train_x = np.hstack((np.array(train_x), np.ones((len(train_x), 1))))
test_x = np.hstack((np.array(test_x), np.ones(((len(test_x), 1)))))
train_y = np.array(train_y).reshape(len(train_y))
test_y = np.array(test_y).reshape(len(test_y))
lr = LogisticRegression(learning_rate=0.00015,
initial_w=np.zeros(train_x.shape[1]))
# batch gradient descent
# history_loss, history_test_loss, history_score,_ = lr.train_gradient_descent(
# epoch=150000, epoch_per_round=10000, train_x=train_x, train_y=train_y, test_x=test_x, test_y=test_y)
# stochastic gradient descent
history_loss, history_test_loss, history_score, _ = lr.train_stochastic_gradient_descent(
iteration_num=500000,
iter_per_round=100,
batch_size=1,
train_x=train_x,
train_y=train_y,
test_x=test_x,
test_y=test_y)
print('Coefficient:', lr.w)
variable_x = range(100, 500001, 100)
Drawer().draw_score(variable_x, history_score, ifSGD=True)
Drawer().draw_loss(variable_x, history_loss, ifSGD=True)
Drawer().draw_loss(variable_x, history_test_loss, ifSGD=True)
# Error of stochastic gradient descent with different batch_size
all_train_loss: List[float] = []
all_test_loss: List[float] = []
all_train_score: List[float] = []
all_test_score: List[float] = []
for i in range(10, 401, 10):
