Refactor prototypes module and begin documentation

prototorch/modules/prototypes.py

@@ -7,16 +7,65 @@ import torch
 from prototorch.functions.initializers import get_initializer
 
 
-class Prototypes1D(torch.nn.Module):
+class _Prototypes(torch.nn.Module):
+    """Abstract prototypes class."""
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def _check_prototype_distribution(self):
+        if 0 in self.prototype_distribution:
+            warnings.warn('Are you sure about the 0 in '
+                          '`prototype_distribution`?')
+
+    def extra_repr(self):
+        return f'prototypes.shape: {tuple(self.prototypes.shape)}'
+
+
+class Prototypes1D(_Prototypes):
+    r"""Create a learnable set of one-dimensional prototypes.
+
+    TODO Complete this doc-string
+
+    Kwargs:
+        prototypes_per_class: number of prototypes to use per class.
+            Default: ``1``
+        prototype_initializer: prototype initializer.
+            Default: ``'ones'``
+        prototype_distribution: prototype distribution vector.
+            Default: ``None``
+        input_dim: dimension of the incoming data.
+        nclasses: number of classes.
+        data: If set to ``None``, data-dependent initializers will be ignored.
+            Default: ``None``
+
+    Shape:
+        - Input: :math:`(N, H_{in})`
+            where :math:`H_{in} = \text{input_dim}`.
+        - Output: :math:`(N, H_{out})`
+            where :math:`H_{out} = \text{total_prototypes}`.
+
+    Attributes:
+        prototypes: the learnable weights of the module of shape
+            :math:`(\text{total_prototypes}, \text{prototype_dimension})`.
+        prototype_labels: the non-learnable labels of the prototypes.
+
+    Examples::
+
+        >>> p = Prototypes1D(input_dim=20, nclasses=10)
+        >>> input = torch.randn(128, 20)
+        >>> output = m(input)
+        >>> print(output.size())
+        torch.Size([20, 10])
+    """
     def __init__(self,
                  prototypes_per_class=1,
-                 prototype_distribution=None,
                  prototype_initializer='ones',
+                 prototype_distribution=None,
                  data=None,
                  dtype=torch.float32,
                  **kwargs):
 
-        # Accept PyTorch tensors, but convert to python lists before processing
+        # Convert torch tensors to python lists before processing
         if torch.is_tensor(prototype_distribution):
             prototype_distribution = prototype_distribution.tolist()
 
@@ -76,6 +125,8 @@ class Prototypes1D(torch.nn.Module):
         with torch.no_grad():
             self.prototype_distribution = torch.tensor(prototype_distribution)
 
+        self._check_prototype_distribution()
+
         self.prototype_initializer = get_initializer(prototype_initializer)
         prototypes, prototype_labels = self.prototype_initializer(
             x_train,