[BUGFIX] Fix RSLVQ

prototorch/models/abstract.py

@@ -4,6 +4,10 @@ from torch.optim.lr_scheduler import ExponentialLR
 
 
 class AbstractPrototypeModel(pl.LightningModule):
+    @property
+    def num_prototypes(self):
+        return len(self.proto_layer.components)
+
     @property
     def prototypes(self):
         return self.proto_layer.components.detach().cpu()

prototorch/models/probabilistic.py

@@ -3,7 +3,7 @@
 import torch
 from prototorch.functions.competitions import stratified_sum
 from prototorch.functions.losses import log_likelihood_ratio_loss, robust_soft_loss
-from prototorch.functions.transform import gaussian
+from prototorch.functions.transforms import gaussian
 
 from .glvq import GLVQ
 
@@ -15,24 +15,22 @@ class ProbabilisticLVQ(GLVQ):
         self.conditional_distribution = gaussian
         self.rejection_confidence = rejection_confidence
 
-    def predict(self, x):
-        probabilities = self.forward(x)
-        confidence, prediction = torch.max(probabilities, dim=1)
-        prediction[confidence < self.rejection_confidence] = -1
-        return prediction
-
     def forward(self, x):
         distances = self._forward(x)
         conditional = self.conditional_distribution(distances,
                                                     self.hparams.variance)
-        prior = 1.0 / torch.Tensor(self.proto_layer.distribution).sum().item()
+        prior = (1. / self.num_prototypes) * torch.ones(self.num_prototypes)
         posterior = conditional * prior
-
-        plabels = torch.LongTensor(self.proto_layer.component_labels)
-        y_pred = stratified_sum(posterior.T, plabels)
-
+        plabels = self.proto_layer._labels
+        y_pred = stratified_sum(posterior, plabels)
         return y_pred
 
+    def predict(self, x):
+        y_pred = self.forward(x)
+        confidence, prediction = torch.max(y_pred, dim=1)
+        prediction[confidence < self.rejection_confidence] = -1
+        return prediction
+
     def training_step(self, batch, batch_idx, optimizer_idx=None):
         X, y = batch
         out = self.forward(X)