from mapReduce import reduce map(Face, nonFace) _mat = reduce() mat = _mat featureNum, sampleNum = _mat.shape assert sampleNum == (POSITIVE_SAMPLE + NEGATIVE_SAMPLE) assert featureNum == FEATURE_NUM Label_Face = [+1 for i in xrange(POSITIVE_SAMPLE)] Label_NonFace = [-1 for i in xrange(NEGATIVE_SAMPLE)] label = numpy.array(Label_Face + Label_NonFace) cache_filename = ADABOOST_CACHE_FILE + str(0) if os.path.isfile(cache_filename): model = getCachedAdaBoost(mat = _mat, label = label, filename= cache_filename, limit = ADABOOST_LIMIT) else: model = AdaBoost(mat, label, limit = ADABOOST_LIMIT) model.train() model.saveModel(cache_filename) print model