Removed references to the R-Matrix; Comment changes

sw/decoders/naive_soft_decision.py

@@ -10,13 +10,11 @@ class SoftDecisionDecoder:
     one with the largest correlation.
     """
 
-    # TODO: Is 'R' actually called 'decoding matrix'?
     def __init__(self, G: np.array, H: np.array):
         """Construct a new SoftDecisionDecoder object.
 
         :param G: Generator matrix
         :param H: Parity check matrix
-        :param R: Decoding matrix
         """
         self._G = G
         self._H = H

sw/decoders/proximal.py

@@ -7,13 +7,11 @@ class ProximalDecoder:
     by Tadashi Wadayama, and Satoshi Takabe.
     """
 
-    # TODO: Is 'R' actually called 'decoding matrix'?
     def __init__(self, H: np.array, K: int = 100, step_size: float = 0.1,
                  gamma: float = 0.05, eta: float = 1.5):
         """Construct a new ProximalDecoder Object.
 
         :param H: Parity Check Matrix
-        :param R: Decoding matrix
         :param K: Max number of iterations to perform when decoding
         :param step_size: Step size for the gradient descent process
         :param gamma: Positive constant. Arises in the approximation of the prior PDF
@@ -54,7 +52,7 @@ class ProximalDecoder:
         """Project a vector onto [-eta, eta]^n in order to avoid numerical instability.
         Detailed in 3.2, p. 3 (Equation (15)).
 
-        :param x:
+        :param x: Vector to project
         :return: x clipped to [-eta, eta]^n
         """
         return np.clip(x, -self._eta, self._eta)

sw/test/test_utility.py

@@ -22,7 +22,7 @@ class CountBitErrorsTestCase(unittest.TestCase):
         self.assertEqual(simulations.count_bit_errors(d3, y_hat3), 4)
 
 
-# TODO: Is this correct?
+# TODO: Rewrite tests for new SNR calculation
 class NoiseAmpFromSNRTestCase(unittest.TestCase):
     """Test case for noise amplitude calculation."""
 

sw/utility/codes.py

@@ -172,6 +172,7 @@ Gs = {'Hamming_7_4': np.array([[1, 0, 0, 0, 0, 1, 1],
                              [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1],
                              [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1]]),
 
+      # TODO: Fix this. This code should be systematic
       'BCH_63_45': np.array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1],
                              [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0],
                              [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0],
@@ -244,15 +245,3 @@ def get_systematic_H(G: np.array) -> np.array:
     H[:, n:] = np.identity(k-n)
 
     return H
-
-
-def get_systematic_R(G: np.array) -> np.array:
-    n, k = G.shape
-
-    I = G[:, :n]
-    assert np.array_equal(I, np.identity(n))
-
-    R = np.zeros(shape=G.shape)
-    R[:, :n] = np.identity(n)
-
-    return R

sw/utility/encoders.py

@@ -1,26 +0,0 @@
-import numpy as np
-
-
-# TODO: Unify the interface regarding [0, 1]^n and [-1, 1]^n
-# TODO: Should the encoder be responsible for mapping the message from [0, 1]^n to [-1, 1]^n?
-#  (ie. should the encoder perform modulation?)
-class Encoder:
-    """Class implementing an encoder for block codes.
-    """
-    def __init__(self, G: np.array):
-        """Construct a new Encoder object.
-
-        :param G: Generator matrix
-        """
-        self._G = G
-
-    def encode(self, d: np.array) -> np.array:
-        """Map a given dataword onto the corresponding codeword.
-
-        The returned codeword is mapped from [0, 1]^n onto [-1, 1]^n.
-
-        :param d: Dataword (element of [0, 1]^n)
-        :return: Codeword (already element of [-1, 1]^n)
-        """
-        # TODO: (0 -> 1), (1 -> -1); Not (0 -> -1), (-1 -> 1)
-        return np.dot(d, self._G) * 2 - 1