__pyx_type_7sklearn_5_tree_ClassificationCriterion.tp_dict, __pyx_vtabptr_7sklearn_5_tree_ClassificationCriterion) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 363; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (PyObject_SetAttrString(__pyx_m, "ClassificationCriterion", (PyObject *)&__pyx_type_7sklearn_5_tree_ClassificationCriterion) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 363; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_7sklearn_5_tree_ClassificationCriterion = &__pyx_type_7sklearn_5_tree_ClassificationCriterion; __pyx_vtabptr_7sklearn_5_tree_Entropy = &__pyx_vtable_7sklearn_5_tree_Entropy; __pyx_vtable_7sklearn_5_tree_Entropy.__pyx_base = *__pyx_vtabptr_7sklearn_5_tree_ClassificationCriterion; __pyx_vtable_7sklearn_5_tree_Entropy.__pyx_base.__pyx_base.node_impurity = (double (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *))__pyx_f_7sklearn_5_tree_7Entropy_node_impurity; __pyx_vtable_7sklearn_5_tree_Entropy.__pyx_base.__pyx_base.children_impurity = (void (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *, double *, double *))__pyx_f_7sklearn_5_tree_7Entropy_children_impurity; __pyx_type_7sklearn_5_tree_Entropy.tp_base = __pyx_ptype_7sklearn_5_tree_ClassificationCriterion; if (PyType_Ready(&__pyx_type_7sklearn_5_tree_Entropy) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 581; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_type_7sklearn_5_tree_Entropy.tp_print = 0; if (__Pyx_SetVtable(__pyx_type_7sklearn_5_tree_Entropy.tp_dict, __pyx_vtabptr_7sklearn_5_tree_Entropy) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 581; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (PyObject_SetAttrString(__pyx_m, "Entropy", (PyObject *)&__pyx_type_7sklearn_5_tree_Entropy) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 581; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_7sklearn_5_tree_Entropy = &__pyx_type_7sklearn_5_tree_Entropy; __pyx_vtabptr_7sklearn_5_tree_Gini = &__pyx_vtable_7sklearn_5_tree_Gini; __pyx_vtable_7sklearn_5_tree_Gini.__pyx_base = *__pyx_vtabptr_7sklearn_5_tree_ClassificationCriterion; __pyx_vtable_7sklearn_5_tree_Gini.__pyx_base.__pyx_base.node_impurity = (double (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *))__pyx_f_7sklearn_5_tree_4Gini_node_impurity; __pyx_vtable_7sklearn_5_tree_Gini.__pyx_base.__pyx_base.children_impurity = (void (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *, double *, double *))__pyx_f_7sklearn_5_tree_4Gini_children_impurity; __pyx_type_7sklearn_5_tree_Gini.tp_base = __pyx_ptype_7sklearn_5_tree_ClassificationCriterion; if (PyType_Ready(&__pyx_type_7sklearn_5_tree_Gini) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 672; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_type_7sklearn_5_tree_Gini.tp_print = 0; if (__Pyx_SetVtable(__pyx_type_7sklearn_5_tree_Gini.tp_dict, __pyx_vtabptr_7sklearn_5_tree_Gini) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 672; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (PyObject_SetAttrString(__pyx_m, "Gini", (PyObject *)&__pyx_type_7sklearn_5_tree_Gini) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 672; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_7sklearn_5_tree_Gini = &__pyx_type_7sklearn_5_tree_Gini; __pyx_vtabptr_7sklearn_5_tree_RegressionCriterion = &__pyx_vtable_7sklearn_5_tree_RegressionCriterion; __pyx_vtable_7sklearn_5_tree_RegressionCriterion.__pyx_base = *__pyx_vtabptr_7sklearn_5_tree_Criterion; __pyx_vtable_7sklearn_5_tree_RegressionCriterion.__pyx_base.init = (void (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *, __pyx_t_7sklearn_5_tree_DOUBLE_t *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_DOUBLE_t *, double, __pyx_t_7sklearn_5_tree_SIZE_t *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t))__pyx_f_7sklearn_5_tree_19RegressionCriterion_init; __pyx_vtable_7sklearn_5_tree_RegressionCriterion.__pyx_base.reset = (void (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *))__pyx_f_7sklearn_5_tree_19RegressionCriterion_reset; __pyx_vtable_7sklearn_5_tree_RegressionCriterion.__pyx_base.update = (void (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *, __pyx_t_7sklearn_5_tree_SIZE_t))__pyx_f_7sklearn_5_tree_19RegressionCriterion_update; __pyx_vtable_7sklearn_5_tree_RegressionCriterion.__pyx_base.node_impurity = (double (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *))__pyx_f_7sklearn_5_tree_19RegressionCriterion_node_impurity; __pyx_vtable_7sklearn_5_tree_RegressionCriterion.__pyx_base.children_impurity = (void (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *, double *, double *))__pyx_f_7sklearn_5_tree_19RegressionCriterion_children_impurity; __pyx_vtable_7sklearn_5_tree_RegressionCriterion.__pyx_base.node_value = (void (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *, double *))__pyx_f_7sklearn_5_tree_19RegressionCriterion_node_value; __pyx_type_7sklearn_5_tree_RegressionCriterion.tp_base = __pyx_ptype_7sklearn_5_tree_Criterion; if (PyType_Ready(&__pyx_type_7sklearn_5_tree_RegressionCriterion) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 766; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_type_7sklearn_5_tree_RegressionCriterion.tp_print = 0; if (__Pyx_SetVtable(__pyx_type_7sklearn_5_tree_RegressionCriterion.tp_dict, __pyx_vtabptr_7sklearn_5_tree_RegressionCriterion) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 766; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (PyObject_SetAttrString(__pyx_m, "RegressionCriterion", (PyObject *)&__pyx_type_7sklearn_5_tree_RegressionCriterion) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 766; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_7sklearn_5_tree_RegressionCriterion = &__pyx_type_7sklearn_5_tree_RegressionCriterion; __pyx_vtabptr_7sklearn_5_tree_MSE = &__pyx_vtable_7sklearn_5_tree_MSE; __pyx_vtable_7sklearn_5_tree_MSE.__pyx_base = *__pyx_vtabptr_7sklearn_5_tree_RegressionCriterion; __pyx_vtable_7sklearn_5_tree_MSE.__pyx_base.__pyx_base.node_impurity = (double (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *))__pyx_f_7sklearn_5_tree_3MSE_node_impurity; __pyx_vtable_7sklearn_5_tree_MSE.__pyx_base.__pyx_base.children_impurity = (void (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *, double *, double *))__pyx_f_7sklearn_5_tree_3MSE_children_impurity; __pyx_type_7sklearn_5_tree_MSE.tp_base = __pyx_ptype_7sklearn_5_tree_RegressionCriterion; if (PyType_Ready(&__pyx_type_7sklearn_5_tree_MSE) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1049; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_type_7sklearn_5_tree_MSE.tp_print = 0; if (__Pyx_SetVtable(__pyx_type_7sklearn_5_tree_MSE.tp_dict, __pyx_vtabptr_7sklearn_5_tree_MSE) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1049; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (PyObject_SetAttrString(__pyx_m, "MSE", (PyObject *)&__pyx_type_7sklearn_5_tree_MSE) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1049; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_7sklearn_5_tree_MSE = &__pyx_type_7sklearn_5_tree_MSE; __pyx_vtabptr_7sklearn_5_tree_FriedmanMSE = &__pyx_vtable_7sklearn_5_tree_FriedmanMSE; __pyx_vtable_7sklearn_5_tree_FriedmanMSE.__pyx_base = *__pyx_vtabptr_7sklearn_5_tree_MSE; __pyx_vtable_7sklearn_5_tree_FriedmanMSE.__pyx_base.__pyx_base.__pyx_base.impurity_improvement = (double (*)(struct __pyx_obj_7sklearn_5_tree_Criterion *, double))__pyx_f_7sklearn_5_tree_11FriedmanMSE_impurity_improvement; __pyx_type_7sklearn_5_tree_FriedmanMSE.tp_base = __pyx_ptype_7sklearn_5_tree_MSE; if (PyType_Ready(&__pyx_type_7sklearn_5_tree_FriedmanMSE) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1090; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_type_7sklearn_5_tree_FriedmanMSE.tp_print = 0; if (__Pyx_SetVtable(__pyx_type_7sklearn_5_tree_FriedmanMSE.tp_dict, __pyx_vtabptr_7sklearn_5_tree_FriedmanMSE) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1090; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (PyObject_SetAttrString(__pyx_m, "FriedmanMSE", (PyObject *)&__pyx_type_7sklearn_5_tree_FriedmanMSE) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1090; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_7sklearn_5_tree_FriedmanMSE = &__pyx_type_7sklearn_5_tree_FriedmanMSE; __pyx_vtabptr_7sklearn_5_tree_BaseDenseSplitter = &__pyx_vtable_7sklearn_5_tree_BaseDenseSplitter; __pyx_vtable_7sklearn_5_tree_BaseDenseSplitter.__pyx_base = *__pyx_vtabptr_7sklearn_5_tree_Splitter; __pyx_vtable_7sklearn_5_tree_BaseDenseSplitter.__pyx_base.init = (void (*)(struct __pyx_obj_7sklearn_5_tree_Splitter *, PyObject *, PyArrayObject *, __pyx_t_7sklearn_5_tree_DOUBLE_t *))__pyx_f_7sklearn_5_tree_17BaseDenseSplitter_init; __pyx_type_7sklearn_5_tree_BaseDenseSplitter.tp_base = __pyx_ptype_7sklearn_5_tree_Splitter; if (PyType_Ready(&__pyx_type_7sklearn_5_tree_BaseDenseSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1244; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_type_7sklearn_5_tree_BaseDenseSplitter.tp_print = 0; if (__Pyx_SetVtable(__pyx_type_7sklearn_5_tree_BaseDenseSplitter.tp_dict, __pyx_vtabptr_7sklearn_5_tree_BaseDenseSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1244; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (PyObject_SetAttrString(__pyx_m, "BaseDenseSplitter", (PyObject *)&__pyx_type_7sklearn_5_tree_BaseDenseSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1244; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_7sklearn_5_tree_BaseDenseSplitter = &__pyx_type_7sklearn_5_tree_BaseDenseSplitter; __pyx_vtabptr_7sklearn_5_tree_BestSplitter = &__pyx_vtable_7sklearn_5_tree_BestSplitter; __pyx_vtable_7sklearn_5_tree_BestSplitter.__pyx_base = *__pyx_vtabptr_7sklearn_5_tree_BaseDenseSplitter; __pyx_vtable_7sklearn_5_tree_BestSplitter.__pyx_base.__pyx_base.node_split = (void (*)(struct __pyx_obj_7sklearn_5_tree_Splitter *, double, struct __pyx_t_7sklearn_5_tree_SplitRecord *, __pyx_t_7sklearn_5_tree_SIZE_t *))__pyx_f_7sklearn_5_tree_12BestSplitter_node_split; __pyx_type_7sklearn_5_tree_BestSplitter.tp_base = __pyx_ptype_7sklearn_5_tree_BaseDenseSplitter; if (PyType_Ready(&__pyx_type_7sklearn_5_tree_BestSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1275; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_type_7sklearn_5_tree_BestSplitter.tp_print = 0; if (__Pyx_SetVtable(__pyx_type_7sklearn_5_tree_BestSplitter.tp_dict, __pyx_vtabptr_7sklearn_5_tree_BestSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1275; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (PyObject_SetAttrString(__pyx_m, "BestSplitter", (PyObject *)&__pyx_type_7sklearn_5_tree_BestSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1275; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_7sklearn_5_tree_BestSplitter = &__pyx_type_7sklearn_5_tree_BestSplitter; __pyx_vtabptr_7sklearn_5_tree_RandomSplitter = &__pyx_vtable_7sklearn_5_tree_RandomSplitter; __pyx_vtable_7sklearn_5_tree_RandomSplitter.__pyx_base = *__pyx_vtabptr_7sklearn_5_tree_BaseDenseSplitter; __pyx_vtable_7sklearn_5_tree_RandomSplitter.__pyx_base.__pyx_base.node_split = (void (*)(struct __pyx_obj_7sklearn_5_tree_Splitter *, double, struct __pyx_t_7sklearn_5_tree_SplitRecord *, __pyx_t_7sklearn_5_tree_SIZE_t *))__pyx_f_7sklearn_5_tree_14RandomSplitter_node_split; __pyx_type_7sklearn_5_tree_RandomSplitter.tp_base = __pyx_ptype_7sklearn_5_tree_BaseDenseSplitter; if (PyType_Ready(&__pyx_type_7sklearn_5_tree_RandomSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1574; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_type_7sklearn_5_tree_RandomSplitter.tp_print = 0; if (__Pyx_SetVtable(__pyx_type_7sklearn_5_tree_RandomSplitter.tp_dict, __pyx_vtabptr_7sklearn_5_tree_RandomSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1574; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (PyObject_SetAttrString(__pyx_m, "RandomSplitter", (PyObject *)&__pyx_type_7sklearn_5_tree_RandomSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1574; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_7sklearn_5_tree_RandomSplitter = &__pyx_type_7sklearn_5_tree_RandomSplitter; __pyx_vtabptr_7sklearn_5_tree_PresortBestSplitter = &__pyx_vtable_7sklearn_5_tree_PresortBestSplitter; __pyx_vtable_7sklearn_5_tree_PresortBestSplitter.__pyx_base = *__pyx_vtabptr_7sklearn_5_tree_BaseDenseSplitter; __pyx_vtable_7sklearn_5_tree_PresortBestSplitter.__pyx_base.__pyx_base.init = (void (*)(struct __pyx_obj_7sklearn_5_tree_Splitter *, PyObject *, PyArrayObject *, __pyx_t_7sklearn_5_tree_DOUBLE_t *))__pyx_f_7sklearn_5_tree_19PresortBestSplitter_init; __pyx_vtable_7sklearn_5_tree_PresortBestSplitter.__pyx_base.__pyx_base.node_split = (void (*)(struct __pyx_obj_7sklearn_5_tree_Splitter *, double, struct __pyx_t_7sklearn_5_tree_SplitRecord *, __pyx_t_7sklearn_5_tree_SIZE_t *))__pyx_f_7sklearn_5_tree_19PresortBestSplitter_node_split; __pyx_type_7sklearn_5_tree_PresortBestSplitter.tp_base = __pyx_ptype_7sklearn_5_tree_BaseDenseSplitter; if (PyType_Ready(&__pyx_type_7sklearn_5_tree_PresortBestSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1776; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_type_7sklearn_5_tree_PresortBestSplitter.tp_print = 0; if (__Pyx_SetVtable(__pyx_type_7sklearn_5_tree_PresortBestSplitter.tp_dict, __pyx_vtabptr_7sklearn_5_tree_PresortBestSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1776; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (PyObject_SetAttrString(__pyx_m, "PresortBestSplitter", (PyObject *)&__pyx_type_7sklearn_5_tree_PresortBestSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1776; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_7sklearn_5_tree_PresortBestSplitter = &__pyx_type_7sklearn_5_tree_PresortBestSplitter; __pyx_vtabptr_7sklearn_5_tree_BaseSparseSplitter = &__pyx_vtable_7sklearn_5_tree_BaseSparseSplitter; __pyx_vtable_7sklearn_5_tree_BaseSparseSplitter.__pyx_base = *__pyx_vtabptr_7sklearn_5_tree_Splitter; __pyx_vtable_7sklearn_5_tree_BaseSparseSplitter.__pyx_base.init = (void (*)(struct __pyx_obj_7sklearn_5_tree_Splitter *, PyObject *, PyArrayObject *, __pyx_t_7sklearn_5_tree_DOUBLE_t *))__pyx_f_7sklearn_5_tree_18BaseSparseSplitter_init; __pyx_vtable_7sklearn_5_tree_BaseSparseSplitter._partition = (__pyx_t_7sklearn_5_tree_SIZE_t (*)(struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter *, double, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t))__pyx_f_7sklearn_5_tree_18BaseSparseSplitter__partition; __pyx_vtable_7sklearn_5_tree_BaseSparseSplitter.extract_nnz = (void (*)(struct __pyx_obj_7sklearn_5_tree_BaseSparseSplitter *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t *, __pyx_t_7sklearn_5_tree_SIZE_t *, int *))__pyx_f_7sklearn_5_tree_18BaseSparseSplitter_extract_nnz; __pyx_type_7sklearn_5_tree_BaseSparseSplitter.tp_base = __pyx_ptype_7sklearn_5_tree_Splitter; if (PyType_Ready(&__pyx_type_7sklearn_5_tree_BaseSparseSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2024; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_type_7sklearn_5_tree_BaseSparseSplitter.tp_print = 0; if (__Pyx_SetVtable(__pyx_type_7sklearn_5_tree_BaseSparseSplitter.tp_dict, __pyx_vtabptr_7sklearn_5_tree_BaseSparseSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2024; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (PyObject_SetAttrString(__pyx_m, "BaseSparseSplitter", (PyObject *)&__pyx_type_7sklearn_5_tree_BaseSparseSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2024; 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__pyx_lineno = 2340; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (PyObject_SetAttrString(__pyx_m, "BestSparseSplitter", (PyObject *)&__pyx_type_7sklearn_5_tree_BestSparseSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2340; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_7sklearn_5_tree_BestSparseSplitter = &__pyx_type_7sklearn_5_tree_BestSparseSplitter; __pyx_vtabptr_7sklearn_5_tree_RandomSparseSplitter = &__pyx_vtable_7sklearn_5_tree_RandomSparseSplitter; __pyx_vtable_7sklearn_5_tree_RandomSparseSplitter.__pyx_base = *__pyx_vtabptr_7sklearn_5_tree_BaseSparseSplitter; __pyx_vtable_7sklearn_5_tree_RandomSparseSplitter.__pyx_base.__pyx_base.node_split = (void (*)(struct __pyx_obj_7sklearn_5_tree_Splitter *, double, struct __pyx_t_7sklearn_5_tree_SplitRecord *, __pyx_t_7sklearn_5_tree_SIZE_t *))__pyx_f_7sklearn_5_tree_20RandomSparseSplitter_node_split; __pyx_type_7sklearn_5_tree_RandomSparseSplitter.tp_base = __pyx_ptype_7sklearn_5_tree_BaseSparseSplitter; if (PyType_Ready(&__pyx_type_7sklearn_5_tree_RandomSparseSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_type_7sklearn_5_tree_RandomSparseSplitter.tp_print = 0; if (__Pyx_SetVtable(__pyx_type_7sklearn_5_tree_RandomSparseSplitter.tp_dict, __pyx_vtabptr_7sklearn_5_tree_RandomSparseSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (PyObject_SetAttrString(__pyx_m, "RandomSparseSplitter", (PyObject *)&__pyx_type_7sklearn_5_tree_RandomSparseSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_7sklearn_5_tree_RandomSparseSplitter = &__pyx_type_7sklearn_5_tree_RandomSparseSplitter; __pyx_vtabptr_7sklearn_5_tree_DepthFirstTreeBuilder = &__pyx_vtable_7sklearn_5_tree_DepthFirstTreeBuilder; __pyx_vtable_7sklearn_5_tree_DepthFirstTreeBuilder.__pyx_base = *__pyx_vtabptr_7sklearn_5_tree_TreeBuilder; __pyx_vtable_7sklearn_5_tree_DepthFirstTreeBuilder.__pyx_base.build = (PyObject *(*)(struct __pyx_obj_7sklearn_5_tree_TreeBuilder *, struct __pyx_obj_7sklearn_5_tree_Tree *, PyObject *, PyArrayObject *, int __pyx_skip_dispatch, struct __pyx_opt_args_7sklearn_5_tree_11TreeBuilder_build *__pyx_optional_args))__pyx_f_7sklearn_5_tree_21DepthFirstTreeBuilder_build; __pyx_type_7sklearn_5_tree_DepthFirstTreeBuilder.tp_base = __pyx_ptype_7sklearn_5_tree_TreeBuilder; if (PyType_Ready(&__pyx_type_7sklearn_5_tree_DepthFirstTreeBuilder) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2804; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_type_7sklearn_5_tree_DepthFirstTreeBuilder.tp_print = 0; if (__Pyx_SetVtable(__pyx_type_7sklearn_5_tree_DepthFirstTreeBuilder.tp_dict, __pyx_vtabptr_7sklearn_5_tree_DepthFirstTreeBuilder) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2804; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (PyObject_SetAttrString(__pyx_m, "DepthFirstTreeBuilder", (PyObject *)&__pyx_type_7sklearn_5_tree_DepthFirstTreeBuilder) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2804; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_7sklearn_5_tree_DepthFirstTreeBuilder = &__pyx_type_7sklearn_5_tree_DepthFirstTreeBuilder; __pyx_vtabptr_7sklearn_5_tree_BestFirstTreeBuilder = &__pyx_vtable_7sklearn_5_tree_BestFirstTreeBuilder; __pyx_vtable_7sklearn_5_tree_BestFirstTreeBuilder.__pyx_base = *__pyx_vtabptr_7sklearn_5_tree_TreeBuilder; __pyx_vtable_7sklearn_5_tree_BestFirstTreeBuilder.__pyx_base.build = (PyObject *(*)(struct __pyx_obj_7sklearn_5_tree_TreeBuilder *, struct __pyx_obj_7sklearn_5_tree_Tree *, PyObject *, PyArrayObject *, int __pyx_skip_dispatch, struct __pyx_opt_args_7sklearn_5_tree_11TreeBuilder_build *__pyx_optional_args))__pyx_f_7sklearn_5_tree_20BestFirstTreeBuilder_build; __pyx_vtable_7sklearn_5_tree_BestFirstTreeBuilder._add_split_node = (int (*)(struct __pyx_obj_7sklearn_5_tree_BestFirstTreeBuilder *, struct __pyx_obj_7sklearn_5_tree_Splitter *, struct __pyx_obj_7sklearn_5_tree_Tree *, __pyx_t_7sklearn_5_tree_SIZE_t, __pyx_t_7sklearn_5_tree_SIZE_t, double, int, int, struct __pyx_t_7sklearn_5_tree_Node *, __pyx_t_7sklearn_5_tree_SIZE_t, struct __pyx_t_7sklearn_5_tree_PriorityHeapRecord *))__pyx_f_7sklearn_5_tree_20BestFirstTreeBuilder__add_split_node; __pyx_type_7sklearn_5_tree_BestFirstTreeBuilder.tp_base = __pyx_ptype_7sklearn_5_tree_TreeBuilder; if (PyType_Ready(&__pyx_type_7sklearn_5_tree_BestFirstTreeBuilder) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2950; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_type_7sklearn_5_tree_BestFirstTreeBuilder.tp_print = 0; if (__Pyx_SetVtable(__pyx_type_7sklearn_5_tree_BestFirstTreeBuilder.tp_dict, __pyx_vtabptr_7sklearn_5_tree_BestFirstTreeBuilder) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2950; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (PyObject_SetAttrString(__pyx_m, "BestFirstTreeBuilder", (PyObject *)&__pyx_type_7sklearn_5_tree_BestFirstTreeBuilder) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2950; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_7sklearn_5_tree_BestFirstTreeBuilder = &__pyx_type_7sklearn_5_tree_BestFirstTreeBuilder; /*--- Type import code ---*/ __pyx_ptype_7cpython_4type_type = __Pyx_ImportType(__Pyx_BUILTIN_MODULE_NAME, "type", #if CYTHON_COMPILING_IN_PYPY sizeof(PyTypeObject), #else sizeof(PyHeapTypeObject), #endif 0); if (unlikely(!__pyx_ptype_7cpython_4type_type)) {__pyx_filename = __pyx_f[3]; __pyx_lineno = 9; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_5numpy_dtype = __Pyx_ImportType("numpy", "dtype", sizeof(PyArray_Descr), 0); if (unlikely(!__pyx_ptype_5numpy_dtype)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 155; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_5numpy_flatiter = __Pyx_ImportType("numpy", "flatiter", sizeof(PyArrayIterObject), 0); if (unlikely(!__pyx_ptype_5numpy_flatiter)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 165; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_5numpy_broadcast = __Pyx_ImportType("numpy", "broadcast", sizeof(PyArrayMultiIterObject), 0); if (unlikely(!__pyx_ptype_5numpy_broadcast)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 169; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_5numpy_ndarray = __Pyx_ImportType("numpy", "ndarray", sizeof(PyArrayObject), 0); if (unlikely(!__pyx_ptype_5numpy_ndarray)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 178; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_5numpy_ufunc = __Pyx_ImportType("numpy", "ufunc", sizeof(PyUFuncObject), 0); if (unlikely(!__pyx_ptype_5numpy_ufunc)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 861; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_7cpython_4bool_bool = __Pyx_ImportType(__Pyx_BUILTIN_MODULE_NAME, "bool", sizeof(PyBoolObject), 0); if (unlikely(!__pyx_ptype_7cpython_4bool_bool)) {__pyx_filename = __pyx_f[4]; __pyx_lineno = 8; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_7cpython_7complex_complex = __Pyx_ImportType(__Pyx_BUILTIN_MODULE_NAME, "complex", sizeof(PyComplexObject), 0); if (unlikely(!__pyx_ptype_7cpython_7complex_complex)) {__pyx_filename = __pyx_f[5]; __pyx_lineno = 15; __pyx_clineno = __LINE__; goto __pyx_L1_error;} /*--- Variable import code ---*/ /*--- Function import code ---*/ /*--- Execution code ---*/ /* "sklearn/_tree.pyx":23 * from cpython cimport Py_INCREF, PyObject * * import numpy as np # <<<<<<<<<<<<<< * cimport numpy as np * np.import_array() */ __pyx_t_1 = __Pyx_Import(__pyx_n_s_numpy, 0, -1); 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This will probably the same as above, but we don't have any guarantees. */ typedef struct { short x; char c; } __Pyx_pad_short; typedef struct { int x; char c; } __Pyx_pad_int; typedef struct { long x; char c; } __Pyx_pad_long; typedef struct { float x; char c; } __Pyx_pad_float; typedef struct { double x; char c; } __Pyx_pad_double; typedef struct { long double x; char c; } __Pyx_pad_longdouble; typedef struct { void *x; char c; } __Pyx_pad_void_p; #ifdef HAVE_LONG_LONG typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { switch (ch) { case 'c': return 'H'; case 'b': case 'h': case 'i': case 'l': case 'q': case 's': case 'p': return 'I'; case 'B': case 'H': case 'I': case 'L': case 'Q': return 'U'; case 'f': case 'd': case 'g': return (is_complex ? 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int i = 0, number; int ndim = ctx->head->field->type->ndim; ; ++ts; if (ctx->new_count != 1) { PyErr_SetString(PyExc_ValueError, "Cannot handle repeated arrays in format string"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; while (*ts && *ts != ')') { switch (*ts) { case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; default: break; } number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) return PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %d", ctx->head->field->type->arraysize[i], number); if (*ts != ',' && *ts != ')') return PyErr_Format(PyExc_ValueError, "Expected a comma in format string, got '%c'", *ts); if (*ts == ',') ts++; i++; } if (i != ndim) return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", ctx->head->field->type->ndim, i); if (!*ts) { PyErr_SetString(PyExc_ValueError, "Unexpected end of format string, expected ')'"); return NULL; } ctx->is_valid_array = 1; ctx->new_count = 1; *tsp = ++ts; return Py_None; } static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { int got_Z = 0; while (1) { switch(*ts) { case 0: if (ctx->enc_type != 0 && ctx->head == NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; if (ctx->head != NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } return ts; case ' ': case '\r': case '\n': ++ts; break; case '<': if (!__Pyx_IsLittleEndian()) { PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '>': case '!': if (__Pyx_IsLittleEndian()) { PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '=': case '@': case '^': ctx->new_packmode = *ts++; break; case 'T': { const char* ts_after_sub; size_t i, struct_count = ctx->new_count; size_t struct_alignment = ctx->struct_alignment; ctx->new_count = 1; ++ts; if (*ts != '{') { PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; ctx->enc_count = 0; ctx->struct_alignment = 0; ++ts; ts_after_sub = ts; for (i = 0; i != struct_count; ++i) { ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); if (!ts_after_sub) return NULL; } ts = ts_after_sub; if (struct_alignment) ctx->struct_alignment = struct_alignment; } break; case '}': { size_t alignment = ctx->struct_alignment; ++ts; if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; if (alignment && ctx->fmt_offset % alignment) { ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); } } return ts; case 'x': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->fmt_offset += ctx->new_count; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->enc_packmode = ctx->new_packmode; ++ts; break; case 'Z': got_Z = 1; ++ts; if (*ts != 'f' && *ts != 'd' && *ts != 'g') { __Pyx_BufFmt_RaiseUnexpectedChar('Z'); return NULL; } case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': case 'l': case 'L': case 'q': case 'Q': case 'f': case 'd': case 'g': case 'O': case 'p': if (ctx->enc_type == *ts && got_Z == ctx->is_complex && ctx->enc_packmode == ctx->new_packmode) { ctx->enc_count += ctx->new_count; ctx->new_count = 1; got_Z = 0; ++ts; break; } case 's': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_count = ctx->new_count; ctx->enc_packmode = ctx->new_packmode; ctx->enc_type = *ts; ctx->is_complex = got_Z; ++ts; ctx->new_count = 1; got_Z = 0; break; case ':': ++ts; while(*ts != ':') ++ts; ++ts; break; case '(': if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; break; default: { int number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; ctx->new_count = (size_t)number; } } } } static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { buf->buf = NULL; buf->obj = NULL; buf->strides = __Pyx_zeros; buf->shape = __Pyx_zeros; buf->suboffsets = __Pyx_minusones; } static CYTHON_INLINE int __Pyx_GetBufferAndValidate( Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack) { if (obj == Py_None || obj == NULL) { __Pyx_ZeroBuffer(buf); return 0; } buf->buf = NULL; if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; if (buf->ndim != nd) { PyErr_Format(PyExc_ValueError, "Buffer has wrong number of dimensions (expected %d, got %d)", nd, buf->ndim); goto fail; } if (!cast) { __Pyx_BufFmt_Context ctx; __Pyx_BufFmt_Init(&ctx, stack, dtype); if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; } if ((unsigned)buf->itemsize != dtype->size) { PyErr_Format(PyExc_ValueError, "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", buf->itemsize, (buf->itemsize > 1) ? "s" : "", dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); goto fail; } if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; return 0; fail:; __Pyx_ZeroBuffer(buf); return -1; } static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { if (info->buf == NULL) return; if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; __Pyx_ReleaseBuffer(info); } static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { #if CYTHON_COMPILING_IN_CPYTHON PyObject *tmp_type, *tmp_value, *tmp_tb; PyThreadState *tstate = PyThreadState_GET(); tmp_type = tstate->curexc_type; tmp_value = tstate->curexc_value; tmp_tb = tstate->curexc_traceback; tstate->curexc_type = type; tstate->curexc_value = value; tstate->curexc_traceback = tb; Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); #else PyErr_Restore(type, value, tb); #endif } static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { #if CYTHON_COMPILING_IN_CPYTHON PyThreadState *tstate = PyThreadState_GET(); *type = tstate->curexc_type; *value = tstate->curexc_value; *tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; #else PyErr_Fetch(type, value, tb); #endif } #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { PyObject *result; ternaryfunc call = func->ob_type->tp_call; if (unlikely(!call)) return PyObject_Call(func, arg, kw); if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) return NULL; result = (*call)(func, arg, kw); Py_LeaveRecursiveCall(); if (unlikely(!result) && unlikely(!PyErr_Occurred())) { PyErr_SetString( PyExc_SystemError, "NULL result without error in PyObject_Call"); } return result; } #endif #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { PyObject *self, *result; PyCFunction cfunc; cfunc = PyCFunction_GET_FUNCTION(func); self = PyCFunction_GET_SELF(func); if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) return NULL; result = cfunc(self, arg); Py_LeaveRecursiveCall(); if (unlikely(!result) && unlikely(!PyErr_Occurred())) { PyErr_SetString( PyExc_SystemError, "NULL result without error in PyObject_Call"); } return result; } #endif #if CYTHON_COMPILING_IN_CPYTHON static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { PyObject *result; PyObject *args = PyTuple_New(1); if (unlikely(!args)) return NULL; Py_INCREF(arg); PyTuple_SET_ITEM(args, 0, arg); result = __Pyx_PyObject_Call(func, args, NULL); Py_DECREF(args); return result; } static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { #ifdef __Pyx_CyFunction_USED if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) { #else if (likely(PyCFunction_Check(func))) { #endif if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { return __Pyx_PyObject_CallMethO(func, arg); } } return __Pyx__PyObject_CallOneArg(func, arg); } #else static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { PyObject* args = PyTuple_Pack(1, arg); return (likely(args)) ? __Pyx_PyObject_Call(func, args, NULL) : NULL; } #endif #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) { #ifdef __Pyx_CyFunction_USED if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) { #else if (likely(PyCFunction_Check(func))) { #endif if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) { return __Pyx_PyObject_CallMethO(func, NULL); } } return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL); } #endif static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { PyObject *r; if (!j) return NULL; r = PyObject_GetItem(o, j); Py_DECREF(j); return r; } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, int wraparound, int boundscheck) { #if CYTHON_COMPILING_IN_CPYTHON if (wraparound & unlikely(i < 0)) i += PyList_GET_SIZE(o); if ((!boundscheck) || likely((0 <= i) & (i < PyList_GET_SIZE(o)))) { PyObject *r = PyList_GET_ITEM(o, i); Py_INCREF(r); return r; } return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); #else return PySequence_GetItem(o, i); #endif } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, int wraparound, int boundscheck) { #if CYTHON_COMPILING_IN_CPYTHON if (wraparound & unlikely(i < 0)) i += PyTuple_GET_SIZE(o); if ((!boundscheck) || likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) { PyObject *r = PyTuple_GET_ITEM(o, i); Py_INCREF(r); return r; } return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); #else return PySequence_GetItem(o, i); #endif } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, int wraparound, int boundscheck) { #if CYTHON_COMPILING_IN_CPYTHON if (is_list || PyList_CheckExact(o)) { Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) { PyObject *r = PyList_GET_ITEM(o, n); Py_INCREF(r); return r; } } else if (PyTuple_CheckExact(o)) { Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) { PyObject *r = PyTuple_GET_ITEM(o, n); Py_INCREF(r); return r; } } else { PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; if (likely(m && m->sq_item)) { if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { Py_ssize_t l = m->sq_length(o); if (likely(l >= 0)) { i += l; } else { if (PyErr_ExceptionMatches(PyExc_OverflowError)) PyErr_Clear(); else return NULL; } } return m->sq_item(o, i); } } #else if (is_list || PySequence_Check(o)) { return PySequence_GetItem(o, i); } #endif return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); } static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { if (unlikely(!type)) { PyErr_SetString(PyExc_SystemError, "Missing type object"); return 0; } if (likely(PyObject_TypeCheck(obj, type))) return 1; PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", Py_TYPE(obj)->tp_name, type->tp_name); return 0; } static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { PyObject *result; #if CYTHON_COMPILING_IN_CPYTHON result = PyDict_GetItem(__pyx_d, name); if (likely(result)) { Py_INCREF(result); } else { #else result = PyObject_GetItem(__pyx_d, name); if (!result) { PyErr_Clear(); #endif result = __Pyx_GetBuiltinName(name); } return result; } static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { PyErr_Format(PyExc_ValueError, "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); } static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { PyErr_Format(PyExc_ValueError, "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", index, (index == 1) ? "" : "s"); } static CYTHON_INLINE int __Pyx_IterFinish(void) { #if CYTHON_COMPILING_IN_CPYTHON PyThreadState *tstate = PyThreadState_GET(); PyObject* exc_type = tstate->curexc_type; if (unlikely(exc_type)) { if (likely(exc_type == PyExc_StopIteration) || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration)) { PyObject *exc_value, *exc_tb; exc_value = tstate->curexc_value; exc_tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; Py_DECREF(exc_type); Py_XDECREF(exc_value); Py_XDECREF(exc_tb); return 0; } else { return -1; } } return 0; #else if (unlikely(PyErr_Occurred())) { if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) { PyErr_Clear(); return 0; } else { return -1; } } return 0; #endif } static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) { if (unlikely(retval)) { Py_DECREF(retval); __Pyx_RaiseTooManyValuesError(expected); return -1; } else { return __Pyx_IterFinish(); } return 0; } static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice( PyObject* obj, Py_ssize_t cstart, Py_ssize_t cstop, PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice, int has_cstart, int has_cstop, CYTHON_UNUSED int wraparound) { #if CYTHON_COMPILING_IN_CPYTHON PyMappingMethods* mp; #if PY_MAJOR_VERSION < 3 PySequenceMethods* ms = Py_TYPE(obj)->tp_as_sequence; if (likely(ms && ms->sq_slice)) { if (!has_cstart) { if (_py_start && (*_py_start != Py_None)) { cstart = __Pyx_PyIndex_AsSsize_t(*_py_start); if ((cstart == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; } else cstart = 0; } if (!has_cstop) { if (_py_stop && (*_py_stop != Py_None)) { cstop = __Pyx_PyIndex_AsSsize_t(*_py_stop); if ((cstop == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; } else cstop = PY_SSIZE_T_MAX; } if (wraparound && unlikely((cstart < 0) | (cstop < 0)) && likely(ms->sq_length)) { Py_ssize_t l = ms->sq_length(obj); if (likely(l >= 0)) { if (cstop < 0) { cstop += l; if (cstop < 0) cstop = 0; } if (cstart < 0) { cstart += l; if (cstart < 0) cstart = 0; } } else { if (PyErr_ExceptionMatches(PyExc_OverflowError)) PyErr_Clear(); else goto bad; } } return ms->sq_slice(obj, cstart, cstop); } #endif mp = Py_TYPE(obj)->tp_as_mapping; if (likely(mp && mp->mp_subscript)) #endif { PyObject* result; PyObject *py_slice, *py_start, *py_stop; if (_py_slice) { py_slice = *_py_slice; } else { PyObject* owned_start = NULL; PyObject* owned_stop = NULL; if (_py_start) { py_start = *_py_start; } else { if (has_cstart) { owned_start = py_start = PyInt_FromSsize_t(cstart); if (unlikely(!py_start)) goto bad; } else py_start = Py_None; } if (_py_stop) { py_stop = *_py_stop; } else { if (has_cstop) { owned_stop = py_stop = PyInt_FromSsize_t(cstop); if (unlikely(!py_stop)) { Py_XDECREF(owned_start); goto bad; } } else py_stop = Py_None; } py_slice = PySlice_New(py_start, py_stop, Py_None); Py_XDECREF(owned_start); Py_XDECREF(owned_stop); if (unlikely(!py_slice)) goto bad; } #if CYTHON_COMPILING_IN_CPYTHON result = mp->mp_subscript(obj, py_slice); #else result = PyObject_GetItem(obj, py_slice); #endif if (!_py_slice) { Py_DECREF(py_slice); } return result; } PyErr_Format(PyExc_TypeError, "'%.200s' object is unsliceable", Py_TYPE(obj)->tp_name); bad: return NULL; } #if PY_MAJOR_VERSION < 3 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, CYTHON_UNUSED PyObject *cause) { Py_XINCREF(type); if (!value || value == Py_None) value = NULL; else Py_INCREF(value); if (!tb || tb == Py_None) tb = NULL; else { Py_INCREF(tb); if (!PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto raise_error; } } if (PyType_Check(type)) { #if CYTHON_COMPILING_IN_PYPY if (!value) { Py_INCREF(Py_None); value = Py_None; } #endif PyErr_NormalizeException(&type, &value, &tb); } else { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto raise_error; } value = type; type = (PyObject*) Py_TYPE(type); Py_INCREF(type); if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto raise_error; } } __Pyx_ErrRestore(type, value, tb); return; raise_error: Py_XDECREF(value); Py_XDECREF(type); Py_XDECREF(tb); return; } #else static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { PyObject* owned_instance = NULL; if (tb == Py_None) { tb = 0; } else if (tb && !PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto bad; } if (value == Py_None) value = 0; if (PyExceptionInstance_Check(type)) { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto bad; } value = type; type = (PyObject*) Py_TYPE(value); } else if (PyExceptionClass_Check(type)) { PyObject *instance_class = NULL; if (value && PyExceptionInstance_Check(value)) { instance_class = (PyObject*) Py_TYPE(value); if (instance_class != type) { if (PyObject_IsSubclass(instance_class, type)) { type = instance_class; } else { instance_class = NULL; } } } if (!instance_class) { PyObject *args; if (!value) args = PyTuple_New(0); else if (PyTuple_Check(value)) { Py_INCREF(value); args = value; } else args = PyTuple_Pack(1, value); if (!args) goto bad; owned_instance = PyObject_Call(type, args, NULL); Py_DECREF(args); if (!owned_instance) goto bad; value = owned_instance; if (!PyExceptionInstance_Check(value)) { PyErr_Format(PyExc_TypeError, "calling %R should have returned an instance of " "BaseException, not %R", type, Py_TYPE(value)); goto bad; } } } else { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto bad; } #if PY_VERSION_HEX >= 0x03030000 if (cause) { #else if (cause && cause != Py_None) { #endif PyObject *fixed_cause; if (cause == Py_None) { fixed_cause = NULL; } else if (PyExceptionClass_Check(cause)) { fixed_cause = PyObject_CallObject(cause, NULL); if (fixed_cause == NULL) goto bad; } else if (PyExceptionInstance_Check(cause)) { fixed_cause = cause; Py_INCREF(fixed_cause); } else { PyErr_SetString(PyExc_TypeError, "exception causes must derive from " "BaseException"); goto bad; } PyException_SetCause(value, fixed_cause); } PyErr_SetObject(type, value); if (tb) { #if CYTHON_COMPILING_IN_PYPY PyObject *tmp_type, *tmp_value, *tmp_tb; PyErr_Fetch(tmp_type, tmp_value, tmp_tb); Py_INCREF(tb); PyErr_Restore(tmp_type, tmp_value, tb); Py_XDECREF(tmp_tb); #else PyThreadState *tstate = PyThreadState_GET(); PyObject* tmp_tb = tstate->curexc_traceback; if (tb != tmp_tb) { Py_INCREF(tb); tstate->curexc_traceback = tb; Py_XDECREF(tmp_tb); } #endif } bad: Py_XDECREF(owned_instance); return; } #endif static void __Pyx_RaiseBufferFallbackError(void) { PyErr_SetString(PyExc_ValueError, "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); } static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); } static int __Pyx_SetVtable(PyObject *dict, void *vtable) { #if PY_VERSION_HEX >= 0x02070000 PyObject *ob = PyCapsule_New(vtable, 0, 0); #else PyObject *ob = PyCObject_FromVoidPtr(vtable, 0); #endif if (!ob) goto bad; if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0) goto bad; Py_DECREF(ob); return 0; bad: Py_XDECREF(ob); return -1; } static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { PyErr_Format(PyExc_ImportError, #if PY_MAJOR_VERSION < 3 "cannot import name %.230s", PyString_AS_STRING(name)); #else "cannot import name %S", name); #endif } return value; } static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { int start = 0, mid = 0, end = count - 1; if (end >= 0 && code_line > entries[end].code_line) { return count; } while (start < end) { mid = (start + end) / 2; if (code_line < entries[mid].code_line) { end = mid; } else if (code_line > entries[mid].code_line) { start = mid + 1; } else { return mid; } } if (code_line <= entries[mid].code_line) { return mid; } else { return mid + 1; } } static PyCodeObject *__pyx_find_code_object(int code_line) { PyCodeObject* code_object; int pos; if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { return NULL; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { return NULL; } code_object = __pyx_code_cache.entries[pos].code_object; Py_INCREF(code_object); return code_object; } static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { int pos, i; __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; if (unlikely(!code_line)) { return; } if (unlikely(!entries)) { entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); if (likely(entries)) { __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = 64; __pyx_code_cache.count = 1; entries[0].code_line = code_line; entries[0].code_object = code_object; Py_INCREF(code_object); } return; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { PyCodeObject* tmp = entries[pos].code_object; entries[pos].code_object = code_object; Py_DECREF(tmp); return; } if (__pyx_code_cache.count == __pyx_code_cache.max_count) { int new_max = __pyx_code_cache.max_count + 64; entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); if (unlikely(!entries)) { return; } __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = new_max; } for (i=__pyx_code_cache.count; i>pos; i--) { entries[i] = entries[i-1]; } entries[pos].code_line = code_line; entries[pos].code_object = code_object; __pyx_code_cache.count++; Py_INCREF(code_object); } #include "compile.h" #include "frameobject.h" #include "traceback.h" static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyObject *py_srcfile = 0; PyObject *py_funcname = 0; #if PY_MAJOR_VERSION < 3 py_srcfile = PyString_FromString(filename); #else py_srcfile = PyUnicode_FromString(filename); #endif if (!py_srcfile) goto bad; if (c_line) { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #else py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #endif } else { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromString(funcname); #else py_funcname = PyUnicode_FromString(funcname); #endif } if (!py_funcname) goto bad; py_code = __Pyx_PyCode_New( 0, 0, 0, 0, 0, __pyx_empty_bytes, /*PyObject *code,*/ __pyx_empty_tuple, /*PyObject *consts,*/ __pyx_empty_tuple, /*PyObject *names,*/ __pyx_empty_tuple, /*PyObject *varnames,*/ __pyx_empty_tuple, /*PyObject *freevars,*/ __pyx_empty_tuple, /*PyObject *cellvars,*/ py_srcfile, /*PyObject *filename,*/ py_funcname, /*PyObject *name,*/ py_line, __pyx_empty_bytes /*PyObject *lnotab*/ ); Py_DECREF(py_srcfile); Py_DECREF(py_funcname); return py_code; bad: Py_XDECREF(py_srcfile); Py_XDECREF(py_funcname); return NULL; } static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyFrameObject *py_frame = 0; py_code = __pyx_find_code_object(c_line ? c_line : py_line); if (!py_code) { py_code = __Pyx_CreateCodeObjectForTraceback( funcname, c_line, py_line, filename); if (!py_code) goto bad; __pyx_insert_code_object(c_line ? c_line : py_line, py_code); } py_frame = PyFrame_New( PyThreadState_GET(), /*PyThreadState *tstate,*/ py_code, /*PyCodeObject *code,*/ __pyx_d, /*PyObject *globals,*/ 0 /*PyObject *locals*/ ); if (!py_frame) goto bad; py_frame->f_lineno = py_line; PyTraceBack_Here(py_frame); bad: Py_XDECREF(py_code); Py_XDECREF(py_frame); } static CYTHON_INLINE PyObject* __Pyx_PyInt_From_Py_intptr_t(Py_intptr_t value) { const Py_intptr_t neg_one = (Py_intptr_t) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(Py_intptr_t) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(Py_intptr_t) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); } else if (sizeof(Py_intptr_t) <= sizeof(unsigned long long)) { return PyLong_FromUnsignedLongLong((unsigned long long) value); } } else { if (sizeof(Py_intptr_t) <= sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(Py_intptr_t) <= sizeof(long long)) { return PyLong_FromLongLong((long long) value); } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(Py_intptr_t), little, !is_unsigned); } } #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value) \ { \ func_type value = func_value; \ if (sizeof(target_type) < sizeof(func_type)) { \ if (unlikely(value != (func_type) (target_type) value)) { \ func_type zero = 0; \ if (is_unsigned && unlikely(value < zero)) \ goto raise_neg_overflow; \ else \ goto raise_overflow; \ } \ } \ return (target_type) value; \ } #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS #include "longintrepr.h" #endif #endif static CYTHON_INLINE Py_intptr_t __Pyx_PyInt_As_Py_intptr_t(PyObject *x) { const Py_intptr_t neg_one = (Py_intptr_t) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(Py_intptr_t) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(Py_intptr_t, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (Py_intptr_t) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(x)) { case 0: return 0; case 1: __PYX_VERIFY_RETURN_INT(Py_intptr_t, digit, ((PyLongObject*)x)->ob_digit[0]); } #endif #endif if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } if (sizeof(Py_intptr_t) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT(Py_intptr_t, unsigned long, PyLong_AsUnsignedLong(x)) } else if (sizeof(Py_intptr_t) <= sizeof(unsigned long long)) { __PYX_VERIFY_RETURN_INT(Py_intptr_t, unsigned long long, PyLong_AsUnsignedLongLong(x)) } } else { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(x)) { case 0: return 0; case 1: __PYX_VERIFY_RETURN_INT(Py_intptr_t, digit, +(((PyLongObject*)x)->ob_digit[0])); case -1: __PYX_VERIFY_RETURN_INT(Py_intptr_t, sdigit, -(sdigit) ((PyLongObject*)x)->ob_digit[0]); } #endif #endif if (sizeof(Py_intptr_t) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT(Py_intptr_t, long, PyLong_AsLong(x)) } else if (sizeof(Py_intptr_t) <= sizeof(long long)) { __PYX_VERIFY_RETURN_INT(Py_intptr_t, long long, PyLong_AsLongLong(x)) } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else Py_intptr_t val; PyObject *v = __Pyx_PyNumber_Int(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (Py_intptr_t) -1; } } else { Py_intptr_t val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (Py_intptr_t) -1; val = __Pyx_PyInt_As_Py_intptr_t(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to Py_intptr_t"); return (Py_intptr_t) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to Py_intptr_t"); return (Py_intptr_t) -1; } static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { PyObject *empty_list = 0; PyObject *module = 0; PyObject *global_dict = 0; PyObject *empty_dict = 0; PyObject *list; #if PY_VERSION_HEX < 0x03030000 PyObject *py_import; py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); if (!py_import) goto bad; #endif if (from_list) list = from_list; else { empty_list = PyList_New(0); if (!empty_list) goto bad; list = empty_list; } global_dict = PyModule_GetDict(__pyx_m); if (!global_dict) goto bad; empty_dict = PyDict_New(); if (!empty_dict) goto bad; { #if PY_MAJOR_VERSION >= 3 if (level == -1) { if (strchr(__Pyx_MODULE_NAME, '.')) { #if PY_VERSION_HEX < 0x03030000 PyObject *py_level = PyInt_FromLong(1); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, NULL); Py_DECREF(py_level); #else module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, 1); #endif if (!module) { if (!PyErr_ExceptionMatches(PyExc_ImportError)) goto bad; PyErr_Clear(); } } level = 0; } #endif if (!module) { #if PY_VERSION_HEX < 0x03030000 PyObject *py_level = PyInt_FromLong(level); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, NULL); Py_DECREF(py_level); #else module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, level); #endif } } bad: #if PY_VERSION_HEX < 0x03030000 Py_XDECREF(py_import); #endif Py_XDECREF(empty_list); Py_XDECREF(empty_dict); return module; } #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); return -1; } static void __Pyx_ReleaseBuffer(Py_buffer *view) { PyObject *obj = view->obj; if (!obj) return; if (PyObject_CheckBuffer(obj)) { PyBuffer_Release(view); return; } if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } Py_DECREF(obj); view->obj = NULL; } #endif static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { const int neg_one = (int) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(int) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (int) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(x)) { case 0: return 0; case 1: __PYX_VERIFY_RETURN_INT(int, digit, ((PyLongObject*)x)->ob_digit[0]); } #endif #endif if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } if (sizeof(int) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT(int, unsigned long, PyLong_AsUnsignedLong(x)) } else if (sizeof(int) <= sizeof(unsigned long long)) { __PYX_VERIFY_RETURN_INT(int, unsigned long long, PyLong_AsUnsignedLongLong(x)) } } else { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(x)) { case 0: return 0; case 1: __PYX_VERIFY_RETURN_INT(int, digit, +(((PyLongObject*)x)->ob_digit[0])); case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, -(sdigit) ((PyLongObject*)x)->ob_digit[0]); } #endif #endif if (sizeof(int) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT(int, long, PyLong_AsLong(x)) } else if (sizeof(int) <= sizeof(long long)) { __PYX_VERIFY_RETURN_INT(int, long long, PyLong_AsLongLong(x)) } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else int val; PyObject *v = __Pyx_PyNumber_Int(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (int) -1; } } else { int val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (int) -1; val = __Pyx_PyInt_As_int(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to int"); return (int) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to int"); return (int) -1; } static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { const int neg_one = (int) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(int) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(int) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); } else if (sizeof(int) <= sizeof(unsigned long long)) { return PyLong_FromUnsignedLongLong((unsigned long long) value); } } else { if (sizeof(int) <= sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(int) <= sizeof(long long)) { return PyLong_FromLongLong((long long) value); } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(int), little, !is_unsigned); } } static CYTHON_INLINE npy_uint32 __Pyx_PyInt_As_npy_uint32(PyObject *x) { const npy_uint32 neg_one = (npy_uint32) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(npy_uint32) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(npy_uint32, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (npy_uint32) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(x)) { case 0: return 0; case 1: __PYX_VERIFY_RETURN_INT(npy_uint32, digit, ((PyLongObject*)x)->ob_digit[0]); } #endif #endif if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } if (sizeof(npy_uint32) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT(npy_uint32, unsigned long, PyLong_AsUnsignedLong(x)) } else if (sizeof(npy_uint32) <= sizeof(unsigned long long)) { __PYX_VERIFY_RETURN_INT(npy_uint32, unsigned long long, PyLong_AsUnsignedLongLong(x)) } } else { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(x)) { case 0: return 0; case 1: __PYX_VERIFY_RETURN_INT(npy_uint32, digit, +(((PyLongObject*)x)->ob_digit[0])); case -1: __PYX_VERIFY_RETURN_INT(npy_uint32, sdigit, -(sdigit) ((PyLongObject*)x)->ob_digit[0]); } #endif #endif if (sizeof(npy_uint32) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT(npy_uint32, long, PyLong_AsLong(x)) } else if (sizeof(npy_uint32) <= sizeof(long long)) { __PYX_VERIFY_RETURN_INT(npy_uint32, long long, PyLong_AsLongLong(x)) } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else npy_uint32 val; PyObject *v = __Pyx_PyNumber_Int(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (npy_uint32) -1; } } else { npy_uint32 val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (npy_uint32) -1; val = __Pyx_PyInt_As_npy_uint32(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to npy_uint32"); return (npy_uint32) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to npy_uint32"); return (npy_uint32) -1; } static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { const long neg_one = (long) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(long) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(long) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); } else if (sizeof(long) <= sizeof(unsigned long long)) { return PyLong_FromUnsignedLongLong((unsigned long long) value); } } else { if (sizeof(long) <= sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(long) <= sizeof(long long)) { return PyLong_FromLongLong((long long) value); } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(long), little, !is_unsigned); } } static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_int32(npy_int32 value) { const npy_int32 neg_one = (npy_int32) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(npy_int32) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(npy_int32) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); } else if (sizeof(npy_int32) <= sizeof(unsigned long long)) { return PyLong_FromUnsignedLongLong((unsigned long long) value); } } else { if (sizeof(npy_int32) <= sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(npy_int32) <= sizeof(long long)) { return PyLong_FromLongLong((long long) value); } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(npy_int32), little, !is_unsigned); } } static CYTHON_INLINE npy_int32 __Pyx_PyInt_As_npy_int32(PyObject *x) { const npy_int32 neg_one = (npy_int32) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(npy_int32) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(npy_int32, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (npy_int32) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(x)) { case 0: return 0; case 1: __PYX_VERIFY_RETURN_INT(npy_int32, digit, ((PyLongObject*)x)->ob_digit[0]); } #endif #endif if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } if (sizeof(npy_int32) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, PyLong_AsUnsignedLong(x)) } else if (sizeof(npy_int32) <= sizeof(unsigned long long)) { __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long long, PyLong_AsUnsignedLongLong(x)) } } else { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(x)) { case 0: return 0; case 1: __PYX_VERIFY_RETURN_INT(npy_int32, digit, +(((PyLongObject*)x)->ob_digit[0])); case -1: __PYX_VERIFY_RETURN_INT(npy_int32, sdigit, -(sdigit) ((PyLongObject*)x)->ob_digit[0]); } #endif #endif if (sizeof(npy_int32) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT(npy_int32, long, PyLong_AsLong(x)) } else if (sizeof(npy_int32) <= sizeof(long long)) { __PYX_VERIFY_RETURN_INT(npy_int32, long long, PyLong_AsLongLong(x)) } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else npy_int32 val; PyObject *v = __Pyx_PyNumber_Int(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (npy_int32) -1; } } else { npy_int32 val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (npy_int32) -1; val = __Pyx_PyInt_As_npy_int32(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to npy_int32"); return (npy_int32) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to npy_int32"); return (npy_int32) -1; } static CYTHON_INLINE long __Pyx_pow_long(long b, long e) { long t = b; switch (e) { case 3: t *= b; case 2: t *= b; case 1: return t; case 0: return 1; } #if 1 if (unlikely(e<0)) return 0; #endif t = 1; while (likely(e)) { t *= (b * (e&1)) | ((~e)&1); /* 1 or b */ b *= b; e >>= 1; } return t; } #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return ::std::complex< float >(x, y); } #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return x + y*(__pyx_t_float_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { __pyx_t_float_complex z; z.real = x; z.imag = y; return z; } #endif #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float denom = b.real * b.real + b.imag * b.imag; z.real = (a.real * b.real + a.imag * b.imag) / denom; z.imag = (a.imag * b.real - a.real * b.imag) / denom; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrtf(z.real*z.real + z.imag*z.imag); #else return hypotf(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { float denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(a, a); case 3: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(z, a); case 4: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } r = a.real; theta = 0; } else { r = __Pyx_c_absf(a); theta = atan2f(a.imag, a.real); } lnr = logf(r); z_r = expf(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cosf(z_theta); z.imag = z_r * sinf(z_theta); return z; } #endif #endif #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return ::std::complex< double >(x, y); } #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return x + y*(__pyx_t_double_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { __pyx_t_double_complex z; z.real = x; z.imag = y; return z; } #endif #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double denom = b.real * b.real + b.imag * b.imag; z.real = (a.real * b.real + a.imag * b.imag) / denom; z.imag = (a.imag * b.real - a.real * b.imag) / denom; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrt(z.real*z.real + z.imag*z.imag); #else return hypot(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { double denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(a, a); case 3: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(z, a); case 4: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } r = a.real; theta = 0; } else { r = __Pyx_c_abs(a); theta = atan2(a.imag, a.real); } lnr = log(r); z_r = exp(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cos(z_theta); z.imag = z_r * sin(z_theta); return z; } #endif #endif static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { const long neg_one = (long) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(long) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (long) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(x)) { case 0: return 0; case 1: __PYX_VERIFY_RETURN_INT(long, digit, ((PyLongObject*)x)->ob_digit[0]); } #endif #endif if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } if (sizeof(long) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT(long, unsigned long, PyLong_AsUnsignedLong(x)) } else if (sizeof(long) <= sizeof(unsigned long long)) { __PYX_VERIFY_RETURN_INT(long, unsigned long long, PyLong_AsUnsignedLongLong(x)) } } else { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(x)) { case 0: return 0; case 1: __PYX_VERIFY_RETURN_INT(long, digit, +(((PyLongObject*)x)->ob_digit[0])); case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, -(sdigit) ((PyLongObject*)x)->ob_digit[0]); } #endif #endif if (sizeof(long) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT(long, long, PyLong_AsLong(x)) } else if (sizeof(long) <= sizeof(long long)) { __PYX_VERIFY_RETURN_INT(long, long long, PyLong_AsLongLong(x)) } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else long val; PyObject *v = __Pyx_PyNumber_Int(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (long) -1; } } else { long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (long) -1; val = __Pyx_PyInt_As_long(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to long"); return (long) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long) -1; } static int __Pyx_check_binary_version(void) { char ctversion[4], rtversion[4]; PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { char message[200]; PyOS_snprintf(message, sizeof(message), "compiletime version %s of module '%.100s' " "does not match runtime version %s", ctversion, __Pyx_MODULE_NAME, rtversion); return PyErr_WarnEx(NULL, message, 1); } return 0; } #ifndef __PYX_HAVE_RT_ImportModule #define __PYX_HAVE_RT_ImportModule static PyObject *__Pyx_ImportModule(const char *name) { PyObject *py_name = 0; PyObject *py_module = 0; py_name = __Pyx_PyIdentifier_FromString(name); if (!py_name) goto bad; py_module = PyImport_Import(py_name); Py_DECREF(py_name); return py_module; bad: Py_XDECREF(py_name); return 0; } #endif #ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict) { PyObject *py_module = 0; PyObject *result = 0; PyObject *py_name = 0; char warning[200]; Py_ssize_t basicsize; #ifdef Py_LIMITED_API PyObject *py_basicsize; #endif py_module = __Pyx_ImportModule(module_name); if (!py_module) goto bad; py_name = __Pyx_PyIdentifier_FromString(class_name); if (!py_name) goto bad; result = PyObject_GetAttr(py_module, py_name); Py_DECREF(py_name); py_name = 0; Py_DECREF(py_module); py_module = 0; if (!result) goto bad; if (!PyType_Check(result)) { PyErr_Format(PyExc_TypeError, "%.200s.%.200s is not a type object", module_name, class_name); goto bad; } #ifndef Py_LIMITED_API basicsize = ((PyTypeObject *)result)->tp_basicsize; #else py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); if (!py_basicsize) goto bad; basicsize = PyLong_AsSsize_t(py_basicsize); Py_DECREF(py_basicsize); py_basicsize = 0; if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) goto bad; #endif if (!strict && (size_t)basicsize > size) { PyOS_snprintf(warning, sizeof(warning), "%s.%s size changed, may indicate binary incompatibility", module_name, class_name); if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; } else if ((size_t)basicsize != size) { PyErr_Format(PyExc_ValueError, "%.200s.%.200s has the wrong size, try recompiling", module_name, class_name); goto bad; } return (PyTypeObject *)result; bad: Py_XDECREF(py_module); Py_XDECREF(result); return NULL; } #endif static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { while (t->p) { #if PY_MAJOR_VERSION < 3 if (t->is_unicode) { *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); } else if (t->intern) { *t->p = PyString_InternFromString(t->s); } else { *t->p = PyString_FromStringAndSize(t->s, t->n - 1); } #else if (t->is_unicode | t->is_str) { if (t->intern) { *t->p = PyUnicode_InternFromString(t->s); } else if (t->encoding) { *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); } else { *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); } } else { *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); } #endif if (!*t->p) return -1; ++t; } return 0; } static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); } static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) { Py_ssize_t ignore; return __Pyx_PyObject_AsStringAndSize(o, &ignore); } static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT if ( #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII __Pyx_sys_getdefaultencoding_not_ascii && #endif PyUnicode_Check(o)) { #if PY_VERSION_HEX < 0x03030000 char* defenc_c; PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); if (!defenc) return NULL; defenc_c = PyBytes_AS_STRING(defenc); #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII { char* end = defenc_c + PyBytes_GET_SIZE(defenc); char* c; for (c = defenc_c; c < end; c++) { if ((unsigned char) (*c) >= 128) { PyUnicode_AsASCIIString(o); return NULL; } } } #endif *length = PyBytes_GET_SIZE(defenc); return defenc_c; #else if (__Pyx_PyUnicode_READY(o) == -1) return NULL; #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII if (PyUnicode_IS_ASCII(o)) { *length = PyUnicode_GET_LENGTH(o); return PyUnicode_AsUTF8(o); } else { PyUnicode_AsASCIIString(o); return NULL; } #else return PyUnicode_AsUTF8AndSize(o, length); #endif #endif } else #endif #if !CYTHON_COMPILING_IN_PYPY if (PyByteArray_Check(o)) { *length = PyByteArray_GET_SIZE(o); return PyByteArray_AS_STRING(o); } else #endif { char* result; int r = PyBytes_AsStringAndSize(o, &result, length); if (unlikely(r < 0)) { return NULL; } else { return result; } } } static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { int is_true = x == Py_True; if (is_true | (x == Py_False) | (x == Py_None)) return is_true; else return PyObject_IsTrue(x); } static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { PyNumberMethods *m; const char *name = NULL; PyObject *res = NULL; #if PY_MAJOR_VERSION < 3 if (PyInt_Check(x) || PyLong_Check(x)) #else if (PyLong_Check(x)) #endif return Py_INCREF(x), x; m = Py_TYPE(x)->tp_as_number; #if PY_MAJOR_VERSION < 3 if (m && m->nb_int) { name = "int"; res = PyNumber_Int(x); } else if (m && m->nb_long) { name = "long"; res = PyNumber_Long(x); } #else if (m && m->nb_int) { name = "int"; res = PyNumber_Long(x); } #endif if (res) { #if PY_MAJOR_VERSION < 3 if (!PyInt_Check(res) && !PyLong_Check(res)) { #else if (!PyLong_Check(res)) { #endif PyErr_Format(PyExc_TypeError, "__%.4s__ returned non-%.4s (type %.200s)", name, name, Py_TYPE(res)->tp_name); Py_DECREF(res); return NULL; } } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_TypeError, "an integer is required"); } return res; } static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { Py_ssize_t ival; PyObject *x; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_CheckExact(b))) return PyInt_AS_LONG(b); #endif if (likely(PyLong_CheckExact(b))) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(b)) { case -1: return -(sdigit)((PyLongObject*)b)->ob_digit[0]; case 0: return 0; case 1: return ((PyLongObject*)b)->ob_digit[0]; } #endif #endif return PyLong_AsSsize_t(b); } x = PyNumber_Index(b); if (!x) return -1; ival = PyInt_AsSsize_t(x); Py_DECREF(x); return ival; } static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { return PyInt_FromSize_t(ival); } #endif /* Py_PYTHON_H */