def main():
# Set parameters
degree = 20
eta = 2
max_iter = 200
# Load data and expand with polynomial features
f = open('data_logreg.json', 'r')
data = json.load(f)
for k, v in data.items():
data[k] = np.array(v) # Encode list into numpy array
# Expand with polynomial features
X_train = logreg_toolbox.poly_2D_design_matrix(data['x1_train'],
data['x2_train'], degree)
n = X_train.shape[1]
# Define the functions of the parameter we want to optimize
def f(theta):
return lr.cost(theta, X_train, data['y_train'])
def df(theta):
return lr.grad(theta, X_train, data['y_train'])
# Test to verify if the computation of the gradient is correct
logreg_toolbox.check_gradient(f, df, n)
# Point for initialization of gradient descent
theta0 = np.zeros(n)
theta_opt, E_list = gd.gradient_descent(f, df, theta0, eta, max_iter)
logreg_toolbox.plot_logreg(data, degree, theta_opt, E_list)
plt.show()
def main():
# Set parameters
degree = 5
eta = 1.
max_iter = 20
# Load data and expand with polynomial features
f = open('data_logreg.json', 'r')
data = json.load(f)
for k, v in data.items():
data[k] = np.array(v) # Encode list into numpy array
# Expand with polynomial features
X_train = logreg_toolbox.poly_2D_design_matrix(data['x1_train'],
data['x2_train'], degree)
n = X_train.shape[1]
# Define the functions of the parameter we want to optimize
def f(theta):
return lr.cost(theta, X_train, data['y_train'])
def df(theta):
return lr.grad(theta, X_train, data['y_train'])
# Test to verify if the computation of the gradient is correct
logreg_toolbox.check_gradient(f, df, n)
# Point for initialization of gradient descent
theta0 = np.zeros(n)
#### VARIANT 1: Optimize with gradient descent
# theta_opt, E_list = gd.gradient_descent(f, df, theta0, eta, max_iter)
#### VARIANT 2: Optimize with gradient descent
# theta_opt, E_list, lr_list = gd.adaptative_gradient_descent(f, df, theta0, eta, max_iter)
# plt.plot(lr_list)
# plt.xlabel('Iterations')
# plt.ylabel('Learning rate')
# print('Adaptative gradient, final learning rate: {:.3g}'.format(lr_list[-1]))
#### VARIANT 3: Optimize with gradient descent
res = minimize(f,
x0=theta0,
jac=df,
options={
'disp': True,
'maxiter': max_iter
})
theta_opt = res.x.reshape((n, 1))
E_list = []
logreg_toolbox.plot_logreg(data, degree, theta_opt, E_list)
plt.show()