Fix divide-by-zero in example

prototorch/functions/initializers.py

@@ -76,7 +76,11 @@ def stratified_mean(x_train, y_train, prototype_distribution, one_hot=True):
 
 
 @register_initializer
-def stratified_random(x_train, y_train, prototype_distribution, one_hot=True):
+def stratified_random(x_train,
+                      y_train,
+                      prototype_distribution,
+                      one_hot=True,
+                      epsilon=1e-7):
     nprotos = torch.sum(prototype_distribution)
     pdim = x_train.shape[1]
     protos = torch.empty(nprotos, pdim)
@@ -89,7 +93,7 @@ def stratified_random(x_train, y_train, prototype_distribution, one_hot=True):
         xl = x_train[matcher]
         rand_index = torch.zeros(1).long().random_(0, xl.shape[0] - 1)
         random_xl = xl[rand_index]
-        protos[i] = random_xl
+        protos[i] = random_xl + epsilon
     plabels = labels_from(prototype_distribution, one_hot=one_hot)
     return protos, plabels
 

prototorch/modules/losses.py

@@ -15,6 +15,6 @@ class GLVQLoss(torch.nn.Module):
 
     def forward(self, outputs, targets):
         distances, plabels = outputs
-        mu = glvq_loss(distances, targets, plabels)
+        mu = glvq_loss(distances, targets, prototype_labels=plabels)
         batch_loss = self.squashing(mu + self.margin, beta=self.beta)
         return torch.sum(batch_loss, dim=0)