Tuner: Moved tuning logic into the python wrapper - draft of Android tuning app using kivy

python/isaac/autotuning/model.py

@@ -0,0 +1,49 @@
+from external.sklearn.forest import RandomForestRegressor
+import numpy as np
+
+def gmean(a, axis=0, dtype=None):
+    if not isinstance(a, np.ndarray):  # if not an ndarray object attempt to convert it
+        log_a = np.log(np.array(a, dtype=dtype))
+    elif dtype:  # Must change the default dtype allowing array type
+        if isinstance(a,np.ma.MaskedArray):
+            log_a = np.log(np.ma.asarray(a, dtype=dtype))
+        else:
+            log_a = np.log(np.asarray(a, dtype=dtype))
+    else:
+        log_a = np.log(a)
+    return np.exp(log_a.mean(axis=axis))
+
+def nrmse(y_ground, y):
+    N = y.size
+    rmsd = np.sqrt(np.sum((y_ground - y)**2)/N)
+    if len(y_ground) > 1:
+        return rmsd/(np.max(y_ground) - np.min(y_ground))
+    else:
+        return rmsd
+
+def train(X, Y, profiles):      
+    X = np.array(X)
+    Y = np.array(Y)
+    M = X.shape[0]
+
+    p = np.random.permutation(X.shape[0])
+    X = X[p,:]
+    Y = Y[p,:]   
+
+    #Train the.profile
+    cut = int(1.00*M)
+    CV = .1
+    XTr, YTr = X[:,:], Y[:,:]
+    XCv, YCv = X[:max(1,CV*M),:], Y[:max(1,CV*M),:]
+
+    nrmses = {}
+    for N in range(1,min(M+1,20)):
+        for depth in range(1,min(M+1,20)):
+            clf = RandomForestRegressor(N, max_depth=depth).fit(XTr, YTr)
+            t = np.argmax(clf.predict(XCv), axis = 1)
+            y = np.array([YCv[i,t[i]] for i in range(t.size)])
+            ground = np.max(YCv[:,:], axis=1)
+            nrmses[clf] = nrmse(ground, y)
+            
+    clf = min(nrmses, key=nrmses.get)
+    return clf, nrmses[clf]