Python实现的线性回归算法示例【附csv文件下载】

本文实例讲述了Python实现的线性回归算法。分享给大家供大家参考，具体如下：用python实现

本文实例讲述了Python实现的线性回归算法。分享给大家供大家参考，具体如下：

用python实现线性回归

Using Python to Implement Line Regression Algorithm

小菜鸟记录学习过程

代码：

#encoding:utf-8
"""
 Author: njulpy
 Version: 1.0
 Data: 2018/04/09
 Project： Using Python to Implement LineRegression Algorithm
"""
import numpy as np
import pandas as pd
from numpy.linalg import inv
from numpy import dot
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from sklearn import linear_model
# 最小二乘法
def lms(x_train,y_train,x_test):
 theta_n = dot(dot(inv(dot(x_train.T, x_train)), x_train.T), y_train) # theta = (X'X)^(-1)X'Y
 #print(theta_n)
 y_pre = dot(x_test,theta_n)
 mse = np.average((y_test-y_pre)**2)
 #print(len(y_pre))
 #print(mse)
 return theta_n,y_pre,mse
#梯度下降算法
def train(x_train, y_train, num, alpha,m, n):
 beta = np.ones(n)
 for i in range(num):
 h = np.dot(x_train, beta) # 计算预测值
 error = h - y_train.T # 计算预测值与训练集的差值
 delt = 2*alpha * np.dot(error, x_train)/m # 计算参数的梯度变化值
 beta = beta - delt
 #print('error', error)
 return beta
if __name__ == "__main__":
 iris = pd.read_csv('iris.csv')
 iris['Bias'] = float(1)
 x = iris[['Sepal.Width', 'Petal.Length', 'Petal.Width', 'Bias']]
 y = iris['Sepal.Length']
 x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=5)
 t = np.arange(len(x_test))
 m, n = np.shape(x_train)
 # Leastsquare
 theta_n, y_pre, mse = lms(x_train, y_train, x_test)
 # plt.plot(t, y_test, label='Test')
 # plt.plot(t, y_pre, label='Predict')
 # plt.show()
 # GradientDescent
 beta = train(x_train, y_train, 1000, 0.001, m, n)
 y_predict = np.dot(x_test, beta.T)
 # plt.plot(t, y_predict)
 # plt.plot(t, y_test)
 # plt.show()
 # sklearn
 regr = linear_model.LinearRegression()
 regr.fit(x_train, y_train)
 y_p = regr.predict(x_test)
 print(regr.coef_,theta_n,beta)
 l1,=plt.plot(t, y_predict)
 l2,=plt.plot(t, y_p)
 l3,=plt.plot(t, y_pre)
 l4,=plt.plot(t, y_test)
 plt.legend(handles=[l1, l2,l3,l4 ], labels=['GradientDescent', 'sklearn','Leastsquare','True'], loc='best')
 plt.show()

输出结果

sklearn: [ 0.65368836  0.70955523 -0.54193454  0.        ]  LeastSquare: [ 0.65368836  0.70955523 -0.54193454  1.84603897]  GradientDescent: [ 0.98359285  0.29325906  0.60084232  1.006859  ]

附：上述示例中的iris.csv文件点击此处本站下载。