def plotTest():
featureDatas, classTypes = GradientRegression.loadDataSet()
weights = GradientRegression.getBestRegressionWeightsByGradientAscent(
featureDatas, classTypes)
print weights
GradientRegression.plotBestRegressionLine(
featureDatas, classTypes, weights)
def randomGradientAscentTest():
featureDatas, classTypes = GradientRegression.loadDataSet()
weightsMatrix, weights = GradientRegression.getBestWeightsByRandomGradientAscent(
featureDatas, classTypes, 50)
print weights
GradientRegression.plotBestRegressionLine(
featureDatas, classTypes, weights)
def colicTest():
frTrain = open('dataset/trainingDatas.txt')
frTest = open('dataset/testDatas.txt')
trainingSet = []
trainingLabels = []
for line in frTrain.readlines():
currLine = line.strip().split('\t')
lineArr = []
for i in range(len(currLine) - 1):
lineArr.append(float(currLine[i]))
trainingSet.append(lineArr)
trainingLabels.append(float(currLine[-1]))
trainWeights = GradientRegression.getBestWeightsByRandomGradientAscent(trainingSet, trainingLabels, 1000)[1]
print trainWeights
errorCount = 0
numTestVec = 0.0
for line in frTest.readlines():
numTestVec += 1.0
currLine = line.strip().split('\t')
lineArr = []
for i in range(len(currLine) - 1):
lineArr.append(float(currLine[i]))
if int(GradientRegression.logisticClassify(lineArr, trainWeights)) != int(currLine[-1]):
errorCount += 1
errorRate = (float(errorCount) / numTestVec)
print "the error rate of this test is: %f" % errorRate
return errorRate
def weightsTest():
featureDatas, classTypes = GradientRegression.loadDataSet()
weights = GradientRegression.getBestRegressionWeightsByGradientAscent(featureDatas, classTypes)
print weights
def plotWeightsAstringencyTest():
featureDatas, classTypes = GradientRegression.loadDataSet()
GradientRegression.plotWeightsAstringency(featureDatas, classTypes)
