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:
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#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)
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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
附:上述示例中的iris.csv文件点击此处本站下载。
希望本文所述对大家python程序设计有所帮助。
原文链接:https://blog.csdn.net/njulpy/article/details/79899352