纯python3.5实现多变量线性回归（附数据）

import numpy as np # 正则化 def regularize(x):#regularize by columns x_tmp = x.copy() for i in range(2, len(x[0])): current_x = x_tmp[:, i] x_avg = np.mean(current_x) x_var = current_x.max() - current_x.min() current_x = (current_x-x_avg)/(x_var) x_tmp[:, i] = current_x return x_tmp # 梯度下降函数 def compute_gradient(current_para, x, y, learning_rate): length = len(x) matrix_gradient = np.zeros(len(x[0])) m = len(y) for i in range(0, length): current_x = x[i] current_y = y[i] current_x = np.asarray(current_x) matrix_gradient += (np.dot(current_para, current_x)-current_y)*current_x para_new = current_para - learning_rate*matrix_gradient/m return para_new # 误差计算函数 def compute_error(para, x, y): total_error = 0 length = len(x) num = len(y) for i in range(0, length): total_error += (y - (np.dot(para, x[i]))) ** 2 total_error = np.sum(total_error, axis=0) return total_error / (2*num) # 参数优化过程，输出误差 def optimizer(initial_para, x, y, learning_rate, num_iter): para = initial_para for i in range(0, num_iter+1): para = compute_gradient(para, x, y, learning_rate) if i % 100 == 0: error = compute_error(para, x, y) print("iter:"+str(i)+" ; error:"+str(error)) return para def linear_regression(): data = np.loadtxt('linear_regression_multiple_variable.csv', delimiter='\t') # 特征数量 n = len(data[0]) # 样本数量 m = len(data) x = data[:, 0:n-1] y = data[:, n-1] matrix_one = np.ones(m) x = np.insert(x, 0, values=matrix_one, axis=1) x = regularize(x) initial_para = np.zeros(n) print("起始参数：") print(initial_para) learning_rate = 0.001 num_iter = 3400 para = optimizer(initial_para, x, y, learning_rate, num_iter) print("最后训练得到的参数：") print(para) linear_regression()