Python实现的逻辑回归算法示例【附测试csv文件下载】

Python实现的逻辑回归算法示例【附测试实现的逻辑回归算法示例【附测试csv文件下载】文件下载】

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

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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：

使用使用python实现逻辑回归实现逻辑回归

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：代码：

"""

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

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):

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()

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.