def test_learning_curve_poly(self):
highest_degree = 8
X_poly = map_poly_features(self.X, highest_degree)
X_poly, mu, sigma = feature_normalization(X_poly)
Xval_poly = map_poly_features(self.Xval, highest_degree)
Xval_poly = (Xval_poly - mu) / sigma
lamda = 0.0
error_train, error_val = learning_curve(X_poly, self.y, Xval_poly, self.yval, lamda)
plt.xlabel('Number of Training Examples')
plt.ylabel("Error")
plt.plot( range(1,self.m+1), error_train)
plt.plot( range(1,self.m+1), error_val)
plt.show()
def test_learning_curve_poly(self):
highest_degree = 8
X_poly = map_poly_features(self.X, highest_degree)
X_poly, mu, sigma = feature_normalization(X_poly)
Xval_poly = map_poly_features(self.Xval, highest_degree)
Xval_poly = (Xval_poly - mu) / sigma
lamda = 0.0
error_train, error_val = learning_curve(X_poly, self.y, Xval_poly,
self.yval, lamda)
plt.xlabel('Number of Training Examples')
plt.ylabel("Error")
plt.plot(range(1, self.m + 1), error_train)
plt.plot(range(1, self.m + 1), error_val)
plt.show()
def test_validation_curve_poly(self):
highest_degree = 8
X_poly = map_poly_features(self.X, highest_degree)
X_poly, mu, sigma = feature_normalization(X_poly)
Xval_poly = map_poly_features(self.Xval, highest_degree)
Xval_poly = (Xval_poly - mu) / sigma
lamdas = np.array([0,0.001,0.003,0.01,0.03,0.1,0.3,1,3,10])
error_train, error_val = validation_curve(X_poly, self.y, Xval_poly, self.yval, lamdas)
plt.xlabel('lamda')
plt.ylabel("Error")
plt.plot( lamdas, error_train, label='Train')
plt.plot( lamdas, error_val, label='Validation')
plt.legend()
plt.show()
def test_validation_curve_poly(self):
highest_degree = 8
X_poly = map_poly_features(self.X, highest_degree)
X_poly, mu, sigma = feature_normalization(X_poly)
Xval_poly = map_poly_features(self.Xval, highest_degree)
Xval_poly = (Xval_poly - mu) / sigma
lamdas = np.array([0, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1, 3, 10])
error_train, error_val = validation_curve(X_poly, self.y, Xval_poly,
self.yval, lamdas)
plt.xlabel('lamda')
plt.ylabel("Error")
plt.plot(lamdas, error_train, label='Train')
plt.plot(lamdas, error_val, label='Validation')
plt.legend()
plt.show()
def test_linear_regression_poly(self):
highest_degree = 8
X_poly = map_poly_features(self.X, highest_degree)
X_poly, mu, sigma = feature_normalization(X_poly)
initial_theta = np.ones(highest_degree+1)
lamda = 0.0
theta = train_linear_reg(initial_theta, X_poly, self.y, lamda)
hypo = predictions(theta, X_poly)
# now scatter the data and plot the hypothesis
df = pd.DataFrame(np.hstack(( self.X, hypo.reshape(self.y.shape), self.y )), columns=['X','hypo','y'])
df = df.sort('X')
plt.xlabel("Change in water level (x)")
plt.ylabel("Water flowing out of the dam (y)")
plt.scatter( df['X'], df['y'], marker='x', c='r', s=30, linewidth=2 )
plt.plot( df['X'], df['hypo'], linestyle='--', linewidth=3 )
plt.show()
def test_linear_regression_poly(self):
highest_degree = 8
X_poly = map_poly_features(self.X, highest_degree)
X_poly, mu, sigma = feature_normalization(X_poly)
initial_theta = np.ones(highest_degree + 1)
lamda = 0.0
theta = train_linear_reg(initial_theta, X_poly, self.y, lamda)
hypo = predictions(theta, X_poly)
# now scatter the data and plot the hypothesis
df = pd.DataFrame(np.hstack(
(self.X, hypo.reshape(self.y.shape), self.y)),
columns=['X', 'hypo', 'y'])
df = df.sort('X')
plt.xlabel("Change in water level (x)")
plt.ylabel("Water flowing out of the dam (y)")
plt.scatter(df['X'], df['y'], marker='x', c='r', s=30, linewidth=2)
plt.plot(df['X'], df['hypo'], linestyle='--', linewidth=3)
plt.show()
