版权声明:本文为博主原创文章,未经作者允许请勿转载。 https://blog.csdn.net/heiheiya https://blog.csdn.net/heiheiya/article/details/83277101
理论请参考:机器学习-Logistic回归算法学习笔记
def sigmoid(inX):
return 1.0/(1+exp(-inX))
def stocGradAscent1(dataMatrix, classLabels, numIter=150):
m, n = shape(dataMatrix)
weights = 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(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 classifyVector(inX, weights):
prob = sigmoid(sum(inX*weights))
if prob > 0.5:
return 1.0
else:
return 0.0
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(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(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 iterations the average error rate is: %f" %(numTests, errorSum/float(numTests)))