#coding:UTF-8
'''
Created on 2015年6月15日

@author: zhaozhiyong

'''

from numpy import *

def loadSimpleData():
    datMat = mat([[1., 2.1],
                  [2., 1.1],
                  [1.3, 1.],
                  [1., 1.],
                  [2., 1.]])
    classLabels = mat([1.0, 1.0, -1.0, -1.0, 1.0])
    return datMat, classLabels

def singleStumpClassipy(dataMat, dim, threshold, thresholdIneq):
    classMat = ones((shape(dataMat)[0], 1))
    #根據thresholdIneq劃分出不同的類，在'-1'和'1'之間切換
    if thresholdIneq == 'left':#在threshold左側的為'-1'
        classMat[dataMat[:, dim] <= threshold] = -1.0
    else:
        classMat[dataMat[:, dim] > threshold] = -1.0
    
    return classMat

def singleStump(dataArr, classLabels, D):
    dataMat = mat(dataArr)
    labelMat = mat(classLabels).T
    m, n = shape(dataMat)
    numSteps = 10.0
    bestStump = {}
    bestClasEst = zeros((m, 1))
    minError = inf
    for i in xrange(n):#對每一個特徵
        #取第i列特徵的最小值和最大值，以確定步長
        rangeMin = dataMat[:, i].min()
        rangeMax = dataMat[:, i].max()
        stepSize = (rangeMax - rangeMin) / numSteps
        for j in xrange(-1, int(numSteps) + 1):
            #不確定是哪個屬於類'-1'，哪個屬於類'1'，分兩種情況
            for inequal in ['left', 'right']:
                threshold = rangeMin + j * stepSize#得到每個劃分的閾值
                predictionClass = singleStumpClassipy(dataMat, i, threshold, inequal)
                errorMat = ones((m, 1))
                errorMat[predictionClass == labelMat] = 0
                weightedError = D.T * errorMat#D是每個樣本的權重
                if weightedError < minError:
                    minError = weightedError
                    bestClasEst = predictionClass.copy()
                    bestStump['dim'] = i
                    bestStump['threshold'] = threshold
                    bestStump['inequal'] = inequal
    
    return bestStump, minError, bestClasEst

def adaBoostTrain(dataArr, classLabels, G):
    weakClassArr = []
    m = shape(dataArr)[0]#樣本個數
    #初始化D，即每個樣本的權重
    D = mat(ones((m, 1)) / m)
    aggClasEst = mat(zeros((m, 1)))
    
    for i in xrange(G):#G表示的是迭代次數
        bestStump, minError, bestClasEst = singleStump(dataArr, classLabels, D)
        print 'D:', D.T
        #計算分類器的權重
        alpha = float(0.5 * log((1.0 - minError) / max(minError, 1e-16)))
        bestStump['alpha'] = alpha
        weakClassArr.append(bestStump)
        print 'bestClasEst:', bestClasEst.T
        
        #重新計算每個樣本的權重D
        expon = multiply(-1 * alpha * mat(classLabels).T, bestClasEst)
        D = multiply(D, exp(expon))
        D = D / D.sum()
        
        aggClasEst += alpha * bestClasEst
        print 'aggClasEst:', aggClasEst
        aggErrors = multiply(sign(aggClasEst) != mat(classLabels).T, ones((m, 1)))
        errorRate = aggErrors.sum() / m
        print 'total error:', errorRate
        if errorRate == 0.0:
            break
    return weakClassArr

def adaBoostClassify(testData, weakClassify):
    dataMat = mat(testData)
    m = shape(dataMat)[0]
    aggClassEst = mat(zeros((m, 1)))
    for i in xrange(len(weakClassify)):#weakClassify是一個列表
        classEst = singleStumpClassipy(dataMat, weakClassify[i]['dim'], weakClassify[i]['threshold'], weakClassify[i]['inequal'])
        aggClassEst += weakClassify[i]['alpha'] * classEst
        print aggClassEst
    return sign(aggClassEst)
            
if __name__ == '__main__':
    datMat, classLabels = loadSimpleData()
    weakClassArr = adaBoostTrain(datMat, classLabels, 30)
    print "weakClassArr:", weakClassArr
    #test
    result = adaBoostClassify([1, 1], weakClassArr)
    print result