示例#1
0
from numpy import *
import AdaBoost
datMat, classLabels = AdaBoost.loadSimpData()
# D=mat(ones((5,1))/5)
# print(AdaBoost.buildingStump(datMat,classLabels,D))
classEstArr = AdaBoost.adaBoostTrainDS(datMat, classLabels, 10)
print(AdaBoost.adaClassify([[5, 5], [0, 0]], classEstArr))
    ax = plt.subplot(111)
    #画图
    for index in sortedIndicies.tolist()[0]:
        if classLabels[index] == 1.0:
            delX = 0; 
            delY = yStep;
        else:
            delX = xStep; 
            delY = 0;
            ySum += cur[1]
        ax.plot([cur[0],cur[0]-delX],[cur[1],cur[1]-delY], c='b')
        cur = (cur[0]-delX,cur[1]-delY)
    ax.plot([0,1],[0,1],'b--')
    plt.xlabel('False positive rate')
    plt.ylabel('True positive rate')
    plt.title('ROC curve for AdaBoost horse colic detection system')
    ax.axis([0,1,0,1])
    plt.show()
    print("the Area Under the Curve is: ",ySum*xStep)



if __name__ == '__main__':
    trainingMat,trainingLabels = AdaBoost.loadDataSet('horseColicTraining2.txt')
    classifierArray = AdaBoost.adaBoostTrainDS(trainingMat,trainingLabels, 10)
    
    testMat,testLabels = AdaBoost.loadDataSet('horseColicTest2.txt')
    prediction10 = AdaBoost.adaClassify(testMat, classifierArray)
    print(prediction10)
    
    plotROC(prediction10, testLabels)
示例#3
0
# -*- coding: utf-8 -*-
"""

AdaBoost:简单数据集
@author: Jerry
"""
import numpy as np
import AdaBoost


def loadDataSet():
    dataMat = np.matrix(([1., 2.1], [2., 1.1], [1.3, 1.], [1., 1.], [2., 1.]))
    classLabels = [1.0, 1.0, -1.0, -1.0, 1.0]
    return dataMat, classLabels


if __name__ == '__main__':
    dataMat, classLabels = loadDataSet()

    #    AdaBoost.adaBoostTrainDS(dataMat,classLabels, 9)

    classifierArray = AdaBoost.adaBoostTrainDS(dataMat, classLabels, 30)
    predictedLabel = AdaBoost.adaClassify([0, 0], classifierArray)
    print(predictedLabel)
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 10 20:38:02 2018

@author: tf
"""

import AdaBoost
import numpy as np

#dataMat, labelMat = AdaBoost.loadDataSet()
#print(dataMat, '\n', labelMat)

#D = np.ones((5, 1)) / 5
#bestStump, minErr, bestClassEst = AdaBoost.buildStump(dataMat, labelMat, D)
#print(bestStump, '\n', minErr, '\n', bestClassEst)

#classifierArr = AdaBoost.adaBoostTrainDS(dataMat, labelMat)
#print(classifierArr)
#print(max(0.1,0.2))

#clas = AdaBoost.adaClassify(np.array([[5, 5], [0, 0]]), classifierArr)
#print(clas)

dataMat, labelMat = AdaBoost.loadFileDataSet('horseColicTraining2.txt')
classifierArr = AdaBoost.adaBoostTrainDS(dataMat, labelMat)
#print(classifierArr)

testDataMat, testLabelMat = AdaBoost.loadFileDataSet('horseColicTest2.txt')
errRate = AdaBoost.adaClassify(testDataMat, classifierArr, testLabelMat)
print(errRate)