Small changes
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@ -28,12 +28,12 @@ The plugin should then be available for use in your Python environment as
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- Generalized Relevance Learning Vector Quantization (GRLVQ)
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- Generalized Relevance Learning Vector Quantization (GRLVQ)
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- Generalized Matrix Learning Vector Quantization (GMLVQ)
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- Generalized Matrix Learning Vector Quantization (GMLVQ)
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- Limited-Rank Matrix Learning Vector Quantization (LiRaMLVQ)
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- Limited-Rank Matrix Learning Vector Quantization (LiRaMLVQ)
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- Learning Vector Quantization Multi-Layer Network (LVQMLN)
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- Siamese GLVQ
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- Siamese GLVQ
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- Neural Gas (NG)
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- Neural Gas (NG)
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## Work in Progress
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## Work in Progress
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- Learning Vector Quantization Multi-Layer Network (LVQMLN)
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- Classification-By-Components Network (CBC)
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- Classification-By-Components Network (CBC)
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- Learning Vector Quantization 2.1 (LVQ2.1)
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- Learning Vector Quantization 2.1 (LVQ2.1)
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@ -33,7 +33,9 @@ if __name__ == "__main__":
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# Callbacks
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# Callbacks
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dvis = pt.models.VisCBC2D(data=(x_train, y_train),
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dvis = pt.models.VisCBC2D(data=(x_train, y_train),
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title="CBC Iris Example")
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title="CBC Iris Example",
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resolution=300,
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axis_off=True)
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# Setup trainer
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# Setup trainer
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trainer = pl.Trainer(
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trainer = pl.Trainer(
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@ -30,7 +30,7 @@ if __name__ == "__main__":
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noise=1e-1))
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noise=1e-1))
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# Callbacks
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# Callbacks
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vis = pt.models.VisGLVQ2D(train_ds, show_last_only=True, block=True)
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vis = pt.models.VisGLVQ2D(train_ds, show_last_only=True)
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snan = StopOnNaN(model.proto_layer.components)
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snan = StopOnNaN(model.proto_layer.components)
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# Setup trainer
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# Setup trainer
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