python实现梯度下降和逻辑回归

深藏阁楼爱情的钟 2023-01-03 11:28 131阅读 0赞

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本文实例为大家分享了python实现梯度下降和逻辑回归的具体代码，供大家参考，具体内容如下

import numpy as np
    import pandas as pd
    import os

data = pd.read\_csv(“iris.csv”) \# 这里的iris数据已做过处理  
m, n = data.shape  
dataMatIn = np.ones((m, n))  
dataMatIn\[:, :-1\] = data.ix\[:, :-1\]  
classLabels = data.ix\[:, -1\]

# sigmoid函数和初始化数据 #

def sigmoid(z):  
return 1 / (1 + np.exp(-z))

# 随机梯度下降 #

def Stocgrad\_descent(dataMatIn, classLabels):  
dataMatrix = np.mat(dataMatIn) \# 训练集  
labelMat = np.mat(classLabels).transpose() \# y值  
m, n = np.shape(dataMatrix) \# m:dataMatrix的行数,n:dataMatrix的列数  
weights = np.ones((n, 1)) \# 初始化回归系数（n, 1)  
alpha = 0.001 \# 步长  
maxCycle = 500 \# 最大循环次数  
epsilon = 0.001  
error = np.zeros((n,1))  
for i in range(maxCycle):  
for j in range(m):  
h = sigmoid(dataMatrix \* weights) \# sigmoid 函数  
weights = weights + alpha \* dataMatrix.transpose() \* (labelMat - h) \# 梯度  
if np.linalg.norm(weights - error) < epsilon:  
break  
else:  
error = weights  
return weights

# 逻辑回归 #

def pred\_result(dataMatIn):  
dataMatrix = np.mat(dataMatIn)  
r = Stocgrad\_descent(dataMatIn, classLabels)  
p = sigmoid(dataMatrix \* r) \# 根据模型预测的概率

# 预测结果二值化 #

pred = \[\]  
for i in range(len(data)):  
if p\[i\] > 0.5:  
pred.append(1)  
else:  
pred.append(0)  
data\[“pred”\] = pred  
os.remove(“data\_and\_pred.csv”) \# 删除List\_lost\_customers数据集 \# 第一次运行此代码时此步骤不要  
data.to\_csv(“data\_and\_pred.csv”, index=False, encoding=“utf\_8\_sig”) \# 数据集保存  
pred\_result(dataMatIn)

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持菜鸟教程www.piaodoo.com。

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