lbg#

diffsptk.KMeans#: alias of LindeBuzoGrayAlgorithm

class diffsptk.LindeBuzoGrayAlgorithm(order, codebook_size, min_data_per_cluster=1, n_iter=100, eps=1e-05, perturb_factor=1e-05, verbose=False)[source]#

See this page for details. This module is not differentiable.

orderint >= 0 [scalar]: Order of vector.
codebook_sizeint >= 1 [scalar]: Target codebook size, must be power of two.
min_data_per_clusterint >= 1 [scalar]: Minimum number of data points in a cluster.
n_iterint >= 1 [scalar]: Number of iterations.
epsfloat >= 0 [scalar]: Convergence threshold.
perturb_factorfloat > 0 [scalar]: Perturbation factor.
verbosebool [scalar]: If True, print progress.

forward(x)[source]#

Design a codebook.

Parameters:

xTensor [shape=(…, M+1)]: Input vectors.

Returns:

codebookTensor [shape=(K, M+1)]: Codebook.
distanceTensor [scalar]: Distance.

Examples

>>> x = diffsptk.nrand(10, 0)
>>> lbg = diffsptk.KMeans(0, 2)
>>> codebook, distance = lbg(x)
>>> codebook
tensor([[-1.3889],
        [ 0.6275]])
>>> distance
tensor(0.2489)