Python loadSimpData Examples

Example #1

#coding:utf-8

import adaboost
from numpy import *

datMat, classLabels = adaboost.loadSimpData()

#print datMat,classLabels

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

classifierArray = adaboost.adaBoostTrainDS(datMat, classLabels, 9)
print classifierArray

Example #2

File: boostTest.py Project: anty-zhang/dm

# -*- coding:utf-8 -*-
import adaboost
from numpy import *

myData,myLabels = adaboost.loadSimpData()
'''
print ('myData is ' , myData)
print ('myLabels is' , myLabels)

D = mat(ones((5,1))/5)
print ('D is', D)

myBStump,myMError,myBCE = adaboost.buildStump(myData, myLabels, D)
print ('myBStump is', myBStump)
print ('myMError is', myMError)
print ('myBCE is', myBCE)
'''
classiFierArray,classEst = adaboost.adaBoostTrainDS(myData,myLabels,30)
print ('classiFierArray is ',classiFierArray)
aggClassEst = adaboost.adaClassify([[5,5],[0,0]], classiFierArray)
print ('aggClassEst is ' ,  aggClassEst)

Example #3

File: MachineLearningInAction.py Project: cyendra/MachineLearningInAction

import kNN
from numpy import *
import operator
from os import listdir
import trees
import treePlotter

import bayes

import logRegres

import svmMLiA
import boost
import adaboost

datMat, classLabels = adaboost.loadSimpData()

D = mat(ones((5,1))/5)
print boost.buildStump(datMat, classLabels, D)

Example #4

File: test_1.py Project: wang4yu6peng13/MachineLearningInAction

import adaboost
from numpy import *

datMat, classLabels = adaboost.loadSimpData()
D = mat(ones((5, 1))/5)
print adaboost.buildStump(datMat, classLabels, D)
classifierArray = adaboost.adaBoostTrainDS(datMat, classLabels, 9)
print classifierArray

datArr, labelArr = adaboost.loadSimpData()
classifierArr = adaboost.adaBoostTrainDS(datArr, labelArr, 30)
print adaboost.adaClassify([0, 0], classifierArr)
print adaboost.adaClassify([[5, 5], [0, 0]], classifierArr)

Example #5

import adaboost
from numpy import *

datMat, classLabels = adaboost.loadSimpData()
D = mat(ones((5, 1)) / 5)
print adaboost.buildStump(datMat, classLabels, D)
classifierArray = adaboost.adaBoostTrainDS(datMat, classLabels, 9)
print classifierArray

datArr, labelArr = adaboost.loadSimpData()
classifierArr = adaboost.adaBoostTrainDS(datArr, labelArr, 30)
print adaboost.adaClassify([0, 0], classifierArr)
print adaboost.adaClassify([[5, 5], [0, 0]], classifierArr)

Example #6

File: main.py Project: strawsyz/machine_learning_study

import adaboost
import boost
from numpy import *
dataMat, dataLabels = adaboost.loadSimpData()
print(dataMat)
print(dataLabels)

D = mat(ones((5, 1)) / 5)
boost.buildStump(dataMat, dataLabels, D)

Example #7

File: command.py Project: sunbeansoft/MLaction

__author__ = 'sunbeansoft'

import adaboost as ad
from numpy import *

datMat, classLabels = ad.loadSimpData()

D = mat(ones((5, 1)))

print ad.buildStump(datMat, classLabels, D)

Example #8

File: 7_5.py Project: gunix007/Machine_Learning_In_Action

import adaboost

datArr, labelArr = adaboost.loadSimpData()
classifierArr = adaboost.adaBoostTrainDS(datArr, labelArr, 30)

adaboost.adaClassify([0, 0], classifierArr)
adaboost.adaClassify([[5, 5], [0, 0]], classifierArr)

Example #9

#!usr/bin/env python3
# -*- coding:utf-8 -*-
"""
#@author:Benny.Chen
#@file: main.py
#@time: 2020/6/6 16:23
#@email:[email protected]
"""
import adaboost as ada
if __name__ == '__main__':
    dataArr,classLabel = ada.loadSimpData()
    ada.adaBoostTrainDS(dataArr,classLabel,10)

Example #10

File: test.py Project: sundyCoder/CSK

from numpy import *
import adaboost
datArr, labelArr = adaboost.loadSimpData()
#classifierArr = adaboost.adaBoostTrainDS(datArr,labelArr,30)
#adaboost.adaClassify([[5, 5],[0,0]],classifierArr)
#aggclassEst = mat(ones(5,1)/5)

Example #11

File: testBuildDecisionStump.py Project: QianguoSun/Machine_Learning_In_Action_Algorithms

#!/usr/bin/python2.7
# _*_ coding: utf-8 _*_

"""
@Author: MarkLiu
"""

import adaboost
import numpy as np

# 训练算法
dataMatrix, classLabels = adaboost.loadSimpData()
bestDecisionStumps = adaboost.adaboostTrainDecisionStump(dataMatrix, classLabels, 20)
print bestDecisionStumps

print "-------测试算法-------"
testDatas = [[0, 0], [5, 0]]
weightedForecastClasses, confidence = \
    adaboost.adaboostClassify(testDatas, bestDecisionStumps)

print "预测的结果及对应的分类把握："
print np.sign(weightedForecastClasses).T
print confidence.T

Example #12

import adaboost
datmat, classlabel=adaboost.loadSimpData()

from numpy import *
d = mat(ones((5,1))/5)
#print(adaboost.buildStump(datmat,classlabel,d))

#classifier,aggClassEst = adaboost.adaBoostTrainDS(datmat,classlabel,9)
#print(classifier)
#print(aggClassEst)

#print(adaboost.adaClassify([[0,0],[1,1]],classifier))

datarr,labelarr = adaboost.loadDataSet('horseColicTraining2.txt')
classifier,aggClassEst = adaboost.adaBoostTrainDS(datarr,labelarr,40)
testarr,testlabelarr = adaboost.loadDataSet('horseColicTest2.txt')
prediction = adaboost.adaClassify(testarr,classifier)
errarr = mat(ones((67,1)))
print(errarr[prediction != mat(testlabelarr).T].sum())
adaboost.plotROC(aggClassEst.T,labelarr)