Added requirements.txt and instructions for running to README

sw/README.md

@@ -2,4 +2,20 @@
 
 ## Software
 
-`TODO`
+### Create a python virtual environment
+
+In the root directory of this repository run
+
+```bash
+$ python3 -m venv venv
+$ source venv/bin/activate
+$ pip3 install -r requirements.txt
+```
+
+### Run the unit tests
+
+In the root directory of this repository run
+
+```bash
+$ python -m unittest discover test
+```

sw/decoders/proximal.py

@@ -6,7 +6,7 @@ class ProximalDecoder:
     """Class implementing the Proximal Decoding algorithm. See "Proximal Decoding for LDPC Codes" by Tadashi
     Wadayama, and Satoshi Takabe.
     """
-    def __init__(self, H: np.array, K: int = 10, step_size: float = 0.1, gamma: float = 0.05):
+    def __init__(self, H: np.array, K: int = 10, step_size: float = 0.01, gamma: float = 0.05):
         """Construct a new ProximalDecoder Object.
 
         :param H: Parity Check Matrix

sw/main.py

@@ -1,6 +1,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
+import pandas as pd
 
 from decoders import proximal
 from decoders import utility
@@ -25,10 +26,13 @@ def main():
 
     print(f"Simulating with c = {c}")
 
-    decoder = proximal.ProximalDecoder(H, K=1000)
+    decoder = proximal.ProximalDecoder(H, K=100)
     SNRs, BERs = utility.test_decoder(decoder, c, N=100)
 
-    plt.stem(SNRs, BERs)
+    data = pd.DataFrame({"SNR": SNRs, "BER": BERs})
+
+    ax = sns.lineplot(data=data, x="SNR", y="BER")
+    ax.set_ylim([0, 1])
     plt.show()
 
 

sw/requirements.txt

@@ -0,0 +1,5 @@
+matplotlib==3.6.2
+numpy==1.23.4
+pandas==1.5.1
+seaborn==0.12.1
+tqdm==4.64.1