[WIP] Add SOM
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@ -26,8 +26,8 @@ be available for use in your Python environment as `prototorch.models`.
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- Generalized Learning Vector Quantization (GLVQ)
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- Generalized Relevance Learning Vector Quantization (GRLVQ)
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- Generalized Matrix Learning Vector Quantization (GMLVQ)
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- Localized and Generalized Matrix Learning Vector Quantization (LGMLVQ)
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- Limited-Rank Matrix Learning Vector Quantization (LiRaMLVQ)
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- Localized and Generalized Matrix Learning Vector Quantization (LGMLVQ)
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- Learning Vector Quantization Multi-Layer Network (LVQMLN)
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- Siamese GLVQ
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- Cross-Entropy Learning Vector Quantization (CELVQ)
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@ -43,6 +43,7 @@ be available for use in your Python environment as `prototorch.models`.
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- Classification-By-Components Network (CBC)
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- Learning Vector Quantization 2.1 (LVQ2.1)
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- Self-Organizing-Map (SOM)
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## Planned models
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112
examples/ksom_colors.py
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112
examples/ksom_colors.py
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@ -0,0 +1,112 @@
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"""Kohonen Self Organizing Map."""
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import argparse
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import prototorch as pt
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import pytorch_lightning as pl
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import torch
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from matplotlib import pyplot as plt
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def hex_to_rgb(hex_values):
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for v in hex_values:
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v = v.lstrip('#')
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lv = len(v)
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c = [int(v[i:i + lv // 3], 16) for i in range(0, lv, lv // 3)]
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yield c
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def rgb_to_hex(rgb_values):
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for v in rgb_values:
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c = "%02x%02x%02x" % tuple(v)
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yield c
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class Vis2DColorSOM(pl.Callback):
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def __init__(self, data, title="ColorSOMe", pause_time=0.1):
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super().__init__()
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self.title = title
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self.fig = plt.figure(self.title)
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self.data = data
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self.pause_time = pause_time
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def on_epoch_end(self, trainer, pl_module):
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ax = self.fig.gca()
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ax.cla()
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ax.set_title(self.title)
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h, w = pl_module._grid.shape[:2]
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protos = pl_module.prototypes.view(h, w, 3)
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ax.imshow(protos)
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# Overlay color names
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d = pl_module.compute_distances(self.data)
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wp = pl_module.predict_from_distances(d)
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for i, iloc in enumerate(wp):
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plt.text(iloc[1],
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iloc[0],
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cnames[i],
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ha="center",
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va="center",
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bbox=dict(facecolor="white", alpha=0.5, lw=0))
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plt.pause(self.pause_time)
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if __name__ == "__main__":
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# Command-line arguments
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parser = argparse.ArgumentParser()
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parser = pl.Trainer.add_argparse_args(parser)
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args = parser.parse_args()
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# Reproducibility
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pl.utilities.seed.seed_everything(seed=42)
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# Prepare the data
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hex_colors = [
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"#000000", "#0000ff", "#00007f", "#1f86ff", "#5466aa", "#997fff",
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"#00ff00", "#ff0000", "#00ffff", "#ff00ff", "#ffff00", "#ffffff",
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"#545454", "#7f7f7f", "#a8a8a8"
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]
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cnames = [
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"black", "blue", "darkblue", "skyblue", "greyblue", "lilac", "green",
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"red", "cyan", "violet", "yellow", "white", "darkgrey", "mediumgrey",
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"lightgrey"
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]
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colors = list(hex_to_rgb(hex_colors))
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data = torch.Tensor(colors) / 255.0
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train_ds = torch.utils.data.TensorDataset(data)
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train_loader = torch.utils.data.DataLoader(train_ds, batch_size=8)
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# Hyperparameters
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hparams = dict(
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shape=(18, 32),
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alpha=1.0,
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sigma=3,
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lr=0.1,
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)
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# Initialize the model
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model = pt.models.KohonenSOM(
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hparams,
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prototype_initializer=pt.components.Random(3),
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)
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# Compute intermediate input and output sizes
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model.example_input_array = torch.zeros(4, 3)
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# Model summary
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print(model)
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# Callbacks
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vis = Vis2DColorSOM(data=data)
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# Setup trainer
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trainer = pl.Trainer.from_argparse_args(
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args,
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max_epochs=300,
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callbacks=[vis],
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weights_summary="full",
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)
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# Training loop
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trainer.fit(model, train_loader)
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@ -20,7 +20,7 @@ from .glvq import (
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from .knn import KNN
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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 .unsupervised import GrowingNeuralGas, NeuralGas
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from .unsupervised import GrowingNeuralGas, HeskesSOM, KohonenSOM, NeuralGas
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from .vis import *
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__version__ = "0.1.7"
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@ -1,25 +1,76 @@
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"""Unsupervised prototype learning algorithms."""
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import logging
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import warnings
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import prototorch as pt
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import pytorch_lightning as pl
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import torch
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import torchmetrics
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from prototorch.components import Components, LabeledComponents
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from prototorch.components.initializers import ZerosInitializer
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from prototorch.functions.competitions import knnc
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from prototorch.functions.distances import euclidean_distance
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from prototorch.functions.competitions import wtac
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from prototorch.functions.distances import squared_euclidean_distance
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from prototorch.functions.helper import get_flat
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from prototorch.modules import LambdaLayer
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from prototorch.modules.losses import NeuralGasEnergy
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from pytorch_lightning.callbacks import Callback
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from .abstract import UnsupervisedPrototypeModel
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from .abstract import NonGradientMixin, UnsupervisedPrototypeModel
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from .callbacks import GNGCallback
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from .extras import ConnectionTopology
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class KohonenSOM(NonGradientMixin, UnsupervisedPrototypeModel):
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"""Kohonen Self-Organizing-Map.
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TODO Allow non-2D grids
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"""
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def __init__(self, hparams, **kwargs):
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h, w = hparams.get("shape")
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# Ignore `num_prototypes`
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hparams["num_prototypes"] = h * w
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distance_fn = kwargs.pop("distance_fn", squared_euclidean_distance)
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super().__init__(hparams, distance_fn=distance_fn, **kwargs)
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# Hyperparameters
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self.save_hyperparameters(hparams)
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# Default hparams
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self.hparams.setdefault("alpha", 0.3)
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self.hparams.setdefault("sigma", max(h, w) / 2.0)
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# Additional parameters
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x, y = torch.arange(h), torch.arange(w)
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grid = torch.stack(torch.meshgrid(x, y), dim=-1)
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self.register_buffer("_grid", grid)
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def predict_from_distances(self, distances):
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grid = self._grid.view(-1, 2)
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wp = wtac(distances, grid)
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return wp
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def training_step(self, train_batch, batch_idx):
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# x = train_batch
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# TODO Check if the batch has labels
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x = train_batch[0]
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d = self.compute_distances(x)
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wp = self.predict_from_distances(d)
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grid = self._grid.view(-1, 2)
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gd = squared_euclidean_distance(wp, grid)
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nh = torch.exp(-gd / self.hparams.sigma**2)
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protos = self.proto_layer.components
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diff = x.unsqueeze(dim=1) - protos
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delta = self.hparams.lr * self.hparams.alpha * nh.unsqueeze(-1) * diff
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updated_protos = protos + delta.sum(dim=0)
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self.proto_layer.load_state_dict({"_components": updated_protos},
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strict=False)
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def extra_repr(self):
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return f"(grid): (shape: {tuple(self._grid.shape)})"
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class HeskesSOM(UnsupervisedPrototypeModel):
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def __init__(self, hparams, **kwargs):
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super().__init__(hparams, **kwargs)
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def training_step(self, train_batch, batch_idx):
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# TODO Implement me!
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raise NotImplementedError()
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class NeuralGas(UnsupervisedPrototypeModel):
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def __init__(self, hparams, **kwargs):
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super().__init__(hparams, **kwargs)
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