機器學習經(jīng)典算法-logistic回歸代碼詳解

更新時間：2017年12月22日 15:39:58 作者：moodytong

這篇文章主要介紹了機器學習經(jīng)典算法-logistic回歸代碼詳解，具有一定借鑒價值，需要的朋友可以參考下。

一、算法簡要

我們希望有這么一種函數(shù)：接受輸入然后預測出類別，這樣用于分類。這里，用到了數(shù)學中的sigmoid函數(shù)，sigmoid函數(shù)的具體表達式和函數(shù)圖象如下：

可以較為清楚的看到，當輸入的x小于0時，函數(shù)值<0.5，將分類預測為0；當輸入的x大于0時，函數(shù)值>0.5，將分類預測為1。

1.1 預測函數(shù)的表示

1.2參數(shù)的求解

二、代碼實現(xiàn)

函數(shù)sigmoid計算相應的函數(shù)值；gradAscent實現(xiàn)的batch-梯度上升，意思就是在每次迭代中所有數(shù)據(jù)集都考慮到了；而stoGradAscent0中，則是將數(shù)據(jù)集中的示例都比那里了一遍，復雜度大大降低；stoGradAscent1則是對隨機梯度上升的改進，具體變化是alpha每次變化的頻率是變化的，而且每次更新參數(shù)用到的示例都是隨機選取的。

from numpy import * 
import matplotlib.pyplot as plt 
def loadDataSet(): 
  dataMat = [] 
  labelMat = [] 
  fr = open('testSet.txt') 
  for line in fr.readlines(): 
    lineArr = line.strip('\n').split('\t') 
    dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])]) 
    labelMat.append(int(lineArr[2])) 
  fr.close() 
  return dataMat, labelMat 
def sigmoid(inX): 
  return 1.0/(1+exp(-inX)) 
def gradAscent(dataMatIn, classLabels): 
  dataMatrix = mat(dataMatIn) 
  labelMat = mat(classLabels).transpose() 
  m,n=shape(dataMatrix) 
  alpha = 0.001 
  maxCycles = 500 
  weights = ones((n,1)) 
  errors=[] 
  for k in range(maxCycles): 
    h = sigmoid(dataMatrix*weights) 
    error = labelMat - h 
    errors.append(sum(error)) 
    weights = weights + alpha*dataMatrix.transpose()*error 
  return weights, errors 
def stoGradAscent0(dataMatIn, classLabels): 
  m,n=shape(dataMatIn) 
  alpha = 0.01 
  weights = ones(n) 
  for i in range(m): 
    h = sigmoid(sum(dataMatIn[i]*weights)) 
    error = classLabels[i] - h  
    weights = weights + alpha*error*dataMatIn[i] 
  return weights 
def stoGradAscent1(dataMatrix, classLabels, numIter = 150): 
  m,n=shape(dataMatrix) 
  weights = ones(n) 
  for j in range(numIter): 
    dataIndex=range(m) 
    for i in range(m): 
      alpha= 4/(1.0+j+i)+0.01 
      randIndex = int(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 
def plotError(errs): 
  k = len(errs) 
  x = range(1,k+1) 
  plt.plot(x,errs,'g--') 
  plt.show() 
def plotBestFit(wei): 
  weights = wei.getA() 
  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 classifyVector(inX, weights): 
  prob = sigmoid(sum(inX*weights)) 
  if prob>0.5: 
    return 1.0 
  else: 
    return 0 
def colicTest(ftr, fte, numIter): 
  frTrain = open(ftr) 
  frTest = open(fte) 
  trainingSet=[] 
  trainingLabels=[] 
  for line in frTrain.readlines(): 
    currLine = line.strip('\n').split('\t') 
    lineArr=[] 
    for i in range(21): 
      lineArr.append(float(currLine[i])) 
    trainingSet.append(lineArr) 
    trainingLabels.append(float(currLine[21])) 
  frTrain.close() 
  trainWeights = stoGradAscent1(array(trainingSet),trainingLabels, numIter) 
  errorCount = 0 
  numTestVec = 0.0 
  for line in frTest.readlines(): 
    numTestVec += 1.0 
    currLine = line.strip('\n').split('\t') 
    lineArr=[] 
    for i in range(21): 
      lineArr.append(float(currLine[i])) 
    if int(classifyVector(array(lineArr), trainWeights))!=int(currLine[21]): 
      errorCount += 1 
  frTest.close() 
  errorRate = (float(errorCount))/numTestVec 
  return errorRate 
def multiTest(ftr, fte, numT, numIter): 
  errors=[] 
  for k in range(numT): 
    error = colicTest(ftr, fte, numIter) 
    errors.append(error) 
  print "There "+str(len(errors))+" test with "+str(numIter)+" interations in all!" 
  for i in range(numT): 
    print "The "+str(i+1)+"th"+" testError is:"+str(errors[i]) 
  print "Average testError: ", float(sum(errors))/len(errors) 
''''' 
data, labels = loadDataSet() 
weights0 = stoGradAscent0(array(data), labels) 
weights,errors = gradAscent(data, labels) 
weights1= stoGradAscent1(array(data), labels, 500) 
print weights 
plotBestFit(weights) 
print weights0 
weights00 = [] 
for w in weights0: 
  weights00.append([w]) 
plotBestFit(mat(weights00)) 
print weights1 
weights11=[] 
for w in weights1: 
  weights11.append([w]) 
plotBestFit(mat(weights11)) 
''' 
multiTest(r"horseColicTraining.txt",r"horseColicTest.txt",10,500)

總結

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