Add probabilistic losses

Based on Soft LVQ paper by Seo and Obermayer

prototorch/functions/losses.py

@@ -57,3 +57,37 @@ def lvq21_loss(distances, target_labels, prototype_labels):
     mu = dp - dm
 
     return mu
+
+
+# Probabilistic
+def log_likelihood_ratio_loss(probabilities, target, prototype_labels):
+    uniques = prototype_labels.unique(sorted=True).tolist()
+    labels = target.tolist()
+
+    key_val = {key: val for key, val in zip(uniques, range(len(uniques)))}
+    target_indices = torch.LongTensor(list(map(key_val.get, labels)))
+
+    whole_probability = probabilities.sum(dim=1)
+    correct_probability = probabilities[torch.arange(len(probabilities)),
+                                        target_indices]
+    wrong_probability = whole_probability - correct_probability
+
+    likelihood = correct_probability / wrong_probability
+    log_likelihood = torch.log(likelihood)
+    return log_likelihood
+
+
+def robust_soft_loss(probabilities, target, prototype_labels):
+    uniques = prototype_labels.unique(sorted=True).tolist()
+    labels = target.tolist()
+
+    key_val = {key: val for key, val in zip(uniques, range(len(uniques)))}
+    target_indices = torch.LongTensor(list(map(key_val.get, labels)))
+
+    whole_probability = probabilities.sum(dim=1)
+    correct_probability = probabilities[torch.arange(len(probabilities)),
+                                        target_indices]
+
+    likelihood = correct_probability / whole_probability
+    log_likelihood = torch.log(likelihood)
+    return log_likelihood