## 1. Setup

This Post is about how to speed up the computation kernel density estimators using the FFT (Fast Fourier Transform). Let be be a random sample drawn from an unknown distribution with density . Remember, the kernel density estimator with bandwidth is then given by

(1)

### 1.1 Convolution Theorem

In the following we will use the notation from a previous article where the convolution theorem for the Fourier Transform was introduced. Accordingly the discrete version of the convolution theorem is given by: Let and be two functions defined at evenly spaced points, their convolution is given by:

(2)

To speed up the kernel computation we will use a particular feature given by:

(3)

### 1.2 Using DFFT Convolution to estimate

Since the discrete convolution theorem requires functions observed at evenly spaced points we create a fine grid of length where we want to evaluate the density.

(4)

where is the dirac delta function. To derive the estimate for all points the computer has to handle operations.Following [1], let the discrete Fourier transform of be denoted by . The Fourier transform of (4) for is then using convolution and translation given by

(5)

where is the Fourier transform of the data. The result corresponding to (4) can then be obtained by applying the inverse DFFT. All these steps then involve only operations. is derived using a histogram on the observed data with binning boundaries defined by , and then applying the Fast Fourier transform.

### 1.3 Implementation

To implement the method the fft function that comes with the numpy package was chosen. The numpy fft() function will return the approximation of the DFT from 0 to . Therefore fftshift() is needed to swap the output vector of the fft() right down the middle. So the output of fftshift(fft()) is then from to . The computation using the fft was around 5 times faster.

import numpy as np import matplotlib.pyplot as plt import timeit # Simulation N=500 X=np.random.normal(-0,1.5,N) plt.scatter(np.linspace(0,N,N), X, c="blue") plt.xlabel('sample') plt.ylabel('X') plt.show() # Presetting Nout=2**7 def epan_kernel(u,b): u= u/b return max(0, 1./b*3./4*(1-u**2)) # Usual estimation def dens(s, X, h=0.5): return [epan_kernel(s-x,1) for x in X] start = timeit.default_timer() grid = np.linspace(-5, 5, num=Nout) density=[(1./X.size)*sum( dens(y, X) ) for y in grid] plt.plot(grid, density ) stop = timeit.default_timer() print('Time: ', stop - start) # Estimation using FFT start = timeit.default_timer() to = np.linspace(-5, 5, Nout+1) t = grid kernel=[epan_kernel(x,1) for x in t] kernel_ft=np.fft.fft(kernel) hist,bins=np.histogram(X, bins=to) density_tmp=kernel_ft.flatten()*np.fft.fft( hist ) denisty_fft=(1./X.size)*np.fft.fftshift( np.fft.ifft(density_tmp).real) plt.plot(t, denisty_fft) stop = timeit.default_timer() print('Time: ', stop - start) print(np.sqrt(sum((density-denisty_fft)**2)))

### References

[Bibtex]

```
@article{Silverman1982,
ISSN = {00359254, 14679876},
URL = {http://www.jstor.org/stable/2347084},
author = {B. W. Silverman},
journal = {Journal of the Royal Statistical Society. Series C (Applied Statistics)},
number = {1},
pages = {93--99},
publisher = {[Wiley, Royal Statistical Society]},
title = {Algorithm AS 176: Kernel Density Estimation Using the Fast Fourier Transform},
volume = {31},
year = {1982}
}
```

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