komm.ReflectedLabeling
Reflected (Gray) binary labeling. It is a binary labeling in which integer $i \in [0 : 2^m)$ is mapped to its Gray code representation.
matrix
NDArray[integer]
cached
property
The labeling matrix $\mathbf{Q}$.
Examples:
>>> labeling = komm.ReflectedLabeling(2)
>>> labeling.matrix
array([[0, 0],
[0, 1],
[1, 1],
[1, 0]])
num_bits
int
property
The number $m$ of bits per index of the labeling.
Examples:
>>> labeling = komm.ReflectedLabeling(2)
>>> labeling.num_bits
2
cardinality
int
property
The cardinality $2^m$ of the labeling.
Examples:
>>> labeling = komm.ReflectedLabeling(2)
>>> labeling.cardinality
4
inverse_mapping
dict[tuple[int, ...], int]
cached
property
The inverse mapping of the labeling. It is a dictionary that maps each binary tuple to the corresponding index.
Examples:
>>> labeling = komm.ReflectedLabeling(2)
>>> labeling.inverse_mapping
{(0, 0): 0, (0, 1): 1, (1, 1): 2, (1, 0): 3}
indices_to_bits()
Returns the binary representation of the given indices.
Parameters:
-
indices
(ArrayLike
) –The indices to be converted to bits. Must be an array of integers in $[0:2^m)$.
Returns:
-
bits
(NDArray[integer]
) –The binary representations of the given indices. Has the same shape as
indices
, but with the last dimension expanded by a factor of $m$.
Examples:
>>> labeling = komm.ReflectedLabeling(2)
>>> labeling.indices_to_bits([2, 0])
array([1, 1, 0, 0])
>>> labeling.indices_to_bits([[2, 0], [3, 3]])
array([[1, 1, 0, 0],
[1, 0, 1, 0]])
bits_to_indices()
Returns the indices corresponding to a given sequence of bits.
Parameters:
-
bits
(ArrayLike
) –The bits to be converted to indices. Must be an array with elements in $\mathbb{B}$ whose last dimension is a multiple $m$.
Returns:
-
indices
(NDArray[integer]
) –The indices corresponding to the given bits. Has the same shape as
bits
, but with the last dimension contracted by a factor of $m$.
Examples:
>>> labeling = komm.ReflectedLabeling(2)
>>> labeling.bits_to_indices([1, 1, 0, 0])
array([2, 0])
>>> labeling.bits_to_indices([[1, 1, 0, 0], [1, 0, 1, 0]])
array([[2, 0],
[3, 3]])
marginalize()
Marginalize metrics over the bits of the labeling. The metrics may represent likelihoods or probabilities, for example. The marginalization is done by computing the L-values of the bits, which are defined as $$ L(\mathtt{b}_i) = \log \frac{\Pr[\mathtt{b}_i = 0]}{\Pr[\mathtt{b}_i = 1]}. $$
Parameters:
-
metrics
(ArrayLike
) –The metrics for each index of the labeling. Must be an array whose last dimension is a multiple of $2^m$.
Returns:
-
lvalues
(NDArray[floating]
) –The marginalized metrics over the bits of the labeling. Has the same shape as
metrics
, but with the last dimension changed by a factor of $m / 2^m$.
Examples:
>>> labeling = komm.ReflectedLabeling(2)
>>> labeling.marginalize([0.1, 0.2, 0.3, 0.4, 0.25, 0.25, 0.25, 0.25])
array([-0.84729786, 0. , 0. , 0. ])
>>> labeling.marginalize([[0.1, 0.2, 0.3, 0.4], [0.25, 0.25, 0.25, 0.25]])
array([[-0.84729786, 0. ],
[ 0. , 0. ]])