Python实现的线性回归算法示例【附csv文件下载】

Python实现的线性回归算法示例【附实现的线性回归算法示例【附csv文件下载】文件下载】

主要介绍了Python实现的线性回归算法,涉及Python使用最小二乘法、梯度下降算法实现线性回归相关算法操作

与使用技巧,需要的朋友可以参考下

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：

用用python实现线性回归实现线性回归

Using Python to Implement Line Regression Algorithm

小菜鸟记录学习过程

代码：代码：

"""

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):

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)

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)

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()