单层决策树实现（AdaBoost）

from numpy import *


def loadSimpData():
    datMat = 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 datMat, classLabels


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


def stumpClassify(dataMatrix, dimen, threshVal, threshIneq):  # just classify the data
    retArray = ones((shape(dataMatrix)[0], 1))
    if threshIneq == 'lt':
        retArray[dataMatrix[:, dimen] <= threshVal] = -1.0
    else:
        retArray[dataMatrix[:, dimen] > threshVal] = -1.0
    return retArray


def buildStump(dataArr, classLabels, D):
    dataMatrix = mat(dataArr);
    labelMat = mat(classLabels).T
    m, n = shape(dataMatrix)
    numSteps = 10.0;
    bestStump = {};
    bestClasEst = mat(zeros((m, 1)))
    minError = inf  # init error sum, to +infinity 正无穷
    for i in range(n):  # loop over all dimensions
        rangeMin = dataMatrix[:, i].min();
        rangeMax = dataMatrix[:, i].max();
        # print(rangeMax,rangeMin)
        stepSize = (rangeMax - rangeMin) / numSteps
        # print(range(-1,int(numSteps)+1))
        for j in range(-1, int(numSteps) + 1):  # loop over all range in current dimension
            for inequal in ['lt', 'gt']:  # go over less than and greater than
                threshVal = (rangeMin + float(j) * stepSize)
                predictedVals = stumpClassify(dataMatrix, i, threshVal, inequal)  # call stump classify with i, j, lessThan
                errArr = mat(ones((m, 1)))
                errArr[predictedVals == labelMat] = 0
                weightedError = D.T * errArr  # calc total error multiplied by D
                print("split: dim %d, thresh %.2f, thresh ineqal: %s, the weighted error is %.3f，minerr=%0.3f" % (i, threshVal, inequal, weightedError,minError))
                if weightedError < minError:
                    minError = weightedError  #错误率
                    bestClasEst = predictedVals.copy() #最好结果错了两个标签
                    bestStump['dim'] = i #保存最好的列特征
                    bestStump['thresh'] = threshVal
                    bestStump['ineq'] = inequal
    return bestStump, minError, bestClasEst
D = mat(ones((5,1))/5)
data,label = loadSimpData()
bestStump, minError, bestClasEst = buildStump(data,label,D)
print(bestStump)
print(minError)
print(bestClasEst)