Logistic Regression，logisticregression,二元分类。自己写的其他机

Logistic Regression，logisticregression,二元分类。自己写的其他机

二元分类。

自己写的其他机器学习的算法：https://github.com/intergret/MachineLearningWithPython

from numpy import *import matplotlib.pyplot as pltclass LogisticRegression:    def __init__(self):        self.dataMat = []        self.labelMat = []        self.weights = []        self.M = 0        self.N = 0        self.alpha = 0.001    def loadDataSet(self,inputfile):        for line in open(inputfile,'r'):            items = line.strip().split()            self.dataMat.append([1.0, float(items[0]), float(items[1])])            self.labelMat.append(int(items[2]))        self.dataMat = mat(self.dataMat)        self.labelMat = mat(self.labelMat).transpose()        self.M,self.N = shape(self.dataMat)        self.weights = mat(ones((self.N,1)))        # self.weights = [[3.0317079],[0.58242302],[-0.58782357]]    def sigmoid(self,z):        return 1.0 / (1.0 + exp(-z))    def classify(self,X):        return 1 if self.sigmoid(sum(X*self.weights)) > 0.5 else 0    def gradientAscent(self):        for k in range(1000):            error = (self.labelMat - self.sigmoid(self.dataMat*self.weights))            self.weights += self.alpha * self.dataMat.transpose()* error        print self.weights    def stochasticGradientAscent_V0(self):        for l in range(300):            for i in range(self.M):                error = self.labelMat[i] - self.sigmoid(sum(self.dataMat[i]*self.weights))                self.weights += self.alpha * self.dataMat[i].transpose() * error    def stochasticGradientAscent_V1(self):        for l in range(300):            idxs = range(self.M)            for i in range(self.M):                alpha = 4.0/(1.0+l+i)+0.01                rdmidx = int(random.uniform(0,len(idxs)))                error = self.labelMat[rdmidx] - self.sigmoid(sum(self.dataMat[rdmidx]*self.weights))                self.weights += self.alpha * self.dataMat[rdmidx].transpose() * error                del(idxs[rdmidx])    def plotSeperator(self):        xcord1 = []; ycord1 = []        xcord2 = []; ycord2 = []        for i in range(self.M):            if int(self.labelMat[i])== 1:                xcord1.append(self.dataMat[i,1]); ycord1.append(self.dataMat[i,2])            else:                xcord2.append(self.dataMat[i,1]); ycord2.append(self.dataMat[i,2])        fig = plt.figure()        ax = fig.add_subplot(111)        ax.scatter(xcord1, ycord1, s=30, c='yellow', marker='s')        ax.scatter(xcord2, ycord2, s=30, c='blue')        x = arange(-3.0, 3.0, 0.1)        w = self.weights.getA()        y = (-w[0]-w[1]*x)/w[2]        ax.plot(x, y)        plt.xlabel('X1')        plt.ylabel('X2');        plt.show()if __name__=='__main__':    inputfile = 'C:\\\\Users\\\\Administrator\\\\Desktop\\\\MachineLearning\\\\LogisticRegression\\\\LogisticInput.txt'    myregression = LogisticRegression()    myregression.loadDataSet(inputfile)    # myregression.gradientAscent()    # myregression.stochasticGradientAscent_V0()    myregression.stochasticGradientAscent_V1()    myregression.plotSeperator()#该片段来自于http://byrx.net