import EXTRAS
import AdaBoost
import numpy
# EXTRAS.plotSimpleData()
# dataMat, labels = AdaBoost.loadSimpleData()
# D = numpy.mat(numpy.ones((5, 1)) / 5)
# bestStump, minError, bestClasEst = AdaBoost.buildStump(dataMat, labels, D)
dataMat, labels = AdaBoost.loadDataSet("E:/TestDatas/MachineLearningInAction/Ch07/horseColicTraining2.txt")
classifierArr, aggClassExt = AdaBoost.adaBoostTrainDS(dataMat, labels, 10)
AdaBoost.plotROC(aggClassExt.T, labels)
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))
def loadDataSet(fileName): #general function to parse tab -delimited floats
numFeat = len(open(fileName).readline().split('\t')) #get number of fields
dataMat = []; labelMat = []
fr = open(fileName)
for line in fr.readlines():
lineArr =[]
curLine = line.strip().split('\t')
for i in range(numFeat-1):
lineArr.append(float(curLine[i]))
dataMat.append(lineArr)
labelMat.append(float(curLine[-1]))
return dataMat,labelMat
datArr,labelArr = loadDataSet('horseColicTest2.txt')
weakClassArr,aggClassEst = AdaBoost.adaBoostTrainDS(datArr,labelArr,40)
'''
testDataArr,testLabelArr= loadDataSet('horseColicTest2.txt')
prediction10=AdaBoost.adaClassify(testDataArr,weakClassArr)
errArr = mat(ones((67,1)))
#统计错误数（错误率=错误数/67）
errArr[prediction10!=mat(testLabelArr).T].sum()
'''
'''非均衡分类问题
假设所有类别的分类代价,在大多数情况下不同类别的分类代价并不相等。
1.调节分类器的阈值
一种不同分类器的评价方法:ROC曲线、AUC
度量分类器性能的指标:构建一个同时使正确率和召回率最大的分类器是具有挑战性的。
'''
# -*- 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 -*-
from numpy import *
import AdaBoost as ab
import LAB as lab
if __name__ == '__main__':
mylist = []
mytrain = []
# 载入网球
for index in range(5):
arr = lab.load('B0_%d.jpg' % index)
arr2 = lab.LAB(arr).flatten() # 转化后展平
mylist.append(arr2.tolist())
mytrain.append(1.)
# 载入苹果
for index in range(5):
arr = lab.load('B2_%d.jpg' % index)
arr2 = lab.LAB(arr).flatten() # 转化后展平
mylist.append(arr2.tolist())
mytrain.append(-1.)
#
dataArr = matrix(mylist)
classLabels = matrix(mytrain)
D = mat(ones((10, 1)) / 10)
classifierArray = ab.adaBoostTrainDS(dataArr, classLabels, 10)
for x in classifierArray:
print('alpha:', x['alpha'], 'dim:', x['dim'], 'thresh:', x['thresh'],
'ineq:', x['ineq'])
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)
# -*- 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)
