lr = LinearRegression(X, y, tolerance=1e-4)
lr.newton_general()
print('LinearRegression, theta of newton_general: ', lr.theta.T, ', num of iterations: ', len(lr.loss_history))
show_result(lr, "Method: newton_general")
# invoke newton with Armijo search method
lr = LinearRegression(X, y, tolerance=1e-4)
lr.newton_armijo()
print('LinearRegression, theta of newton_armijo: ', lr.theta.T, ', num of iterations: ', len(lr.loss_history))
show_result(lr, "Method: newton_armijo")
# local weight linear regression
taus = np.linspace(1, 0, 5, False)
for tau in np.nditer(taus):
_, y_estimate = lr.fit_local_weight_lr(X, tau)
plt.title("local weight linear regression with tau=" + str(tau))
plt.plot(lr.features[:, :-1], lr.labels, 'bo')
plt.plot(X, y_estimate, 'r-')
plt.show()
# Test fot Logistic Regression
# load data
data = pd.read_csv("./apple_juice.dat", header=None, sep=r"\s+")
data = data.as_matrix()
X = data[:, 0: -1]
y = data[:, -1]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
# create Logistic Regression model
lr = LogisticRegression(X_train, y_train, tolerance=1e-5)
