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
