Removed TODOs; Formatting; Changed test_decoder() default SNR array

sw/decoders/maximum_likelihood.py

@@ -3,7 +3,6 @@ import numpy as np
 import itertools
 
 
-# TODO: Unify the interface regarding [0, 1]^n and [-1, 1]^n
 class MLDecoder:
     """This class naively implements a soft decision decoder. The decoder calculates
     the correlation between the received signal and each codeword and then chooses the

sw/decoders/proximal.py

@@ -1,7 +1,6 @@
 import numpy as np
 
 
-# TODO: Unify the interface regarding [0, 1]^n and [-1, 1]^n
 class ProximalDecoder:
     """Class implementing the Proximal Decoding algorithm. See "Proximal Decoding for LDPC Codes"
     by Tadashi Wadayama, and Satoshi Takabe.

sw/utility/codes.py

@@ -225,7 +225,6 @@ Gs = {'Hamming_7_4': np.array([[1, 0, 0, 0, 0, 1, 1],
 #
 
 
-# TODO: Are n and k switched around or correct?
 def get_systematic_H(G: np.array) -> np.array:
     """Compute the H matrix for a systematic code.
 
@@ -239,8 +238,8 @@ def get_systematic_H(G: np.array) -> np.array:
 
     P = G[:, k:]
 
-    H = np.zeros(shape=(n-k, k + n-k))
+    H = np.zeros(shape=(n - k, k + n - k))
     H[:, :k] = P.T
-    H[:, k:] = np.identity(n-k)
+    H[:, k:] = np.identity(n - k)
 
     return H

sw/utility/simulations.py

@@ -20,7 +20,7 @@ def count_bit_errors(d: np.array, d_hat: np.array) -> int:
 
 def test_decoder(x: np.array,
                  decoder: typing.Any,
-                 SNRs: typing.Sequence[float] = np.linspace(1, 4, 7),
+                 SNRs: typing.Sequence[float] = np.linspace(1, 7, 7),
                  target_bit_errors: int = 100,
                  N_max: int = 10000) \
         -> typing.Tuple[np.array, np.array]: