def plot_sample(nonreg=True, k = 0):
(X_train_raw, y_train, X_test_raw, y_test) = get_data()
X_train = transform(X_train_raw)
X_test = transform(X_test_raw)
N = X_train.shape[0]
if nonreg is True:
w = linear_regression(X_train, y_train)
else:
w = weight_decay_regression(X_train, y_train, 10.0**k)
def plot_decision_fn(X):
X_trans = transform(X)
return np.sign(np.dot(X_trans, w))
(cont_x, cont_y, cont_z) = decision_boundary_2D(-1, 1, 0.0025, -1, 1, 0.0025,
plot_decision_fn)
print("E_in :", linear_error(X_train, y_train, w))
print("E_out:", linear_error(X_test, y_test, w))
x_plot = X_test_raw[:,0]
y_plot = X_test_raw[:,1]
c = np.where(y_test==1, 'r', 'b')
plt.scatter(x_plot,y_plot, c=c)
plt.contour(cont_x, cont_y, cont_z, [0], colors='g')
plt.xlim([-1, 1])
plt.ylim([-1, 1])
plt.grid()
plt.show()
def caltech_classification_data():
"""NEED TO FIGURE OUT NN FOR CLASSIFICATION"""
(X_train_raw, y_train, X_test_raw, y_test) = get_data()
nn = NeuralNetwork([3, 4, 1])
def plot_decision_fn(X):
out = [compute_activations(x, nn)[-1] for x in X]
return out
(cont_x, cont_y, cont_z) = decision_boundary_2D(-1, 1, 0.0025, -1, 1, 0.0025,
plot_decision_fn)
x_plot = X_test_raw[:,0]
y_plot = X_test_raw[:,1]
c = np.where(y_test==1, 'r', 'b')
plt.scatter(x_plot,y_plot, c=c)
plt.contour(cont_x, cont_y, cont_z, [0], colors='g')
plt.xlim([-1, 1])
plt.ylim([-1, 1])
plt.grid()
plt.show()
