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authorJulian T <julian@jtle.dk>2021-03-11 13:18:35 +0100
committerJulian T <julian@jtle.dk>2021-03-11 13:18:35 +0100
commit1455ccc4eb1047aba2a50b8c2c3a5bd3887d0226 (patch)
tree6734cab7074f82f76919b7b03ead87e588b62e39
parentb7423cb043cfa4c9e88eeb345c3bb8dfd82aafc2 (diff)
Do channel simulation assignment for Communications
-rw-r--r--sem6/com/m3/channel_test.py90
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}%")
+