X_all = np.matrix(np.zeros([d, 2]))
X_all[:, 0] = np.matrix(x0[:, i]).T
X_all[:, 1] = np.matrix(x1[:, i]).T
Y_all[:, i] = clf.predict(X_all)
plt.contour(np.array(x0),
np.array(x1),
np.array(Y_all),
levels=[0.0],
colors='red')
if __name__ == '__main__':
# set rbf_kernel.gamma = 0.1
X_1, Y_1 = read_dense_data(open('data/sample_data_1.txt'))
X_2, Y_2 = read_dense_data(open('data/sample_data_2.txt'))
X_3, Y_3 = read_dense_data(open('data/sample_data_3.txt'))
X_1 = np.matrix(X_1)
X_2 = np.matrix(X_2)
X_3 = np.matrix(X_3)
Y_1 = np.matrix(map_label(Y_1)).T
Y_2 = np.matrix(map_label(Y_2)).T
Y_3 = np.matrix(map_label(Y_3)).T
Y_1[np.where(Y_1 == 0)] = -1
Y_2[np.where(Y_2 == 0)] = -1
Y_3[np.where(Y_3 == 0)] = -1
[x0, x1] = plt.meshgrid(x0_plot, x1_plot);
Y_all = np.matrix(np.zeros([d, d]))
for i in range(d):
X_all = np.matrix(np.zeros([d, 2]))
X_all[:, 0] = np.matrix(x0[:, i]).T
X_all[:, 1] = np.matrix(x1[:, i]).T
Y_all[:, i] = clf.predict(X_all)
plt.contour(np.array(x0), np.array(x1), np.array(Y_all), levels = [0.0], colors = 'red')
if __name__ == '__main__':
# set rbf_kernel.gamma = 0.1
X_1, Y_1 = read_dense_data(open('data/sample_data_1.txt'))
X_2, Y_2 = read_dense_data(open('data/sample_data_2.txt'))
X_3, Y_3 = read_dense_data(open('data/sample_data_3.txt'))
X_1 = np.matrix(X_1)
X_2 = np.matrix(X_2)
X_3 = np.matrix(X_3)
Y_1 = np.matrix(map_label(Y_1)).T
Y_2 = np.matrix(map_label(Y_2)).T
Y_3 = np.matrix(map_label(Y_3)).T
Y_1[np.where(Y_1 == 0)] = -1
Y_2[np.where(Y_2 == 0)] = -1
Y_3[np.where(Y_3 == 0)] = -1
m = len(X)
D = (X * w - Y)
fx = float((1.0 / (2 * m)) * (D.T * D) + (lamb / 2.0) * (w.T * w))
df = (1.0 / m) * X.T * (X * w - Y) + 1.0 * lamb * w
self.c += 1
return fx, df
if __name__ == '__main__':
train_path = 'data/housing.train'
test_path = 'data/housing.test'
X_train, Y_train = read_dense_data(open(train_path))
X_test, Y_test = read_dense_data(open(test_path))
X_train = np.matrix(X_train)
Y_train = [float(y) for y in Y_train]
Y_train = np.matrix(Y_train).T
X_test = np.matrix(X_test)
Y_test = [float(y) for y in Y_test]
Y_test = np.matrix(Y_test).T
reg = LinearRegression()
reg.train(X_train, Y_train)
reg.test(X_train, Y_train)
reg.test(X_test, Y_test)
err = P.sum()
if update % 100 == 0:
print >>sys.stderr, "Update : %d\ttraining loss: %lf" % (update, 1.0 * err / len(Y))
if err < min_err:
min_err = err
self.w = w_test
if __name__ == "__main__":
train_path = "data/heart_scale.train"
test_path = "data/heart_scale.test"
X_train, Y_train = read_dense_data(open(train_path))
X_test, Y_test = read_dense_data(open(test_path))
X_train = np.matrix(X_train)
Y_train = [int(y) for y in Y_train]
Y_train = np.matrix(Y_train).T
X_test = np.matrix(X_test)
Y_test = [int(y) for y in Y_test]
Y_test = np.matrix(Y_test).T
clf = Perceptron()
clf.train(X_train, Y_train)
acc_train = clf.test(X_train, Y_train)
acc_test = clf.test(X_test, Y_test)
