Realize linear regression with python through jupyter notebook

1. Today Xiaoguan will talk about using python to achieve linear regression

2. Come on, show:

#python实现线性回归
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.csv保存为自己的本地路径
  iris = pd.read_csv('E:\新加卷（E）\python学习\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()

　　Share the download address of iris.csv: http://yy.jb51.net:81/201812/yuanma/iris.csv(jb51.net).rar

3. Print the result:

4. The code is a bit long. I hope to analyze it patiently. If you don’t understand, you can send me a private message. The contact information is below

I hope I can help everyone, I ask you if you want a like, will you give it, thank you all.
Copyright statement: The copyright of this article belongs to the author (@攻城狮小关) and CSDN. Welcome to reprint, but you must keep this paragraph without the author’s consent Statement, and provide the original link in an obvious place on the article page, otherwise the right to pursue legal responsibility is reserved.
It is not easy for everyone to write articles, please respect the fruits of labor~
Communication plus Q: 1909561302
Blog Park Address https://www.cnblogs.com/guanguan-com/

Realize linear regression with python through jupyter notebook

Guess you like