邏輯迴歸演算法的一種實現
阿新 • • 發佈:2018-11-09
"""邏輯迴歸演算法的一種實現__1""" import numpy as np import matplotlib.pyplot as plt """載入資料集,將資料集中兩列資料分別儲存到datamat和labelmat""" def loadDataSet(): dataMat = [] labelMat = [] fr = open('/home/jerry/文件/testset.csv') for line in fr.readlines(): lineArr = line.strip().split() dataMat.append([1.0,float(lineArr[0]),float(lineArr[1])]) labelMat.append(int (lineArr[2])) fr.close() return dataMat,labelMat """求解最佳擬合的一組迴歸係數""" def gradAscent(dataMatIn,classLables): dataMatrix = mat(dataMatIn) labelMat = mat(classLables).transpose() m,n = shape(dataMatrix) alpha = 0.001 maxCycles = 500 weights = ones((n,1)) for k in range(maxCycles): h = sigmoid(dataMatrix*weights) error = (labelMat - h) weights = weights + alpha * dataMatrix.transpose()*error return weights.getA() """繪製決策邊界""" def plotBestFit(weights): dataMat,labelMat = loadDataSet() dataArr = np.array(dataMat) #將datamat轉換為numpy陣列 n = np.shape(dataMat)[0] xcord1 = [];ycord1 = [] #正樣本 xcord2 = [];ycord2 = [] #負樣本 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 = 20, c = 'red', marker = 's', alpha = 0.5) ax.scatter(xcord2, ycord2, s = 20, c = 'green', alpha = 0.5) x = np.arange(-3.0, 3.0, 0.1) y = (-weights[0] - weights[1]*x) / weights[2] ax.plot(x,y) plt.title('BestFit') plt.xlabel('x1') plt.ylabel('x2') plt.show() if __name__ == '__main__': dataMat,labelMat = loadDataSet() weights = gradAscent(dataMat,labelMat) plotBestFit(weights)