#!/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
datasArr.append(floatdatas[:-1])
labelsArr.append(int(floatdatas[-1]))
frTrain.close()
return datasArr, labelsArr
if __name__ == '__main__':
trainDatas, trainLabels = loadDataSet('dataset/trainingDatas.txt')
print np.shape(trainDatas)
print trainDatas
print np.shape(trainLabels)
print trainLabels
# 训练算法获得多个简单决策树分类器
bestDecisionStumps = adaboost.adaboostTrainDecisionStump(trainDatas, trainLabels, 40)
testDatas, testLabels = loadDataSet('dataset/testDatas.txt')
# 返回预测结果
weightedForecastClasses, confidence = \
adaboost.adaboostClassify(testDatas, bestDecisionStumps)
# 统计分类错误
errorArr = np.matrix(np.zeros((len(testLabels), 1)))
errorArr[np.sign(weightedForecastClasses) != np.matrix(testLabels).T] = 1
print "errorArr:"
print errorArr.T
print "分类的结果："
print "预测的类别：", np.sign(weightedForecastClasses).T
print "测试数据共 %d 个" % len(testLabels)
print "错误分类共 %d 个" % errorArr.sum()
print "分类的错误率为：", 1.0 * errorArr.sum() / len(testLabels)
