wht#

diffsptk.WHT#: alias of WalshHadamardTransform

class diffsptk.WalshHadamardTransform(wht_length: int, wht_type: str | int = 'natural')[source]#

Walsh-Hadamard Transform module.

Parameters:

wht_lengthint >= 1: The WHT length, \(L\), must be a power of 2.
wht_type[‘sequency’, ‘natural’, ‘dyadic’]: The order of the coefficients in the Walsh matrix.

References

[1]

K. Usha et al., “Generation of Walsh codes in two different orderings using 4-bit Gray and Inverse Gray codes,” Indian Journal of Science and Technology, vol. 5, no. 3, pp. 2341-2345, 2012.

forward(x: Tensor) → Tensor[source]#

Apply WHT to the input.

Parameters:

xTensor [shape=(…, L)]: The input.

Returns:

outTensor [shape=(…, L)]: The WHT output.

Examples

>>> x = diffsptk.ramp(3)
>>> wht = diffsptk.WHT(4)
>>> y = wht(x)
>>> y
tensor([ 3., -1., -2.,  0.])
>>> z = wht(y)
>>> z
tensor([0., 1., 2., 3.])

diffsptk.functional.wht(x: Tensor, wht_type: str = 'natural') → Tensor[source]#

Apply WHT to the input.

Parameters:

xTensor [shape=(…, L)]: The input.
wht_type[‘sequency’, ‘natural’, ‘dyadic’]: The order of the coefficients in the Walsh matrix.

Returns:

outTensor [shape=(…, L)]: The WHT output.