<pre class="prettyprint linenums lang-py">from numpy import * import pandas as pd filename= r"C:\Users\曹晨\Desktop\ata.txt" #文件目录 def loadDataSet(): #读取数据（这里只有两个特征） dataMat = [] labelMat = [] fr = open(filename) for line in fr.readlines(): lineArr = line.strip().split(',') dataMat.append([1.0, float(lineArr[0]), float(lineArr[1]),float(lineArr[2]),float(lineArr[4]),float(lineArr[5]),float(lineArr[6]),float(lineArr[7]),float(lineArr[8]),float(lineArr[9]),float(lineArr[10]),float(lineArr[11]),float(lineArr[12]),float(lineArr[13]),float(lineArr[14]),float(lineArr[15]),float(lineArr[16]),float(lineArr[17]),float(lineArr[18]),float(lineArr[19]),float(lineArr[20]),float(lineArr[21]),float(lineArr[22]),float(lineArr[23]),float(lineArr[24]),float(lineArr[25]),float(lineArr[26]),float(lineArr[27]),float(lineArr[28]),float(lineArr[29]),float(lineArr[30]),float(lineArr[31]),float(lineArr[32]),float(lineArr[33]),float(lineArr[34])]) #前面的1，表示方程的常量。比如两个特征X1,X2，共需要三个参数，W1+W2*X1+W3*X2 labelMat.append(float(lineArr[35])) return dataMat,labelMat def sigmoid(inX): #sigmoid函数 return 1.0/(1+exp(-inX)) def gradAscent(dataMat, labelMat): #梯度上升求最优参数 dataMatrix=mat(dataMat) #将读取的数据转换为矩阵 classLabels=mat(labelMat).transpose() #将读取的数据转换为矩阵 m,n = shape(dataMatrix) alpha = 0.001 #设置梯度的阀值，该值越大梯度上升幅度越大 maxCycles = 500 #设置迭代的次数，一般看实际数据进行设定，有些可能200次就够了 weights = ones((n,1)) #设置初始的参数，并都赋默认值为1。注意这里权重以矩阵形式表示三个参数。 for k in range(maxCycles): h = sigmoid(dataMatrix*weights) error = (classLabels - h) #求导后差值 weights = weights + alpha * dataMatrix.transpose()* error #迭代更新权重 return weights def stocGradAscent0(dataMat, labelMat): #随机梯度上升，当数据量比较大时，每次迭代都选择全量数据进行计算，计算量会非常大。所以采用每次迭代中一次只选择其中的一行数据进行更新权重。 dataMatrix=mat(dataMat) classLabels=labelMat m,n=shape(dataMatrix) alpha=0.01 maxCycles = 500 weights=ones((n,1)) for k in range(maxCycles): for i in range(m): #遍历计算每一行 h = sigmoid(sum(dataMatrix[i] * weights)) error = classLabels[i] - h weights = weights + alpha * error * dataMatrix[i].transpose() return weights def stocGradAscent1(dataMat, labelMat): #改进版随机梯度上升，在每次迭代中随机选择样本来更新权重，并且随迭代次数增加，权重变化越小。 dataMatrix=mat(dataMat) classLabels=labelMat m,n=shape(dataMatrix) weights=ones((n,1)) maxCycles=500 for j in range(maxCycles): #迭代 dataIndex=[i for i in range(m)] for i in range(m): #随机遍历每一行 alpha=4/(1+j+i)+0.0001 #随迭代次数增加，权重变化越小。 randIndex=int(random.uniform(0,len(dataIndex))) #随机抽样 h=sigmoid(sum(dataMatrix[randIndex]*weights)) error=classLabels[randIndex]-h weights=weights+alpha*error*dataMatrix[randIndex].transpose() del(dataIndex[randIndex]) #去除已经抽取的样本 return weights ''' def plotBestFit(weights): #画出最终分类的图 import matplotlib.pyplot as plt dataMat,labelMat=loadDataSet() dataArr = array(dataMat) n = shape(dataArr)[0] xcord1 = []; ycord1 = [] xcord2 = []; ycord2 = [] for i in range(n): if int(labelMat[i])== 1: xcord1.append(dataArr[i,1]) ycord1.append(dataArr[i,2]) else: xcord2.append(dataArr[i,1]) ycord2.append(dataArr[i,2]) fig = plt.figure() ax = fig.add_subplot(111) ax.scatter(xcord1, ycord1, s=30, c='red', marker='s') ax.scatter(xcord2, ycord2, s=30, c='green') x = arange(-3.0, 3.0, 0.1) y = (-weights[0]-weights[1]*x)/weights[2] ax.plot(x, y) plt.xlabel('X1') plt.ylabel('X2') plt.show() ''' def main(): dataArr, labelMat = loadDataSet() # 读入训练样本中的原始数据 A = stocGradAscent1(dataArr, labelMat) # 回归系数a的值 h = sigmoid(mat(dataArr)*A) #预测结果h(a)的值 print(A) #print(labelMat) m =pd.DataFrame.from_records(h) m.to_csv("excel_csv_h.csv") # plotBestFit(A.getA()) if __name__=='__main__': main()</pre>