def __init__(self, gamma=1, teta_onl=1, teta_agglo=1, bthres=0.5, simil='mid', sing='max', isDraw=False, oper='min', isNorm=True, norm_range=[0, 1], V_pre=np.array([], dtype=np.float64), W_pre=np.array([], dtype=np.float64), classId_pre=np.array([], dtype=np.int16)): BaseBatchLearningGFMM.__init__(self, gamma, teta_onl, isDraw, oper, isNorm, norm_range) self.teta_onl = teta_onl self.teta_agglo = teta_agglo self.V = V_pre self.W = W_pre self.classId = classId_pre self.bthres = bthres self.simil = simil self.sing = sing
def __init__(self, gamma = 1, teta = 1, bthres = 0.5, simil = 'mid', sing = 'max', isDraw = False, oper = 'min', isNorm = True, norm_range = [0, 1], cardin = np.array([], dtype=np.int64), clusters = np.array([], dtype=object)): BaseBatchLearningGFMM.__init__(self, gamma, teta, isDraw, oper, isNorm, norm_range) self.bthres = bthres self.simil = simil if simil == 'mid': self.sing = sing else: self.sing = 'max' self.cardin = cardin self.clusters = clusters
def __init__(self, numClassifier=10, numFold=10, gamma=1, teta=1, bthres=0.5, simil='mid', sing='max', oper='min', isNorm=True, norm_range=[0, 1]): BaseBatchLearningGFMM.__init__(self, gamma, teta, False, oper, isNorm, norm_range) self.numFold = numFold self.numClassifier = numClassifier self.bthres = bthres self.simil = simil self.sing = sing self.numHyperboxes = 0
def __init__(self, gamma=1, teta_onl=1, teta_agglo=1, bthres=0.5, simil='mid', sing='max', isDraw=False, oper='min', isNorm=True, norm_range=[0, 1]): BaseBatchLearningGFMM.__init__(self, gamma, teta_onl, isDraw, oper, isNorm, norm_range) self.teta_onl = teta_onl self.teta_agglo = teta_agglo self.bthres = bthres self.simil = simil self.sing = sing
def __init__(self, numClassifier=5, gamma=1, teta=1, bthres=0.95, bthres_min=0.05, simil='mid', sing='max', oper='min', isNorm=True, norm_range=[0, 1]): BaseBatchLearningGFMM.__init__(self, gamma, teta, False, oper, isNorm, norm_range) self.bthres_min = bthres_min self.numClassifier = numClassifier self.bthres = bthres self.simil = simil self.sing = sing self.baseClassifiers = np.empty(numClassifier, dtype=BaseBatchLearningGFMM) self.numHyperboxes = 0