feat: distribute GMLVQ into mixins

prototorch/y_arch/library/__init__.py

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+from .glvq import GLVQ
+
+__all__ = [
+    "GLVQ",
+]

prototorch/y_arch/library/glvq.py

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+from dataclasses import dataclass
+
+from prototorch.y_arch import (
+    SimpleComparisonMixin,
+    SingleLearningRateMixin,
+    SupervisedArchitecture,
+    WTACompetitionMixin,
+)
+from prototorch.y_arch.architectures.loss import GLVQLossMixin
+
+
+class GLVQ(
+        SupervisedArchitecture,
+        SimpleComparisonMixin,
+        GLVQLossMixin,
+        WTACompetitionMixin,
+        SingleLearningRateMixin,
+):
+    """
+    Generalized Learning Vector Quantization (GLVQ)
+
+    A GLVQ architecture that uses the winner-take-all strategy and the GLVQ loss.
+    """
+
+    @dataclass
+    class HyperParameters(
+            SimpleComparisonMixin.HyperParameters,
+            SingleLearningRateMixin.HyperParameters,
+            GLVQLossMixin.HyperParameters,
+            WTACompetitionMixin.HyperParameters,
+            SupervisedArchitecture.HyperParameters,
+    ):
+        """
+        No hyperparameters.
+        """

prototorch/y_arch/library/gmlvq.py

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+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Callable
+
+import torch
+from prototorch.core.distances import omega_distance
+from prototorch.y_arch import (
+    GLVQLossMixin,
+    MultipleLearningRateMixin,
+    OmegaComparisonMixin,
+    SupervisedArchitecture,
+    WTACompetitionMixin,
+)
+
+
+class GMLVQ(
+        SupervisedArchitecture,
+        OmegaComparisonMixin,
+        GLVQLossMixin,
+        WTACompetitionMixin,
+        MultipleLearningRateMixin,
+):
+    """
+    Generalized Matrix Learning Vector Quantization (GMLVQ)
+
+    A GMLVQ architecture that uses the winner-take-all strategy and the GLVQ loss.
+    """
+    # HyperParameters
+    # ----------------------------------------------------------------------------------------------------
+    @dataclass
+    class HyperParameters(
+            MultipleLearningRateMixin.HyperParameters,
+            OmegaComparisonMixin.HyperParameters,
+            GLVQLossMixin.HyperParameters,
+            WTACompetitionMixin.HyperParameters,
+            SupervisedArchitecture.HyperParameters,
+    ):
+        """
+        comparison_fn: The comparison / dissimilarity function to use. Override Default: omega_distance.
+        comparison_args: Keyword arguments for the comparison function. Override Default: {}.
+        """
+        comparison_fn: Callable = omega_distance
+        comparison_args: dict = field(default_factory=lambda: dict())
+        optimizer: type[torch.optim.Optimizer] = torch.optim.Adam
+
+        lr: dict = field(default_factory=lambda: dict(
+            components_layer=0.1,
+            _omega=0.5,
+        ))