Add many more solutions and notes

1 files changed, 0 insertions, 90 deletions

diff --git a/sem6/com/m3/channel_test.py b/sem6/com/m3/channel_test.py
deleted file mode 100644
index c8f9e85..0000000
--- a/sem6/com/m3/channel_test.py
+++ /dev/null
@@ -1,90 +0,0 @@
-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}%")
-