def calculate_alpha_s(self, weak_learner, s):
alpha = 0.0
new_s = s
data_num = len(weak_learner.train_Y)
X = []
for i in range(data_num):
predict_string = np.array(list(map(str, weak_learner.train_X[i])))
predict_string = ' '.join(predict_string[1:]) + ' ' + str(weak_learner.train_Y[i])
prediction = weak_learner.prediction(predict_string, 'test_data')
X.append([float(prediction['prediction'])])
X = np.array(X)
linear = linear_regression.LinearRegression()
linear.status = 'load_train_data'
linear.train_X = X
linear.train_Y = weak_learner.train_Y - s
linear.set_param()
linear.init_W()
linear.train()
alpha = linear.W[0]
new_s = s + alpha * np.ravel(X)
return alpha, new_s
def train(self):
predict_X = []
for x in self.models[0].train_X:
x_string = ' '.join(map(str, x.tolist()[1:]))
transform_x = self.feature_transform(x_string)
transform_x.insert(0, 1)
predict_X.append(transform_x)
self.linear = linear_regression.LinearRegression()
self.linear.load_train_data()
self.linear.load_test_data()
self.linear.train_X = np.array(predict_X)
self.linear.train_Y = self.models[0].train_Y
self.linear.set_param()
self.linear.init_W()
self.linear.train()