1 files changed, 90 insertions, 0 deletions
diff --git a/sem6/com/m3/channel_test.py b/sem6/com/m3/channel_test.py
new file mode 100644
index 0000000..c8f9e85
--- /dev/null
+++ b/sem6/com/m3/channel_test.py
@@ -0,0 +1,90 @@
+import numpy as np
+
+class MatrixEncoder:
+    def ident(dim, value=1):
+        return np.identity(dim) * value
+
+class Vector:
+    def __init__(self, arr):
+        self.arr = arr
+        self.len = len(arr)
+
+    def add(self, other):
+        return self.__class__(self.arr + other.arr)
+
+    def __str__(self):
+        return str(self.arr)
+
+    def print(self, f="{}"):
+        print(f.format(self))
+        return self
+
+    def gaussian(size, mu, sigma):
+        return Vector(sigma * np.random.randn(size) + mu)
+
+
+class BitVector(Vector):
+    def gen_uniform(size):
+        arr = np.random.randint(0, 2, size)
+        return BitVector(arr)
+
+    def to_int(self):
+        reverse = np.flip(self.arr)
+
+        res = 0
+        for (i, b) in enumerate(reverse):
+            res += b * 2**i
+        return res
+
+    def encode(self, matrix):
+        return SymbolVector(matrix[self.to_int()])
+
+    def from_int(index, size=None):
+        bits = []
+        while index > 0:
+            remainder = index % 2
+            index = index // 2
+            bits.insert(0, remainder)
+
+        # Padding if they want
+        if size is not None:
+            missing = max(0, size - len(bits))
+            bits = [0] * missing + bits
+
+        return BitVector(bits)
+
+    def check_if_error(self, other):
+        return np.array_equal(self.arr, other.arr)
+
+class SymbolVector(Vector):
+    def with_noise(self, c):
+        noise = Vector.gaussian(self.len, 0, c)
+        return self.add(noise)
+
+    def decide_and_decode(self, matrix):
+        dim = len(matrix)
+        # Collect all distances in vector
+        dists = np.arange(dim)
+        for (i, vec) in enumerate(matrix):
+            dists[i] = np.dot(self.arr, vec)
+
+        # Find index with smallest
+        index = np.argmax(dists)
+
+        # Convert integer back to bit vector
+        return BitVector.from_int(index, size=int(np.log2(dim)))
+
+encoder = MatrixEncoder.ident(4, np.sqrt(1))
+
+faults = 0
+total = 10000
+for i in range(total):
+    original = BitVector.gen_uniform(2)
+    after = original.encode(encoder) \
+        .with_noise(1) \
+        .decide_and_decode(encoder)
+    if original.check_if_error(after):
+        faults += 1
+
+print(f"Fault percentage {(faults / total) * 100}%")
+