lbg#

diffsptk.LBG#: alias of LindeBuzoGrayAlgorithm

class diffsptk.LindeBuzoGrayAlgorithm(order, codebook_size, *, min_data_per_cluster=1, n_iter=100, eps=1e-05, perturb_factor=1e-05, init='mean', metric='none', batch_size=None, seed=None, verbose=False)[source]#

See this page for details. Note that the forward method is not differentiable.

Parameters:

orderint >= 0: The order of the vector, $M$ .
codebook_sizeint >= 1: The target codebook size, $K$ .
min_data_per_clusterint >= 1: The minimum number of data points required in each cluster.
n_iterint >= 1: The number of iterations.
epsfloat >= 0: The convergence threshold.
perturb_factorfloat > 0: The perturbation factor.
init[‘none’, ‘mean’] or torch.Tensor [shape=(1~K, M+1)]: The initialization method for the codebook.
metric[‘none, ‘aic’, ‘bic’]: The metric used for model selection.
batch_sizeint >= 1 or None: The batch size.
seedint or None: The random seed.
verbosebool or int: If 1, shows the distance at each iteration; if 2, shows progress bars.

References

[1]

Y. Linde et al., “An algorithm for vector quantizer design,” IEEE Transactions on Communications, vol. 28, no. 1, pp. 84-95, 1980.

forward(x, return_indices=False)[source]#

Design a codebook using the Linde-Buzo-Gray algorithm.

Parameters:

xTensor [shape=(T, M+1)] or DataLoader: The input vectors or a DataLoader that yields the input vectors.
return_indicesbool: If True, return the codebook indices.

Returns:

codebookTensor [shape=(K, M+1)]: The generated codebook.
indicesTensor [shape=(T,)] (optional): The codebook indices.
distanceTensor [scalar]: The distance between the input vectors and the codebook.

Examples

>>> x = diffsptk.nrand(10, 0)
>>> lbg = diffsptk.LBG(0, 2)
>>> codebook, indices, distance = lbg(x, return_indices=True)
>>> codebook
tensor([[-0.5277],
        [ 0.6747]])
>>> indices
tensor([0, 0, 0, 1, 0, 1, 1, 1, 1, 0])
>>> distance
tensor(0.2331)

transform(x)[source]#

Transform the input vectors using the codebook.

Parameters:

xTensor [shape=(T, M+1)]: The input vectors.

Returns:

xqTensor [shape=(T, M+1)]: The quantized vectors.
indicesTensor [shape=(T,)]: The codebook indices.

Examples

>>> lbg = diffsptk.LBG(0, 2)
>>> torch.save(lbg.state_dict(), "lbg.pt")
>>> lbg.load_state_dict(torch.load("lbg.pt"))
>>> x = diffsptk.nrand(10, 0)
>>> xq, indices = lbg.transform(x)

lbg#

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