def __init__(self, myBust):
# MSE: Mean Square Error
# Cross-Entropy: NLL of probability (output)
self.base_learner = interface.CSLFN_param(myBust.learners[-1])
self.T = myBust.Tmax
self.alg = myBust.alg
def __init__(self, myDSNBust):
# MSE: Mean Square Error
# Cross-Entropy: NLL of probability (output)
self.base_learner = interface.CSLFN_param(myDSNBust.layers[-1])
self.Lmax = myDSNBust.Lmax
self.nL = myDSNBust.nL
self.alpha = myDSNBust.alpha
self.beta = myDSNBust.beta
self.Inyection = myDSNBust.Inyection
self.Agregation = myDSNBust.Agregation
self.Enphasis = myDSNBust.Enphasis
def __init__(self, myDSN):
# MSE: Mean Square Error
# Cross-Entropy: NLL of probability (output)
self.base_learner = interface.CSLFN_param(myDSN.layers[-1])
self.nL = myDSN.nL
self.nP = myDSN.nP
# Intermediate results info.
# Intermediate results info.
self.gammas = myDSN.gammas
if (myDSN.visual.store_layers_scores == 1):
self.scoreTr_layers = myDSN.scoreTr_layers
self.scoreVal_layers = myDSN.scoreVal_layers
if (myDSN.visual.store_layers_soft_error == 1):
self.errorTr_layers = myDSN.errorTr_layers
self.errorVal_layers = myDSN.errorVal_layers
def __init__(self, myDSNBust):
# MSE: Mean Square Error
# Cross-Entropy: NLL of probability (output)
self.base_learner = interface.CSLFN_param(myDSNBust.layers[-1])
self.Lmax = myDSNBust.Lmax
self.nL = myDSNBust.nL
self.alpha = myDSNBust.alpha
self.beta = myDSNBust.beta
self.Inyection = myDSNBust.Inyection
self.Agregation = myDSNBust.Agregation
self.Enphasis = myDSNBust.Enphasis
# Intermediate results info.
self.gammas = myDSNBust.gammas
if (myDSNBust.visual.store_layers_scores == 1):
self.scoreTr_layers = myDSNBust.scoreTr_layers
self.scoreVal_layers = myDSNBust.scoreVal_layers
if (myDSNBust.visual.store_layers_soft_error == 1):
self.errorTr_layers = myDSNBust.errorTr_layers
self.errorVal_layers = myDSNBust.errorVal_layers