Python实现的逻辑回归算法示例【附测试csv文件下载】_Python

Python实现的逻辑回归算法示例【附测试csv文件下载】

2021-05-09 00:18njulpy Python

这篇文章主要介绍了Python实现的逻辑回归算法,结合具体实例形式分析了Python逻辑回归算法相关实现技巧,需要的朋友可以参考下

本文实例讲述了python实现的逻辑回归算法。分享给大家供大家参考，具体如下：

使用python实现逻辑回归
using python to implement logistic regression algorithm

菜鸟写的逻辑回归，记录一下学习过程

代码：

				?

									#encoding:utf-8

									"""

									 author:  njulpy

									 version:  1.0

									 data:  2018/04/10

									 project： using python to implement logisticregression algorithm

									"""

									import numpy as np

									import pandas as pd

									import matplotlib.pyplot as plt

									from sklearn.model_selection import train_test_split

									#建立sigmoid函数

									def sigmoid(x):

									 x = x.astype(float)

									 return 1./(1+np.exp(-x))

									#训练模型，采用梯度下降算法

									def train(x_train,y_train,num,alpha,m,n):

									 beta = np.ones(n)

									 for i in range(num):

									  h=sigmoid(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=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

									def predict(x_test,beta):

									 y_predict=np.zeros(len(y_test))+0.5

									 s=sigmoid(np.dot(beta,x_test.t))

									 y_predict[s < 0.34] = 0

									 y_predict[s > 0.67] = 1

									 return y_predict

									def accurancy(y_predict,y_test):

									 acc=1-np.sum(np.absolute(y_predict-y_test))/len(y_test)

									 return acc

									if __name__ == "__main__":

									 data = pd.read_csv('iris.csv')

									 x = data.iloc[:,1:5]

									 y = data.iloc[:,5].copy()

									 y.loc[y== 'setosa'] = 0

									 y.loc[y== 'versicolor'] = 0.5

									 y.loc[y== 'virginica'] = 1

									 x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3,random_state=15)

									 m,n=np.shape(x_train)

									 alpha = 0.01

									 beta=train(x_train,y_train,1000,alpha,m,n)

									 pre=predict(x_test,beta)

									 t = np.arange(len(x_test))

									 plt.figure()

									 p1 = plt.plot(t,pre)

									 p2 = plt.plot(t,y_test,label='test')

									 label = ['prediction', 'true']

									 plt.legend(label, loc=1)

									 plt.show()

									 acc=accurancy(pre,y_test)

									 print('the predicted value is ',pre)

									 print('the true value is ',np.array(y_test))

									 print('the accuracy rate is ',acc)

输出结果：

the predicted value is [ 0.   0.5 1.   0.   0.   1.   1.   0.5 1.   1.   1.   0.5 0.5 0.5 1.
0.   0.5 1.   0.   1.   0.5 0.   0.5 0.5 0.   0.   1.   1.   1.   1.
0.   1.   1.   1.   0.   0.   1.   0.   0.   0.5 1.   0.   0.   0.5 1. ]
the true value is [0 0.5 0.5 0 0 0.5 1 0.5 0.5 1 1 0.5 0.5 0.5 1 0 0.5 1 0 1 0.5 0 0.5 0.5 0
0 1 1 1 0.5 0 1 0.5 1 0 0 1 0 0 0.5 1 0 0 0.5 1]
the accuracy rate is 0.9444444444444444

Python实现的逻辑回归算法示例【附测试csv文件下载】