-
sdr.dmc(x: ArrayLike, P: ArrayLike, X: ArrayLike | None =
None
, Y: ArrayLike | None =None
, seed: int | None =None
) NDArray[int_] Passes the input sequence \(x\) through a discrete memoryless channel (DMC) with transition probability matrix \(P\).
- Parameters:¶
- x: ArrayLike¶
The input sequence \(x\) with \(x_i \in \mathcal{X}\).
- P: ArrayLike¶
The \(m \times n\) transition probability matrix \(P\), where \(P_{i,j} = \Pr(Y = y_j | X = x_i)\).
- X: ArrayLike | None =
None
¶ The input alphabet \(\mathcal{X}\) of size \(m\). If
None
, it is assumed that \(\mathcal{X} = \{0, 1, \ldots, m-1\}\).- Y: ArrayLike | None =
None
¶ The output alphabet \(\mathcal{Y}\) of size \(n\). If
None
, it is assumed that \(\mathcal{Y} = \{0, 1, \ldots, n-1\}\).- seed: int | None =
None
¶ The seed for the random number generator. This is passed to
numpy.random.default_rng()
.
- Returns:¶
The output sequence \(y\) with \(y_i \in \mathcal{Y}\).
Examples¶
Define the binary symmetric channel (BSC) with transition probability \(p=0.25\).
In [1]: p = 0.25 In [2]: x = np.random.randint(0, 2, 20); x Out[2]: array([0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1]) In [3]: P = [[1 - p, p], [p, 1 - p]] In [4]: y = sdr.dmc(x, P); y Out[4]: array([0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1]) In [5]: x == y Out[5]: array([ True, True, False, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, False, True])
Define the binary erasure channel (BEC) with erasure probability \(p=0.25\).
In [6]: p = 0.25 In [7]: x = np.random.randint(0, 2, 20); x Out[7]: array([0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1]) In [8]: P = [[1 - p, 0, p], [0, 1 - p, p]] # Specify the erasure as -1 In [9]: y = sdr.dmc(x, P, Y=[0, 1, -1]); y Out[9]: array([ 0, 1, -1, 0, 1, -1, 1, 1, 1, -1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1]) In [10]: x == y Out[10]: array([ True, True, False, True, True, False, True, True, True, False, True, True, True, True, False, False, True, True, True, True])