Esempi in Python per loadDataSet

Esempio n. 1

File: adaboost_test.py Progetto: z8g/common

 def test_loadDataSet(self):
     dataArr, labelArr = adaboost.loadDataSet('train.txt')
     print "[dataArr]", dataArr
     print "[labelArr]", labelArr
     classifierArray = adaboost.adaBoostTrainDS(dataArr, labelArr, 9)
     testArr, testLabelArr = adaboost.loadDataSet('test.txt')
     prediction10 = adaboost.adaClassify(testArr, classifierArray)
     errArr = mat(ones((67, 1)))
     print errArr[prediction10 != mat(testLabelArr).T].sum()

Esempio n. 2

def testHolic():
    datArr,labelArr = adaboost.loadDataSet('horseColicTraining2.txt')
    classifierArray, classifierEst = adaboost.adaBoostTrainDS(datArr, labelArr, 10)

    testArr, testLabelArr = adaboost.loadDataSet('horseColicTest2.txt')
    prediction10 = adaboost.adaClassify(testArr, classifierArray)
    print("prediction:", prediction10)

    errArr = mat(ones((67,1)))
    errCnt = errArr[prediction10 != mat(testLabelArr).T].sum()
    print("err count:%d error rate:%.2f" % (errCnt, float(errCnt)/67))

    adaboost.plotROC(classifierEst.T, labelArr)

Esempio n. 3

File: 7_6.py Progetto: rgszyxyz/machineLearninginAction

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

'7.6'

__author__ = 'lxp'

import adaboost
import numpy as np

datArr, labelArr = adaboost.loadDataSet('horseColicTraining2.txt')
classifierArray = adaboost.adaBoostTrainDS(datArr, labelArr, 10)
testArr, testLabelArr = adaboost.loadDataSet('horseColicTest2.txt')
prediction10 = adaboost.adaClassify(testArr, classifierArray)
errArr = np.mat(np.ones((67, 1)))
print(errArr[prediction10 != np.mat(testLabelArr).T].sum())

Esempio n. 4

import adaboost
from numpy import *
#datMat, classLabels = adaboost.loadSimpleData()
datMat, classLabels = adaboost.loadDataSet('horseColicTraining2.txt')
classifierArray, aggClassEst = adaboost.adaBoostTrainDS(
    datMat, classLabels, 10)
adaboost.plotROC(aggClassEst.T, classLabels)

Esempio n. 5

File: shell.py Progetto: zack624/Machine-Learning-In-Action

# -*- coding: utf-8 -*-

import adaboost
from numpy import *
da, la = adaboost.loadDataSet('horseColicTraining.txt')
ca = adaboost.adaBoostTrainDS(da, la, 10)
tda, tla = adaboost.loadDataSet('horseColicTest.txt')
prediction10 = adaboost.adaClassify(tda, ca)
errArr = mat(ones((67, 1)))
errArr[prediction10 != mat(tla).T].sum()

reload(adaboost)
da, la = adaboost.loadDataSet('horseColicTraining.txt')
ca, ace = adaboost.adaBoostTrainDS(da, la, 40)
adaboost.plotROC(ace.T, la)

Esempio n. 6

# coding:utf-8

import adaboost
from numpy import *

#datMat,classLabels=adaboost.loadSimpData()

# D=mat(ones((5,1))/5)
# result=adaboost.buildStump(datMat,classLabels,D)
# print result

# classifierArray=adaboost.adaBoostTrainDS(datMat,classLabels,9)
# print classifierArray
#
# reload(adaboost)
# result= adaboost.adaClassify([0,0],classifierArray)
# print result

# dataArr,labelArr=adaboost.loadDataSet('horseColicTraining2.txt')
# classifierArray=adaboost.adaBoostTrainDS(dataArr,labelArr,10)
# testArr,testLabelArr=adaboost.loadDataSet('horseColicTest2.txt')
# prediction10=adaboost.adaClassify(testArr,classifierArray)
# errArr=mat(ones((67,1)))
# errArr[prediction10!=mat(testLabelArr).T].sum()

dataArr,labelArr=adaboost.loadDataSet('horseColicTraining2.txt')
classifierArray,aggClassEst=adaboost.adaBoostTrainDS_plt(dataArr,labelArr,10)
adaboost.plotROC(aggClassEst.T,labelArr)

Esempio n. 7

File: class_imbalance.py Progetto: z8g/common

def test_plotROC():
    from adaboost import loadDataSet
    dataArr, labelArr = loadDataSet("train.txt")
    _, aggClassEst = adaBoostTrainDS(dataArr, labelArr, 9)
    plotROC(aggClassEst.T, labelArr)

Esempio n. 8

File: test_2.py Progetto: wang4yu6peng13/MachineLearningInAction

import adaboost
from numpy import *

datArr, labelArr = adaboost.loadDataSet('horseColicTraining2.txt')
#classifierArray = adaboost.adaBoostTrainDS(datArr, labelArr, 10)
#testArr, testLabelArr = adaboost.loadDataSet('horseColicTest2.txt')
#prediction10 = adaboost.adaClassify(testArr, classifierArray)
#errArr = mat(ones((67, 1)))
#print errArr[prediction10!=mat(testLabelArr).T].sum()

classifierArray, aggClassEst = adaboost.adaBoostTrainDS(datArr, labelArr, 10)
adaboost.plotROC(aggClassEst.T, labelArr)

Esempio n. 9

File: adaboostTest.py Progetto: ForrestPi/ML

#!/usr/bin/env python
#-*- coding:utf-8 -*-
import adaboost
from numpy import *
#dataMat,classLabels=adaboost.loadSimData()

# D=mat(ones((5,1))/5)
# print D
# 
# bestStump,minError,bestClasEst=adaboost.buildStump(dataMat,classLabels,D)
# print bestStump

# classifierArray=adaboost.adaBoostTrainDS(dataMat,classLabels,30)
# print adaboost.adaClassify([[5,5],[0,0]],classifierArray)

dataArr,labelArr=adaboost.loadDataSet('./dataSet/horseColicTraining2.txt')
classifierArray,aggClassEst=adaboost.adaBoostTrainDS(dataArr,labelArr,10)
adaboost.plotROC(aggClassEst.T,labelArr)
testArr,testLabelArr=adaboost.loadDataSet('./dataSet/horseColicTest2.txt')
prediction10=adaboost.adaClassify(testArr,classifierArray)
errArr=mat(ones((67,1)))
print errArr[prediction10!=mat(testLabelArr).T].sum()

Esempio n. 10

File: testBoost.py Progetto: Mic-JasonTang/Machine_Learing_In_Action

# bestStump, minError, bestClasEst = adaboost.buildStump(dataMat, labelMat, D)

# print(bestStump)
# print(minError)
# print(bestClasEst)

# classifierArr = adaboost.adaBoostTrainDS(dataMat, labelMat, 40)

# print(classifierArr)

# result = adaboost.adaClassify([[1, 5], [2, 4]], classifierArr)

# print(result)

dataMat, labelMat = adaboost.loadDataSet("horseColicTraining2.txt")

classifierArr, aggClassEst = adaboost.adaBoostTrainDS(dataMat, labelMat, 50)

print(classifierArr)
print(aggClassEst.T.shape)

adaboost.plotROC(aggClassEst.T, labelMat)

# dataMat, labelMat = adaboost.loadDataSet("horseColicTest2.txt")
# pred = adaboost.adaClassify(dataMat, classifierArr)

# print(np.mat(labelMat).shape)
# print(np.mat(labelMat).T.shape)
# print(len(dataMat))
# errorMat = np.mat(np.ones((len(dataMat), 1)))

Esempio n. 11

File: test.py Progetto: sevenry/python_learning_note

#print( ones((shape(datMat)[0],1)))
D=mat(ones((5,1))/5)
#print(D)
#print(adaboost.buildStump(datMat,classLabels,D))
#adaboost.buildStump(datMat,classLabels,D)

classifierArray,kk=adaboost.adaBoostTrainDS(datMat,classLabels,44)
print(classifierArray)
#adaboost.addrin()

ans=adaboost.adaClassify(datMat,classifierArray)
#print(ans)

'''

datArr,labelArr=adaboost.loadDataSet('horseColicTraining2.txt')
classifierArray,aggClassEst=adaboost.adaBoostTrainDS(datArr,labelArr,40)
#print(classifierArray)
#print(aggClassEst[0:10])
#print(shape(aggClassEst.T))
#sortedIndicies = aggClassEst.T.argsort()
#print(shape(sortedIndicies))
#print(sortedIndicies[0,:10])
#print(sortedIndicies[0])
#print(len(classifierArray))
#adaboost.plotROC(aggClassEst.T,labelArr)

##利用测试集作检测
datatest,labeltest=adaboost.loadDataSet('horseColicTest2.txt')
pre=adaboost.adaClassify(datatest,classifierArray)
s=0

Esempio n. 12

File: test.py Progetto: FenHua/machinelearning

import adaboost
reload(adaboost)
from numpy import *
datMat,classLabels=adaboost.loadSimpData()
'''
D=mat(ones((5,1))/5)
bestStump,minError,bestClassEst=adaboost.buildStump(datMat,classLabels,D)
print bestStump
print minError
print bestClassEst
'''
'''
classifierArray=adaboost.adaBoostTrainDs(datMat,classLabels,40)
print classifierArray
result=adaboost.adaClassify([0,0],classifierArray)
print result
'''

datArr,labelArr=adaboost.loadDataSet('C:\Users\YAN\Desktop\Adaboost/horseColicTraining.txt')
classifierArray,aggClassEst=adaboost.adaBoostTrainDs(datArr,labelArr,10)
'''
testArr,testLabelArr=adaboost.loadDataSet('C:\Users\YAN\Desktop\Adaboost/horseColicTest.txt')
prediction=adaboost.adaClassify(testArr,classifierArray)
errArr=mat(ones((67,1)))
result=errArr[prediction!=mat(testLabelArr).T].sum()
print result
'''
adaboost.plotROC(aggClassEst.T,labelArr)

Esempio n. 13

File: test.py Progetto: TheOneAC/ML

#!/usr/bin/python
from numpy import *

import adaboost

datMat, classLabels = adaboost.loadDataSet()

#D = matrix(ones((5,1))/5)
#adaboost.buildStump(datMat, classLabels, D)

classifierArr = adaboost.adaBoostTrainDS(datMat, classLabels,9)
adaboost.adaClassify([0,0],classifierArr)