[Numpy-discussion] reformulating a series of correlations as one fft, ifft pair

josef.pktd@gmai... josef.pktd@gmai...
Mon Apr 19 08:01:27 CDT 2010

```On Mon, Apr 19, 2010 at 2:36 AM, Paul Northug <pnorthug@gmail.com> wrote:
> I am having trouble reformulating a series of correlations as a single
> fft, ifft pair.
>
> I have a set of kernels phi : (M = channel, N = kernel, T = time)
> correlated with signal a : (N, P+T-1) yielding x : (M, T).
>
> The correlation, for now, is only in the last dimension, with the two
> other dimensions degenerate. Below are 4 implementations (x == y == z
> != w). I would like to reformulate the operation as a single pair of
> 3d ffts to compare how it fares for different problem dimensions but I
> am not able to do it. Implementation #4 is incorrect. Please note the
> .conj() to get correlations rather than convolutions.
>
> Any tips would be appreciated. Optimally, I would not have to pad with
> too many zeros as the matrices can be quite large. The challenge is to
> beat implementation 2 as P grows. M, N, P are typically on the order
> of 10^2 and T, 10^3.
>
> #
> import numpy as np
> from scipy.signal import fftn, ifftn, correlate
>
> M, N, P, T = 2, 3, 4, 5
>
> phi = np.random.randn(M, N, P)
> a = np.random.randn(N, P+T-1)
> x = np.zeros((M, T))
>
> # 1. correlate
>
> x = np.array([correlate(phi[i], a, 'valid').squeeze() for i in range(M)])
>
> # 2. dot products
>
> y = np.zeros_like(x)
> for b in range(P): y += np.dot(phi[:,:,b], a[:,b:b+T])
>
> # 3. a series of ffts (code adapted from scipy convolve)
>
> s1 = np.array(phi.shape[1:])
> s2 = np.array(a.shape)
> size = s1+s2-1
> z = np.empty_like(y)
> fsize = (2**np.ceil(np.log2(size))).astype(np.int) # power of 2 pad
> af = fftn(a, fsize)
> fslice = tuple([slice(0, sz) for sz in size])
> for i in range(M):
>    phif = fftn(phi[i], fsize).conj()
>    z[i] = np.real(ifftn(phif * af)[fslice])[0,:T]
>
> # 4. <BROKEN> single fft, ifft pair - some rows are correct
>
> s1 = np.array(phi.shape)
> s2 = np.array(a[np.newaxis].shape)
> size = s1+s2-1
> fsize = (2**np.ceil(np.log2(size))).astype(np.int)
> af = fftn(a[np.newaxis], fsize)
> phif = fftn(phi, fsize).conj()
> fslice = tuple([slice(0, sz) for sz in size])
> w = np.real(ifftn(phif * af)[fslice])[:,:,:T]

I only checked this in your example

>>> np.squeeze(signal.fftconvolve(phi[:,::-1,::-1], a[None,...],mode='valid'))
array([[ 0.42695078,  4.3433924 , -2.53084395, -4.22672281, -5.16705981],
[-2.35108765, -2.15060513, -1.23528122,  1.40386599,  1.38189896]])

>>> y
array([[ 0.42695078,  4.3433924 , -2.53084395, -4.22672281, -5.16705981],
[-2.35108765, -2.15060513, -1.23528122,  1.40386599,  1.38189896]])
>>> x
array([[ 0.42695078,  4.3433924 , -2.53084395, -4.22672281, -5.16705981],
[-2.35108765, -2.15060513, -1.23528122,  1.40386599,  1.38189896]])

Josef

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```

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