torch: torch_hamming_window – R documentation

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torch

torch_hamming_window

Hamming_window

Description

Hamming_window

Usage

torch_hamming_window(
  window_length,
  periodic = TRUE,
  alpha = 0.54,
  beta = 0.46,
  dtype = NULL,
  layout = torch_strided(),
  device = NULL,
  requires_grad = FALSE
)

Arguments

`window_length`	(int) the size of returned window
`periodic`	(bool, optional) If TRUE, returns a window to be used as periodic function. If False, return a symmetric window.
`alpha`	(float, optional) The coefficient α in the equation above
`beta`	(float, optional) The coefficient β in the equation above
`dtype`	(`torch.dtype`, optional) the desired data type of returned tensor. Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported.
`layout`	(`torch.layout`, optional) the desired layout of returned window tensor. Only `torch_strided` (dense layout) is supported.
`device`	(`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.
`requires_grad`	(bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`.

hamming_window(window_length, periodic=TRUE, alpha=0.54, beta=0.46, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor

Hamming window function.

w[n] = α - β\ \cos ≤ft( \frac{2 π n}{N - 1} \right),

where N is the full window size.

The input window_length is a positive integer controlling the returned window size. periodic flag determines whether the returned window trims off the last duplicate value from the symmetric window and is ready to be used as a periodic window with functions like torch_stft. Therefore, if periodic is true, the N in above formula is in fact \mbox{window\_length} + 1. Also, we always have torch_hamming_window(L, periodic=TRUE) equal to torch_hamming_window(L + 1, periodic=False)[:-1]).

Note

If `window_length` \eqn{=1}, the returned window contains a single value 1.

This is a generalized version of `torch_hann_window`.

torch

Tensors and Neural Networks with 'GPU' Acceleration

v0.3.0

MIT + file LICENSE

Authors

Daniel Falbel [aut, cre, cph], Javier Luraschi [aut], Dmitriy Selivanov [ctb], Athos Damiani [ctb], Christophe Regouby [ctb], Krzysztof Joachimiak [ctb], RStudio [cph]

Initial release

torch_hamming_window

Description

Usage

Arguments

hamming_window(window_length, periodic=TRUE, alpha=0.54, beta=0.46, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor

Note

torch

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