Fix a bunch of codacy code-style issues

RELEASE.md

@@ -1,9 +1,11 @@
-# Release 0.1.1-dev0
+# ProtoTorch Releases
 
-## Includes
-- Bugfixes.
-- 100% test coverage.
+## Release 0.1.1-dev0
 
-# Release 0.1.0-dev0
+### Includes
+  - Bugfixes.
+  - 100% test coverage.
+
+## Release 0.1.0-dev0
 
 Initial public release of ProtoTorch.

examples/glvq_iris.py

@@ -21,6 +21,7 @@ x_train = scaler.transform(x_train)
 # Define the GLVQ model
 class Model(torch.nn.Module):
     def __init__(self, **kwargs):
+        """GLVQ model."""
         super().__init__()
         self.p1 = Prototypes1D(input_dim=2,
                                prototypes_per_class=1,

prototorch/functions/activations.py

@@ -16,16 +16,17 @@ def register_activation(f):
 @register_activation
 # @torch.jit.script
 def identity(x, beta=torch.tensor([0])):
-    """:math:`f(x) = x`"""
+    """:math:`f(x) = x`."""
     return x
 
 
 @register_activation
 # @torch.jit.script
 def sigmoid_beta(x, beta=torch.tensor([10])):
-    """:math:`f(x) = \\frac{1}{1 + e^{-\\beta x}}`
+    r""":math:`f(x) = \\frac{1}{1 + e^{-\\beta x}}`.
 
     Keyword Arguments:
+    __________________
         beta (float): Parameter :math:`\\beta`
     """
     out = torch.reciprocal(1.0 + torch.exp(-int(beta.item()) * x))
@@ -35,9 +36,10 @@ def sigmoid_beta(x, beta=torch.tensor([10])):
 @register_activation
 # @torch.jit.script
 def swish_beta(x, beta=torch.tensor([10])):
-    """:math:`f(x) = \\frac{x}{1 + e^{-\\beta x}}`
+    r""":math:`f(x) = \\frac{x}{1 + e^{-\\beta x}}`.
 
     Keyword Arguments:
+    __________________
         beta (float): Parameter :math:`\\beta`
     """
     out = x * sigmoid_beta(x, beta=beta)

prototorch/functions/distances.py

@@ -4,7 +4,8 @@ import torch
 
 
 def squared_euclidean_distance(x, y):
-    """Compute the squared Euclidean distance between :math:`x` and :math:`y`.
+    """
+    Compute the squared Euclidean distance between :math:`x` and :math:`y`.
 
     Expected dimension of x is 2.
     Expected dimension of y is 2.
@@ -17,7 +18,8 @@ def squared_euclidean_distance(x, y):
 
 
 def euclidean_distance(x, y):
-    """Compute the Euclidean distance between :math:`x` and :math:`y`.
+    """
+    Compute the Euclidean distance between :math:`x` and :math:`y`.
 
     Expected dimension of x is 2.
     Expected dimension of y is 2.
@@ -28,7 +30,8 @@ def euclidean_distance(x, y):
 
 
 def lpnorm_distance(x, y, p):
-    """Compute :math:`{\\langle x, y \\rangle}_p`.
+    """
+    Compute :math:`{\\langle x, y \\rangle}_p`.
 
     Expected dimension of x is 2.
     Expected dimension of y is 2.
@@ -38,7 +41,8 @@ def lpnorm_distance(x, y, p):
 
 
 def omega_distance(x, y, omega):
-    """Omega distance.
+    """
+    Omega distance.
 
     Compute :math:`{\\langle \\Omega x, \\Omega y \\rangle}_p`
 
@@ -53,7 +57,8 @@ def omega_distance(x, y, omega):
 
 
 def lomega_distance(x, y, omegas):
-    """Localized Omega distance.
+    """
+    Localized Omega distance.
 
     Compute :math:`{\\langle \\Omega_k x, \\Omega_k y_k \\rangle}_p`
 

prototorch/modules/losses.py

@@ -7,7 +7,6 @@ from prototorch.functions.losses import glvq_loss
 
 
 class GLVQLoss(torch.nn.Module):
-    """GLVQ Loss."""
     def __init__(self, margin=0.0, squashing='identity', beta=10, **kwargs):
         super().__init__(**kwargs)
         self.margin = margin