新手小白刚刚开始接触机器学习，写了一个线性回归的代码，但是跑出来的结果一直不对，黔驴技穷了，求大佬指点
class LinearRegression:
def read_data(self):
data = np.genfromtxt("data.csv", delimiter=',')
x_num = data.shape[1] - 1
data_num = data.shape[0]
bais = np.ones(data_num).reshape((data_num, 1))
x = data[:, 0 : x_num].reshape((data_num, x_num))
x = np.concatenate((bais, x), axis=1)
y = data[:, -1].reshape((data_num, 1))
return data, x, y
def generate_parameter(self, x_num):
parameter = np.zeros((x_num, 1))
return parameter
def compute_cost(self, x, y, parameter):
tmp = np.dot(x, parameter) - y
return 0.5 * float(np.dot(np.transpose(tmp), tmp))
def gradient_descent(self, x, y, parameter, lr):
tmp = np.dot(x, parameter) - y
gradient = np.dot(np.transpose(x), tmp)
parameter_updated = parameter - lr * gradient
return parameter_updated
def train_model(self, x, y, parameter, lr, iteration):
cost_list = []
para_tmp = parameter
for i in range(iteration):
cost_list.append(self.compute_cost(x, y, para_tmp))
para_tmp = self.gradient_descent(x, y, para_tmp, lr)
return para_tmp, cost_list
