迭代法写线性回归

标签：ram   imp   ati   learning   oss   err   desc   ota   put   

import numpy as np

def compute_error_points(b, w, points):
    total_error = 0
    for i in range(0, len(points)):
        x = points[i, 0]
        y = points[i, 1]
        # 计算均方误差
        total_error += (y - (w * x + b)) ** 2
    # 返回loss
    return total_error / float(len(points))

def step_gradient(b_current, w_current, points, learning_rate):
    b_gradient = 0
    w_gradient = 0
    N = float(len(points))
    for i in range(0, len(points)):
        x = points[i, 0]
        y = points[i, 1]
        # 求b的偏导
        b_gradient += (2/N) * ((w_current * x + b_current) - y)
        # 求w的偏导
        w_gradient += (2/N) * x * ((w_current * x + b_current) - y)

    new_b = b_current - (learning_rate * b_gradient)
    new_w = w_current - (learning_rate * w_gradient)

    # 返回迭代的b和w
    return [new_b, new_w]

def gradient_descent_runner(points, starting_b, starting_w, learning_rate, num_iterations):
    ‘‘‘
    :param points: data
    :param starting_b: 开始的b
    :param starting_w: 开始的w
    :param learning_rate: 迭代率
    :param num_iterations: 迭代次数
    :return:
    ‘‘‘
    b = starting_b
    w = starting_w
    # 迭代w和b
    for i in range(num_iterations):
        b, w = step_gradient(b, w, np.array(points), learning_rate)
    return [b, w]

points = np.random.random(size=(100, 2))
b, w = gradient_descent_runner(points, 0, 0, 0.0001, 1000)
print(b, w)

迭代法写线性回归

标签：ram   imp   ati   learning   oss   err   desc   ota   put   

原文地址：https://www.cnblogs.com/abc23/p/11020541.html