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logistic回归

梯度上升法

import numpy as np 

"""
function:
    加载数据
parameter:
    无
returns:
    dataMat - 数据集
    labelMat - 标签集
"""
def loadDataSet():
    dataMat = []#数据集
    labelMat = []#标签集
    fr = open('testSet.txt')
    for line in fr.readlines():
        lineArr = line.strip().split()
        dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])])
        labelMat.append(int(lineArr[2]))
    return dataMat, labelMat 

"""
function:
    计算sigmoid函数
parameters:
    inX - 变量
return:
    ans - 结果
"""
def sigmoid(inX):
    ans = 1.0 / (1+np.exp(-inX))
    return ans

"""
function:
    梯度上升
parameters:
    dataMatIn - 数据集
    classLabels - 标签集
return:
    weights - 最优参数
"""
def gradAscent(dataMatIn, classLabels):
    dataMatrix = np.mat(dataMatIn)
    labelMat = np.mat(classLabels).transpose()#矩阵转置
    m,n = np.shape(dataMatrix)
    alpha = 0.001#步长
    maxCycles = 500#迭代次数
    weights = np.ones((n,1))
    for k in range(maxCycles):
        h = sigmoid(dataMatrix * weights)#100*3矩阵和100*1矩阵*乘，而不是dot乘
        error = labelMat - h#这里labelMat广播了
        weights = weights + alpha * dataMatrix.transpose() * error 
    return weights 

if __name__ == "__main__":
    dataArr, labelMat = loadDataSet()
    w = gradAscent(dataArr,labelMat)
    print(w)

做图

import numpy as np
import matplotlib.pyplot as plt

"""
function:
    加载数据
parameter:
    无
returns:
    dataMat - 数据集
    labelMat - 标签集
"""
def loadDataSet():
    dataMat = []#数据集
    labelMat = []#标签集
    fr = open('testSet.txt')
    for line in fr.readlines():
        lineArr = line.strip().split()
        dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])])
        labelMat.append(int(lineArr[2]))
    return dataMat, labelMat 

"""
function:
    计算sigmoid函数
parameters:
    inX - 变量
return:
    ans - 结果
"""
def sigmoid(inX):
    ans = 1.0 / (1+np.exp(-inX))
    return ans

"""
function:
    梯度上升
parameters:
    dataMatIn - 数据集
    classLabels - 标签集
return:
    weights - 最优参数
"""
def gradAscent(dataMatIn, classLabels):
    dataMatrix = np.mat(dataMatIn)
    labelMat = np.mat(classLabels).transpose()#矩阵转置
    m,n = np.shape(dataMatrix)
    alpha = 0.001#步长
    maxCycles = 500#迭代次数
    weights = np.ones((n,1))
    for k in range(maxCycles):
        h = sigmoid(dataMatrix * weights)#100*3矩阵和100*1矩阵*乘，而不是dot乘
        error = labelMat - h#这里labelMat广播了
        weights = weights + alpha * dataMatrix.transpose() * error 
    return weights 

"""
function:
    做图
parameters:
    dataMat - 数据集
    weights - 系数
return:
    无
"""
def plotBestFit(dataMat,weights):
    dataMatrix = np.array(dataMat)
    x1 = dataMatrix[0:,1]
    y1 = dataMatrix[0:,2]
    plt.scatter(x1,y1,s=25,marker='o')
    plt.xlim([-4,4])
    plt.ylim([-5,20])
    x2 = np.arange(-3.0, 3.0, 0.1)
    y2 = (-weights[0]-weights[1]*x2)/weights[2]
    y2 = y2.T
    plt.plot(x2,y2)
    plt.show()

if __name__ == "__main__":
    dataMat, labelMat = loadDataSet()
    weights = gradAscent(dataMat,labelMat)
    plotBestFit(dataMat,weights)

随机梯度上升

import numpy as np
import matplotlib.pyplot as plt

"""
function:
    加载数据
parameter:
    无
returns:
    dataMat - 数据集
    labelMat - 标签集
"""
def loadDataSet():
    dataMat = []#数据集
    labelMat = []#标签集
    fr = open('testSet.txt')
    for line in fr.readlines():
        lineArr = line.strip().split()
        dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])])
        labelMat.append(int(lineArr[2]))
    return dataMat, labelMat 

"""
function:
    计算sigmoid函数
parameters:
    inX - 变量
return:
    ans - 结果
"""
def sigmoid(inX):
    ans = 1.0 / (1+np.exp(-inX))
    return ans

"""
function:
    梯度上升
parameters:
    dataMatIn - 数据集
    classLabels - 标签集
return:
    weights - 最优参数
"""
def gradAscent(dataMatIn, classLabels):
    dataMatrix = np.mat(dataMatIn)
    labelMat = np.mat(classLabels).transpose()#矩阵转置
    m,n = np.shape(dataMatrix)
    alpha = 0.001#步长
    maxCycles = 500#迭代次数
    weights = np.ones((n,1))
    for k in range(maxCycles):
        h = sigmoid(dataMatrix * weights)#100*3矩阵和100*1矩阵*乘，而不是dot乘
        error = labelMat - h#这里labelMat广播了
        weights = weights + alpha * dataMatrix.transpose() * error 
    return weights 

"""
function:
    做图
parameters:
    dataMat - 数据集
    weights - 系数
return:
    无
"""
def plotBestFit(dataMat,weights):
    dataMatrix = np.array(dataMat)
    x1 = dataMatrix[0:,1]
    y1 = dataMatrix[0:,2]
    plt.scatter(x1,y1,s=25,marker='o')
    plt.xlim([-4,4])
    plt.ylim([-5,20])
    x2 = np.arange(-3.0, 3.0, 0.1)
    y2 = (-weights[0]-weights[1]*x2)/weights[2]
    y2 = y2.T
    plt.plot(x2,y2)
    plt.show()

"""
function:
    随机梯度上升
parameters:
    dataMatrix - 数据集
    classLabels - 标签集
return:
    weights - 参数
"""
def stocGradAscent0(dataMatrix, classLabels):
    m, n = np.shape(dataMatrix)
    alpha = 0.01
    weights = np.ones(n)
    for i in range(m):
        h = sigmoid(sum(dataMatrix[i]*weights))
        error = classLabels[i] - h
        weights = weights + alpha * error * dataMatrix[i]
    return weights 

if __name__ == "__main__":
    dataArr, labelMat = loadDataSet()
    weights = stocGradAscent0(np.array(dataArr),labelMat)
    plotBestFit(dataArr,weights)

随机梯度上升改进

import numpy as np
import matplotlib.pyplot as plt

"""
function:
    加载数据
parameter:
    无
returns:
    dataMat - 数据集
    labelMat - 标签集
"""
def loadDataSet():
    dataMat = []#数据集
    labelMat = []#标签集
    fr = open('testSet.txt')
    for line in fr.readlines():
        lineArr = line.strip().split()
        dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])])
        labelMat.append(int(lineArr[2]))
    return dataMat, labelMat 

"""
function:
    计算sigmoid函数
parameters:
    inX - 变量
return:
    ans - 结果
"""
def sigmoid(inX):
    ans = 1.0 / (1+np.exp(-inX))
    return ans

"""
function:
    梯度上升
parameters:
    dataMatIn - 数据集
    classLabels - 标签集
return:
    weights - 最优参数
"""
def gradAscent(dataMatIn, classLabels):
    dataMatrix = np.mat(dataMatIn)
    labelMat = np.mat(classLabels).transpose()#矩阵转置
    m,n = np.shape(dataMatrix)
    alpha = 0.001#步长
    maxCycles = 500#迭代次数
    weights = np.ones((n,1))
    for k in range(maxCycles):
        h = sigmoid(dataMatrix * weights)#100*3矩阵和100*1矩阵*乘，而不是dot乘
        error = labelMat - h#这里labelMat广播了
        weights = weights + alpha * dataMatrix.transpose() * error 
    return weights 

"""
function:
    做图
parameters:
    dataMat - 数据集
    weights - 系数
return:
    无
"""
def plotBestFit(dataMat,weights):
    dataMatrix = np.array(dataMat)
    x1 = dataMatrix[0:,1]
    y1 = dataMatrix[0:,2]
    plt.scatter(x1,y1,s=25,marker='o')
    plt.xlim([-4,4])
    plt.ylim([-5,20])
    x2 = np.arange(-3.0, 3.0, 0.1)
    y2 = (-weights[0]-weights[1]*x2)/weights[2]
    y2 = y2.T
    plt.plot(x2,y2)
    plt.show()

"""
function:
    随机梯度上升
parameters:
    dataMatrix - 数据集
    classLabels - 标签集
return:
    weights - 参数
"""
def stocGradAscent0(dataMatrix, classLabels):
    m, n = np.shape(dataMatrix)
    alpha = 0.01
    weights = np.ones(n)
    for i in range(m):
        h = sigmoid(sum(dataMatrix[i]*weights))
        error = classLabels[i] - h
        weights = weights + alpha * error * dataMatrix[i]
    return weights 

"""
function:
    随机梯度上升改进
parameters:
    dataMatrix - 数据集
    classLabels - 标签集
    numIter - 迭代次数
return:
    weights - 参数
"""
def stocGradAscent1(dataMatrix, classLabels, numIter=150):
    m,n = np.shape(dataMatrix)
    weights = np.ones(n)
    for j in range(numIter):
        dataIndex = list(range(m))
        for i in range(m):
            alpha = 4 / (1.0+j+i)+0.01
            randIndex = int(np.random.uniform(0,len(dataIndex)))
            h = sigmoid(sum(dataMatrix[randIndex]*weights))
            error = classLabels[randIndex] - h
            weights = weights + alpha * error * dataMatrix[randIndex]
            del(dataIndex[randIndex])
    return weights 

if __name__ == "__main__":
    dataArr, labelMat = loadDataSet()
    weights = stocGradAscent1(np.array(dataArr),labelMat)
    plotBestFit(dataArr,weights)

从疝气病预测病马的死亡率

import numpy as np 

"""
function:
    计算sigmoid函数
parameters:
    inX - 变量
return:
    ans - 结果
"""
def sigmoid(inX):
    ans = 1.0 / (1+np.exp(-inX))
    return ans

"""
function:
    随机梯度上升改进
parameters:
    dataMatrix - 数据集
    classLabels - 标签集
    numIter - 迭代次数
return:
    weights - 参数
"""
def stocGradAscent1(dataMatrix, classLabels, numIter=150):
    m,n = np.shape(dataMatrix)
    weights = np.ones(n)
    for j in range(numIter):
        dataIndex = list(range(m))
        for i in range(m):
            alpha = 4 / (1.0+j+i)+0.01
            randIndex = int(np.random.uniform(0,len(dataIndex)))
            h = sigmoid(sum(dataMatrix[randIndex]*weights))
            error = classLabels[randIndex] - h
            weights = weights + alpha * error * dataMatrix[randIndex]
            del(dataIndex[randIndex])
    return weights 

"""
function:
    分类器
parameters:
    inX - 待分类向量
    weights - 参数
returns:
    分类结果
"""
def classifyVector(inX, weights):
    prob = sigmoid(sum(inX*weights))
    if prob > 0.5:
        return 1.0
    else: 
        return 0.0

"""
function:
    整个计算流程
parameters:
    无
returns:
    erroRate - 错误率
"""
def colicTest():
    frTrain = open('horseColicTraining.txt')
    frTest = open('horseColicTest.txt')
    trainingSet = []
    trainingLabels = []
    for line in frTrain.readlines():
        currLine = line.strip().split('\t')
        lineArr = []
        for i in range(21):
            lineArr.append(float(currLine[i]))
        trainingSet.append(lineArr)
        trainingLabels.append(float(currLine[21]))
    trainWeights = stocGradAscent1(np.array(trainingSet),trainingLabels,500)
    errorCount = 0
    numTestVec = 0.0
    for line in frTest.readlines():
        numTestVec += 1.0
        currLine = line.strip().split('\t')
        lineArr = []
        for i in range(21):
            lineArr.append(float(currLine[i]))
        if (int(classifyVector(np.array(lineArr), trainWeights)) != 
                int(currLine[21])):
            errorCount += 1
    errorRate = (float(errorCount) / numTestVec)
    print("the error rate of this test is: %f" % errorRate)
    return errorRate 

def multiTest():
    numTests = 10
    errorSum = 0.0
    for k in range(numTests):
        errorSum += colicTest()
    print("after %d itrations the average error rate is: %f" % (numTests,errorSum/float(numTests)))

if __name__ == "__main__":
    multiTest()

机器学习实战　logistic回归

标签：sig   error   而不是   aar   sha   txt   dex   学习   parameter   

原文地址：https://www.cnblogs.com/w-j-c/p/10483122.html