梯度下降算法进行线性回归

原理和思路都很简单，不多说，上代码。

import numpy as np


class GradientDescent(object):
    def __init__(self, data_file, learning_rate=0.0001, iterations=1000):
        self.learning_rate = learning_rate
        self.points = np.genfromtxt(data_file, delimiter=',')
        self.loss = 0
        self.b = 0
        self.w = 0
        self.iterations = iterations
        self.main()

    def main(self):
        self.compute_loss()
        print('Starting gradient descent at b = {}, w = {} with the initial loss = {}'
              .format(self.b, self.w, self.loss))
        self.gradient_descent()
        self.compute_loss()
        print('After {} iterations, ending gradient descent at b = {}, w = {} with the final loss = {}'
              .format(self.iterations, self.b, self.w, self.loss))

    def compute_loss(self):
        self.loss = 0
        for point in self.points:
            x, y = point[0], point[1]
            self.loss += (y - (self.w * x + self.b)) ** 2
        self.loss /= len(self.points)

    def gradient_descent(self):
        print('Running...')
        for iteration in range(self.iterations):
            b_gradient = 0
            w_gradient = 0
            n = len(self.points)
            for point in self.points:
                x, y = point[0], point[1]
                b_gradient += -(2 / n) * (y - ((self.w * x) + self.b))
                w_gradient += -(2 / n) * x * (y - ((self.w * x) + self.b))
            self.w -= self.learning_rate * w_gradient
            self.b -= self.learning_rate * b_gradient


if __name__ == '__main__':
    gd = GradientDescent('data.csv')