from sklearn.svm import SVC import data_util as util bc_data_train, bc_data_test, bc_target_train, bc_target_test = util.load_breast_cancer() v_data_train, v_data_test, v_target_train, v_target_test = util.load_vowel() KERNELS = ["linear", "rbf", "rbf", "rbf", "rbf", "rbf", "rbf", "poly", "poly", "poly", "poly"] GAMMA = [.001, .01, .5, 1., 1.25, 1.5] def svm(): print "---bc---" d = 2 g = 0 for kernel in KERNELS: if kernel == "poly": _svm(bc_data_train, bc_data_test, bc_target_train, bc_target_test, kernel, degree=d) d += 1 elif kernel == "rbf": _svm(bc_data_train, bc_data_test, bc_target_train, bc_target_test, kernel, gamma=GAMMA[g]) g += 1 else: _svm(bc_data_train, bc_data_test, bc_target_train, bc_target_test, kernel) print "---v---" d = 2 g = 0 for kernel in KERNELS: if kernel == "poly": _svm(v_data_train, v_data_test, v_target_train, v_target_test, kernel, degree=d) d += 1
compressor.fit(tx, y=ty.ravel())
newtx = compressor.transform(tx)
runtime=time() - t0
shape = newtx.shape
print runtime, shape
print "K-best done"
def clustering(tx, ty, rx, ry, num=2):
print "KM"
for j in range(tx[1].size - 1):
i = j + 1
clf = KM(n_components=i)
clf.fit(tx)
print "KM done"
print "EM"
for j in range(tx[1].size - 1):
i = j + 1
clf = EM(n_components=i)
clf.fit(tx)
print "EM done"
if __name__ == "__main__":
print "----BC-----"
tx, ty, rx, ry = util.load_breast_cancer()
dimensional(tx, ty, rx, ry, add=chi2)
print "----V-----"
tx, ty, rx, ry = util.load_vowel()
dimensional(tx, ty, rx, ry, add=f_classif)
import data_util as util import numpy as np print "---bc--" bc = util.load_breast_cancer() print "train", len(bc[2]), np.bincount(bc[2]) print "test", len(bc[3]), np.bincount(bc[3]) print "---v--" vowels = util.load_vowel() print "train", len(vowels[2]), np.bincount(vowels[2]) print "test", len(vowels[3]), np.bincount(vowels[3])
print result
resultst.append(result[1])
resultsr.append(result[2])
plot([0, iterations, 0, 1], (iter_arr, resultst, "ro", iter_arr, resultsr, "bo"), "Network Epoch", "Percent Error", "Neural Network Error", "NN"+add)
print "nn done" + add
def _nn(tx, ty, rx, ry, n_iter):
print "_nn"
nn = Classifier(
layers=[
Layer("Tanh", units=100),
Layer("Softmax")],
n_iter=n_iter)
nn.fit(tx, ty)
resultst = nn.score(tx, ty)
resultsr = nn.score(rx, ry)
print "_nn done"
return n_iter, resultst, resultsr
if __name__=="__main__":
train_x, train_y, test_x, test_y = util.load_vowel()
# nn(train_x, train_y, test_x, test_y)
em(train_x, train_y, test_x, test_y)
# km(train_x, train_y, test_x, test_y)
# pca(train_x, train_y, test_x, test_y)
# ica(train_x, train_y, test_x, test_y)
# randproj(train_x, train_y, test_x, test_y)
# kbest(train_x, train_y, test_x, test_y)