[REFACTOR] Rename LikelihoodRatioLVQ to SLVQ
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@ -74,7 +74,7 @@ For a test sample they return a distribution instead of a class assignment.
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The following two algorihms were presented by :cite:t:`seo2003` .
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The following two algorihms were presented by :cite:t:`seo2003` .
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Every prototypes is a center of a gaussian distribution of its class, generating a mixture model.
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Every prototypes is a center of a gaussian distribution of its class, generating a mixture model.
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.. autoclass:: prototorch.models.probabilistic.LikelihoodRatioLVQ
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.. autoclass:: prototorch.models.probabilistic.SLVQ
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:members:
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:members:
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.. autoclass:: prototorch.models.probabilistic.RSLVQ
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.. autoclass:: prototorch.models.probabilistic.RSLVQ
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@ -19,7 +19,7 @@ from .glvq import (
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)
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)
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from .knn import KNN
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from .knn import KNN
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from .lvq import LVQ1, LVQ21, MedianLVQ
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from .lvq import LVQ1, LVQ21, MedianLVQ
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from .probabilistic import CELVQ, RSLVQ, LikelihoodRatioLVQ
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from .probabilistic import CELVQ, RSLVQ, SLVQ
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from .unsupervised import GrowingNeuralGas, HeskesSOM, KohonenSOM, NeuralGas
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from .unsupervised import GrowingNeuralGas, HeskesSOM, KohonenSOM, NeuralGas
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from .vis import *
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from .vis import *
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@ -61,8 +61,8 @@ class ProbabilisticLVQ(GLVQ):
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return loss
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return loss
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class LikelihoodRatioLVQ(ProbabilisticLVQ):
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class SLVQ(ProbabilisticLVQ):
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"""Learning Vector Quantization based on Likelihood Ratios."""
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"""Soft Learning Vector Quantization."""
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def __init__(self, *args, **kwargs):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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super().__init__(*args, **kwargs)
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self.loss = LossLayer(nllr_loss)
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self.loss = LossLayer(nllr_loss)
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