65 lines
1.8 KiB
Python
65 lines
1.8 KiB
Python
import os
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import sys
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import re
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import random
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import numpy as np
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from sklearn.neighbors.kde import KernelDensity
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from pyviennacl.atidlas import FetchingPolicy
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def resample(X, draw):
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Xtuples = [tuple(x) for x in X]
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r = random.random()
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while(True):
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x = draw()
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if tuple(x) not in Xtuples:
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break
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return x.astype(int)
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def generate_dataset(TemplateType, execution_handler, nTuning, nDataPoints, compute_perf, draw):
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print "Getting some good profiles..."
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nDim = draw().size
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X = np.empty((nTuning, nDim))
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t = np.empty(nTuning)
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profiles = []
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for i in range(nTuning):
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x = resample(X, draw)
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y = execution_handler(x)
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if y not in profiles:
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profiles.append(y)
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idx = profiles.index(y)
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X[i,:] = x
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t[i] = idx
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print "Generating the dataset..."
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Y = np.empty((nDataPoints, len(profiles)))
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X = np.empty((nDataPoints, nDim))
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t = []
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for i in range(nDataPoints):
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x = resample(X, draw)
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for j,y in enumerate(profiles):
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T = execution_handler(x, os.devnull, y)
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Y[i,j] = compute_perf(x, T)
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idx = np.argmax(Y[i,:])
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X[i,:] = x
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t = np.argmax(Y[:i+1,], axis=1)
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if i%10==0:
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sys.stdout.write('%d data points generated\r'%i)
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sys.stdout.flush()
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template_name = TemplateType.__name__
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dir = os.path.join("data", template_name)
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if not os.path.exists(dir):
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os.makedirs(dir)
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np.savetxt(os.path.join(dir,"profiles.csv"), profiles)
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np.savetxt(os.path.join(dir,"X.csv"), X)
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np.savetxt(os.path.join(dir,"Y.csv"), Y)
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profiles = np.loadtxt(os.path.join(dir, "profiles.csv"))
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X = np.loadtxt(os.path.join(dir, "X.csv"),ndmin=2)
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Y = np.loadtxt(os.path.join(dir, "Y.csv"),ndmin=2)
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return X, Y, profiles
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