194 lines
5.2 KiB
Python
194 lines
5.2 KiB
Python
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#!/usr/bin/env python3
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# -*- coding:UTF-8 -*-
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from sklearn.linear_model import LogisticRegression
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import numpy as np
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import random
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"""
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函数说明:sigmoid函数
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Parameters:
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inX - 数据
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Returns:
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sigmoid函数
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Author:
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Jack Cui
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Blog:
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http://blog.csdn.net/c406495762
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Zhihu:
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https://www.zhihu.com/people/Jack--Cui/
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Modify:
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2017-09-05
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"""
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def sigmoid(inX):
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return 1.0 / (1 + np.exp(-inX))
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"""
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函数说明:改进的随机梯度上升算法
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Parameters:
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dataMatrix - 数据数组
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classLabels - 数据标签
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numIter - 迭代次数
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Returns:
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weights - 求得的回归系数数组(最优参数)
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Author:
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Jack Cui
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Blog:
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http://blog.csdn.net/c406495762
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Zhihu:
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https://www.zhihu.com/people/Jack--Cui/
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Modify:
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2017-09-05
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"""
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def stocGradAscent1(dataMatrix, classLabels, numIter=150):
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m,n = np.shape(dataMatrix) #返回dataMatrix的大小。m为行数,n为列数。
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weights = np.ones(n) #参数初始化 #存储每次更新的回归系数
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for j in range(numIter):
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dataIndex = list(range(m))
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for i in range(m):
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alpha = 4/(1.0+j+i)+0.01 #降低alpha的大小，每次减小1/(j+i)。
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randIndex = int(random.uniform(0,len(dataIndex))) #随机选取样本
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h = sigmoid(sum(dataMatrix[randIndex]*weights)) #选择随机选取的一个样本，计算h
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error = classLabels[randIndex] - h #计算误差
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weights = weights + alpha * error * dataMatrix[randIndex] #更新回归系数
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del(dataIndex[randIndex]) #删除已经使用的样本
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return weights #返回
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"""
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函数说明:梯度上升算法
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Parameters:
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dataMatIn - 数据集
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classLabels - 数据标签
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Returns:
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weights.getA() - 求得的权重数组(最优参数)
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Author:
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Jack Cui
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Blog:
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http://blog.csdn.net/c406495762
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Zhihu:
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https://www.zhihu.com/people/Jack--Cui/
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Modify:
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2017-08-28
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"""
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def gradAscent(dataMatIn, classLabels):
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dataMatrix = np.mat(dataMatIn) #转换成numpy的mat
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labelMat = np.mat(classLabels).transpose() #转换成numpy的mat,并进行转置
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m, n = np.shape(dataMatrix) #返回dataMatrix的大小。m为行数,n为列数。
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alpha = 0.01 #移动步长,也就是学习速率,控制更新的幅度。
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maxCycles = 500 #最大迭代次数
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weights = np.ones((n,1))
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for k in range(maxCycles):
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h = sigmoid(dataMatrix * weights) #梯度上升矢量化公式
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error = labelMat - h
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weights = weights + alpha * dataMatrix.transpose() * error
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return weights.getA() #将矩阵转换为数组，并返回
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"""
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函数说明:使用Python写的Logistic分类器做预测
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Parameters:
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无
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Returns:
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无
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Author:
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Jack Cui
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Blog:
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http://blog.csdn.net/c406495762
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Zhihu:
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https://www.zhihu.com/people/Jack--Cui/
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Modify:
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2017-09-05
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"""
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def colicTest():
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frTrain = open('horseColicTraining.txt') #打开训练集
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frTest = open('horseColicTest.txt') #打开测试集
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trainingSet = []; trainingLabels = []
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for line in frTrain.readlines():
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currLine = line.strip().split('\t')
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lineArr = []
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for i in range(len(currLine)-1):
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lineArr.append(float(currLine[i]))
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trainingSet.append(lineArr)
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trainingLabels.append(float(currLine[-1]))
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trainWeights = stocGradAscent1(np.array(trainingSet), trainingLabels,500) #使用改进的随即上升梯度训练
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errorCount = 0; numTestVec = 0.0
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for line in frTest.readlines():
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numTestVec += 1.0
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currLine = line.strip().split('\t')
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lineArr =[]
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for i in range(len(currLine)-1):
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lineArr.append(float(currLine[i]))
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if int(classifyVector(np.array(lineArr), trainWeights))!= int(currLine[-1]):
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errorCount += 1
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errorRate = (float(errorCount)/numTestVec) * 100 #错误率计算
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print("测试集错误率为: %.2f%%" % errorRate)
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"""
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函数说明:分类函数
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Parameters:
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inX - 特征向量
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weights - 回归系数
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Returns:
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分类结果
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Author:
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Jack Cui
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Blog:
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http://blog.csdn.net/c406495762
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Zhihu:
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https://www.zhihu.com/people/Jack--Cui/
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Modify:
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2017-09-05
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"""
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def classifyVector(inX, weights):
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prob = sigmoid(sum(inX*weights))
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if prob > 0.5: return 1.0
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else: return 0.0
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"""
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函数说明:使用Sklearn构建Logistic回归分类器
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Parameters:
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无
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Returns:
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无
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Author:
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Jack Cui
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Blog:
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http://blog.csdn.net/c406495762
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Zhihu:
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https://www.zhihu.com/people/Jack--Cui/
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Modify:
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2017-09-05
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"""
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def colicSklearn():
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frTrain = open('horseColicTraining.txt') #打开训练集
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frTest = open('horseColicTest.txt') #打开测试集
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trainingSet = []; trainingLabels = []
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testSet = []; testLabels = []
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for line in frTrain.readlines():
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currLine = line.strip().split('\t')
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lineArr = []
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for i in range(len(currLine)-1):
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lineArr.append(float(currLine[i]))
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trainingSet.append(lineArr)
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trainingLabels.append(float(currLine[-1]))
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for line in frTest.readlines():
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currLine = line.strip().split('\t')
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lineArr =[]
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for i in range(len(currLine)-1):
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lineArr.append(float(currLine[i]))
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testSet.append(lineArr)
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testLabels.append(float(currLine[-1]))
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classifier = LogisticRegression(solver = 'sag',max_iter = 5000).fit(trainingSet, trainingLabels)
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test_accurcy = classifier.score(testSet, testLabels) * 100
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print('正确率:%f%%' % test_accurcy)
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if __name__ == '__main__':
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colicSklearn()
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