better naming convention

neural_filters/__init__.py

-from .NeuralFilterCell import *
-from .NeuralFilter1P import *
-from .NeuralFilter2R import *
-from .NeuralFilter2CC import *
-from .NeuralFilter2CD import *
-from .LogMSELoss import *
+from .neural_filter import *
+from .neural_filter_1L import *
+from .neural_filter_2R import *
+from .neural_filter_2CC import *
+from .neural_filter_2CD import *
+from .log_loss import *

neural_filters/NeuralFilterCell.py → neural_filters/neural_filter.py

@@ -31,7 +31,7 @@ from torch.nn import functional as F
 import numpy as np
 
 
-class NeuralFilterCell(torch.nn.Module):
+class NeuralFilter(torch.nn.Module):
     """
     A trainable first-order all-pole filter :math:`\\frac{1}{1 - P z^{-1}}`
 
@@ -39,7 +39,7 @@ class NeuralFilterCell(torch.nn.Module):
     """
 
     def __init__(self, hidden_size):
-        super(NeuralFilterCell, self).__init__()
+        super(NeuralFilter, self).__init__()
 
         self.hidden_size = hidden_size
 

neural_filters/NeuralFilter1P.py → neural_filters/neural_filter_1L.py

@@ -29,9 +29,9 @@ import torch
 from torch.nn import Parameter
 from torch.nn import functional as F
 import math
-from . import NeuralFilterCell
+from . import NeuralFilter
 
-class NeuralFilter1P(NeuralFilterCell):
+class NeuralFilter1L(NeuralFilter):
     """
     A trainable first-order all-pole filter :math:`\\frac{K}{1 - P z^{-1}}` with bias on the input
 
@@ -40,7 +40,7 @@ class NeuralFilter1P(NeuralFilterCell):
     """
 
     def __init__(self, input_size, hidden_size):
-        super(NeuralFilter1P, self).__init__(hidden_size)
+        super(NeuralFilter1L, self).__init__(hidden_size)
 
         self.input_size = input_size
 
@@ -58,7 +58,7 @@ class NeuralFilter1P(NeuralFilterCell):
         for weight in self.parameters():
             weight.data.uniform_(-stdv, stdv)
 
-        super(NeuralFilter1P, self).reset_parameters(init)
+        super(NeuralFilter1L, self).reset_parameters(init)
 
     def check_forward_input(self, input):
         if input.size(-1) != self.input_size:
@@ -68,5 +68,5 @@ class NeuralFilter1P(NeuralFilterCell):
 
     def step(self, input, hidden):
         in_gate = F.linear(input, self.weight_in, self.bias_in)
-        next = super(NeuralFilter1P, self).step(in_gate, hidden)
+        next = super(NeuralFilter1L, self).step(in_gate, hidden)
         return next

neural_filters/NeuralFilter2CD.py → neural_filters/neural_filter_2CD.py

@@ -25,7 +25,7 @@ along with neural_filters. If not, see <http://www.gnu.org/licenses/>.
 
 """
 
-from . import NeuralFilterCell
+from . import NeuralFilter
 
 import torch
 import numpy as np
@@ -42,7 +42,7 @@ class NeuralFilter2CD (torch.nn.Module):
 
         self.hidden_size = hidden_size
 
-        self.cell = NeuralFilterCell(self.hidden_size)
+        self.cell = NeuralFilter(self.hidden_size)
 
     def reset_parameters(self, init=None):
         self.cell.reset_parameters(init)

neural_filters/NeuralFilter2R.py → neural_filters/neural_filter_2R.py

@@ -25,7 +25,7 @@ along with neural_filters. If not, see <http://www.gnu.org/licenses/>.
 
 """
 
-from . import NeuralFilterCell
+from . import NeuralFilter
 
 import torch
 import numpy as np
@@ -42,8 +42,8 @@ class NeuralFilter2R (torch.nn.Module):
 
         self.hidden_size = hidden_size
 
-        self.first_cell = NeuralFilterCell(self.hidden_size)
-        self.second_cell = NeuralFilterCell(self.hidden_size)
+        self.first_cell = NeuralFilter(self.hidden_size)
+        self.second_cell = NeuralFilter(self.hidden_size)
 
         self.reset_parameters((-0.5, 0.5))