torch (version 0.1.0)

torch_ifft: Ifft

Description

Ifft

Usage

torch_ifft(self, signal_ndim, normalized = FALSE)

Arguments

self

(Tensor) the input tensor of at least signal_ndim + 1 dimensions

signal_ndim

(int) the number of dimensions in each signal. signal_ndim can only be 1, 2 or 3

normalized

(bool, optional) controls whether to return normalized results. Default: FALSE

ifft(input, signal_ndim, normalized=False) -> Tensor

Complex-to-complex Inverse Discrete Fourier Transform

This method computes the complex-to-complex inverse discrete Fourier transform. Ignoring the batch dimensions, it computes the following expression:

$$ X[\omega_1, \dots, \omega_d] = \frac{1}{\prod_{i=1}^d N_i} \sum_{n_1=0}^{N_1-1} \dots \sum_{n_d=0}^{N_d-1} x[n_1, \dots, n_d] e^{\ j\ 2 \pi \sum_{i=0}^d \frac{\omega_i n_i}{N_i}}, $$ where \(d\) = signal_ndim is number of dimensions for the signal, and \(N_i\) is the size of signal dimension \(i\).

The argument specifications are almost identical with torch_fft. However, if normalized is set to TRUE, this instead returns the results multiplied by \(\sqrt{\prod_{i=1}^d N_i}\), to become a unitary operator. Therefore, to invert a torch_fft, the normalized argument should be set identically for torch_fft.

Returns the real and the imaginary parts together as one tensor of the same shape of input.

The inverse of this function is torch_fft.

Warning

For CPU tensors, this method is currently only available with MKL. Use torch_backends.mkl.is_available to check if MKL is installed.

Examples

# NOT RUN {
if (torch_is_installed()) {

x = torch_randn(c(3, 3, 2))
x
y = torch_fft(x, 2)
torch_ifft(y, 2)  # recover x
}
# }