线性回归Python（梯度下降法）

import numpy as np
import matplotlib.pyplot as plt
data = np.genfromtxt("data.csv",delimiter = ",")
x_data = data[:,0]
y_data = data[:,1]
plt.scatter(x_data,y_data)
plt.show()


#学习率
lr = 0.0001
#截距
b = 0
#斜率
k = 0
#迭代次数
epochs = 50

#最小二乘法
def compute_error(b,k,x_data,y_data):
    totalError = 0
    for i in range(0, len(x_data)):
        totalError += (y_data[i] - (k *x_data[i] + b))**2
    return totalError/float(len(x_data))/2.0

def gradient_descent_runner(x_data, y_data, b, k, lr, epochs):
    # 计算总数据量
    m = float(len(x_data))
    # 循环epochs次
    for i in range(epochs):
        b_grad = 0
        k_grad = 0
        for j in range(0, len(x_data)):
            b_grad += (1/m) * (((k * x_data[j]) + b) - y_data[j])
            k_grad += (1/m) * x_data[j] * (((k * x_data[j]) + b) - y_data[j])
        # 更新b和k
        b = b - (lr * b_grad)
        k = k - (lr * k_grad)
        # 每迭代5次，输出一次图像
        if i % 5==0:
            print("epochs:",i)
            plt.plot(x_data, y_data, 'b.')
            plt.plot(x_data, k*x_data + b, 'r')
            plt.show()
    return b, k
print("Starting b = {0}, k = {1}, error = {2}".format(b, k, compute_error(b, k, x_data, y_data)))
print("Running...")
b, k = gradient_descent_runner(x_data, y_data, b, k, lr, epochs)
print("After {0} iterations b = {1}, k = {2}, error = {3}".format(epochs, b, k, compute_error(b, k, x_data, y_data)))