Notice: This put up is an excerpt from the forthcoming ebook, Deep Studying and Scientific Computing with R torch. The chapter in query is on the Discrete Fourier Rework (DFT), and is situated partly three. Half three is devoted to scientific computation past deep studying.
There are two chapters on the Fourier Rework. The primary strives to, in as “verbal” and lucid a approach as was attainable to me, solid a lightweight on what’s behind the magic; it additionally exhibits how, surprisingly, you’ll be able to code the DFT in merely half a dozen traces. The second focuses on quick implementation (the Quick Fourier Rework, or FFT), once more with each conceptual/explanatory in addition to sensible, codeityourself elements.
Collectively, these cowl way more materials than might sensibly match right into a weblog put up; due to this fact, please think about what follows extra as a “teaser” than a completely fledged article.
Within the sciences, the Fourier Rework is nearly in all places. Acknowledged very typically, it converts knowledge from one illustration to a different, with none lack of data (if executed accurately, that’s.) If you happen to use torch
, it’s only a operate name away: torch_fft_fft()
goes a technique, torch_fft_ifft()
the opposite. For the person, that’s handy – you “simply” have to know tips on how to interpret the outcomes. Right here, I need to assist with that. We begin with an instance operate name, enjoying round with its output, after which, attempt to get a grip on what’s going on behind the scenes.
Understanding the output of torch_fft_fft()
As we care about precise understanding, we begin from the only attainable instance sign, a pure cosine that performs one revolution over the whole sampling interval.
Place to begin: A cosine of frequency 1
The way in which we set issues up, there shall be sixtyfour samples; the sampling interval thus equals N = 64
. The content material of frequency()
, the under helper operate used to assemble the sign, displays how we characterize the cosine. Specifically:
[
f(x) = cos(frac{2 pi}{N} k x)
]
Right here (x) values progress over time (or house), and (ok) is the frequency index. A cosine is periodic with interval (2 pi); so if we would like it to first return to its beginning state after sixtyfour samples, and (x) runs between zero and sixtythree, we’ll need (ok) to be equal to (1). Like that, we’ll attain the preliminary state once more at place (x = frac{2 pi}{64} * 1 * 64).
Let’s rapidly verify this did what it was presupposed to:
df < knowledge.body(x = sample_positions, y = as.numeric(x))
ggplot(df, aes(x = x, y = y)) +
geom_line() +
xlab("time") +
ylab("amplitude") +
theme_minimal()
Now that we have now the enter sign, torch_fft_fft()
computes for us the Fourier coefficients, that’s, the significance of the assorted frequencies current within the sign. The variety of frequencies thoughtabout will equal the variety of sampling factors: So (X) shall be of size sixtyfour as effectively.
(In our instance, you’ll discover that the second half of coefficients will equal the primary in magnitude. That is the case for each realvalued sign. In such instances, you can name torch_fft_rfft()
as an alternative, which yields “nicer” (within the sense of shorter) vectors to work with. Right here although, I need to clarify the final case, since that’s what you’ll discover executed in most expositions on the subject.)
Even with the sign being actual, the Fourier coefficients are advanced numbers. There are 4 methods to examine them. The primary is to extract the true half:
[1] 0 32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[29] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[57] 0 0 0 0 0 0 0 32
Solely a single coefficient is nonzero, the one at place 1. (We begin counting from zero, and should discard the second half, as defined above.)
Now wanting on the imaginary half, we discover it’s zero all through:
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[29] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[57] 0 0 0 0 0 0 0 0
At this level we all know that there’s only a single frequency current within the sign, specifically, that at (ok = 1). This matches (and it higher needed to) the way in which we constructed the sign: specifically, as engaging in a single revolution over the whole sampling interval.
Since, in idea, each coefficient might have nonzero actual and imaginary elements, typically what you’d report is the magnitude (the sq. root of the sum of squared actual and imaginary elements):
[1] 0 32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[29] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[57] 0 0 0 0 0 0 0 32
Unsurprisingly, these values precisely replicate the respective actual elements.
Lastly, there’s the part, indicating a attainable shift of the sign (a pure cosine is unshifted). In torch
, we have now torch_angle()
complementing torch_abs()
, however we have to keep in mind roundoff error right here. We all know that in every however a single case, the true and imaginary elements are each precisely zero; however on account of finite precision in how numbers are introduced in a pc, the precise values will typically not be zero. As an alternative, they’ll be very small. If we take considered one of these “pretend nonzeroes” and divide it by one other, as occurs within the angle calculation, large values may end up. To stop this from occurring, our customized implementation rounds each inputs earlier than triggering the division.
part < operate(Ft, threshold = 1e5) {
torch_atan2(
torch_abs(torch_round(Ft$imag * threshold)),
torch_abs(torch_round(Ft$actual * threshold))
)
}
as.numeric(part(Ft)) %>% spherical(5)
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[29] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[57] 0 0 0 0 0 0 0 0
As anticipated, there isn’t a part shift within the sign.
Let’s visualize what we discovered.
create_plot < operate(x, y, amount) {
df < knowledge.body(
x_ = x,
y_ = as.numeric(y) %>% spherical(5)
)
ggplot(df, aes(x = x_, y = y_)) +
geom_col() +
xlab("frequency") +
ylab(amount) +
theme_minimal()
}
p_real < create_plot(
sample_positions,
real_part,
"actual half"
)
p_imag < create_plot(
sample_positions,
imag_part,
"imaginary half"
)
p_magnitude < create_plot(
sample_positions,
magnitude,
"magnitude"
)
p_phase < create_plot(
sample_positions,
part(Ft),
"part"
)
p_real + p_imag + p_magnitude + p_phase
It’s truthful to say that we have now no purpose to doubt what torch_fft_fft()
has executed. However with a pure sinusoid like this, we will perceive precisely what’s occurring by computing the DFT ourselves, by hand. Doing this now will considerably assist us later, once we’re writing the code.
Reconstructing the magic
One caveat about this part. With a subject as wealthy because the Fourier Rework, and an viewers who I think about to fluctuate extensively on a dimension of math and sciences schooling, my probabilities to fulfill your expectations, expensive reader, have to be very near zero. Nonetheless, I need to take the chance. If you happen to’re an professional on these items, you’ll anyway be simply scanning the textual content, looking for items of torch
code. If you happen to’re reasonably acquainted with the DFT, you should still like being reminded of its interior workings. And – most significantly – in case you’re somewhat new, and even fully new, to this subject, you’ll hopefully take away (at the least) one factor: that what looks like one of many best wonders of the universe (assuming there’s a actuality by some means comparable to what goes on in our minds) might be a surprise, however neither “magic” nor a factor reserved to the initiated.
In a nutshell, the Fourier Rework is a foundation transformation. Within the case of the DFT – the Discrete Fourier Rework, the place time and frequency representations each are finite vectors, not features – the brand new foundation appears like this:
[
begin{aligned}
&mathbf{w}^{0n}_N = e^{ifrac{2 pi}{N}* 0 * n} = 1
&mathbf{w}^{1n}_N = e^{ifrac{2 pi}{N}* 1 * n} = e^{ifrac{2 pi}{N} n}
&mathbf{w}^{2n}_N = e^{ifrac{2 pi}{N}* 2 * n} = e^{ifrac{2 pi}{N}2n}& …
&mathbf{w}^{(N1)n}_N = e^{ifrac{2 pi}{N}* (N1) * n} = e^{ifrac{2 pi}{N}(N1)n}
end{aligned}
]
Right here (N), as earlier than, is the variety of samples (64, in our case); thus, there are (N) foundation vectors. With (ok) operating via the idea vectors, they are often written:
[
mathbf{w}^{kn}_N = e^{ifrac{2 pi}{N}k n}
] {#eqdft1}
Like (ok), (n) runs from (0) to (N1). To grasp what these foundation vectors are doing, it’s useful to quickly change to a shorter sampling interval, (N = 4), say. If we achieve this, we have now 4 foundation vectors: (mathbf{w}^{0n}_N), (mathbf{w}^{1n}_N), (mathbf{w}^{2n}_N), and (mathbf{w}^{3n}_N). The primary one appears like this:
[
mathbf{w}^{0n}_N
=
begin{bmatrix}
e^{ifrac{2 pi}{4}* 0 * 0}
e^{ifrac{2 pi}{4}* 0 * 1}
e^{ifrac{2 pi}{4}* 0 * 2}
e^{ifrac{2 pi}{4}* 0 * 3}
end{bmatrix}
=
begin{bmatrix}
1
1
1
1
end{bmatrix}
]
The second, like so:
[
mathbf{w}^{1n}_N
=
begin{bmatrix}
e^{ifrac{2 pi}{4}* 1 * 0}
e^{ifrac{2 pi}{4}* 1 * 1}
e^{ifrac{2 pi}{4}* 1 * 2}
e^{ifrac{2 pi}{4}* 1 * 3}
end{bmatrix}
=
begin{bmatrix}
1
e^{ifrac{pi}{2}}
e^{i pi}
e^{ifrac{3 pi}{4}}
end{bmatrix}
=
begin{bmatrix}
1
i
1
i
end{bmatrix}
]
That is the third:
[
mathbf{w}^{2n}_N
=
begin{bmatrix}
e^{ifrac{2 pi}{4}* 2 * 0}
e^{ifrac{2 pi}{4}* 2 * 1}
e^{ifrac{2 pi}{4}* 2 * 2}
e^{ifrac{2 pi}{4}* 2 * 3}
end{bmatrix}
=
begin{bmatrix}
1
e^{ipi}
e^{i 2 pi}
e^{ifrac{3 pi}{2}}
end{bmatrix}
=
begin{bmatrix}
1
1
1
1
end{bmatrix}
]
And eventually, the fourth:
[
mathbf{w}^{3n}_N
=
begin{bmatrix}
e^{ifrac{2 pi}{4}* 3 * 0}
e^{ifrac{2 pi}{4}* 3 * 1}
e^{ifrac{2 pi}{4}* 3 * 2}
e^{ifrac{2 pi}{4}* 3 * 3}
end{bmatrix}
=
begin{bmatrix}
1
e^{ifrac{3 pi}{2}}
e^{i 3 pi}
e^{ifrac{9 pi}{2}}
end{bmatrix}
=
begin{bmatrix}
1
i
1
i
end{bmatrix}
]
We are able to characterize these 4 foundation vectors when it comes to their “velocity”: how briskly they transfer across the unit circle. To do that, we merely have a look at the rightmost column vectors, the place the ultimate calculation outcomes seem. The values in that column correspond to positions pointed to by the revolving foundation vector at completely different deadlines. Which means taking a look at a single “replace of place”, we will see how briskly the vector is shifting in a single time step.
Trying first at (mathbf{w}^{0n}_N), we see that it doesn’t transfer in any respect. (mathbf{w}^{1n}_N) goes from (1) to (i) to (1) to (i); yet one more step, and it will be again the place it began. That’s one revolution in 4 steps, or a step dimension of (frac{pi}{2}). Then (mathbf{w}^{2n}_N) goes at double that tempo, shifting a distance of (pi) alongside the circle. That approach, it finally ends up finishing two revolutions general. Lastly, (mathbf{w}^{3n}_N) achieves three full loops, for a step dimension of (frac{3 pi}{2}).
The factor that makes these foundation vectors so helpful is that they’re mutually orthogonal. That’s, their dot product is zero:
[
langle mathbf{w}^{kn}_N, mathbf{w}^{ln}_N rangle = sum_{n=0}^{N1} ({e^{ifrac{2 pi}{N}k n}})^* e^{ifrac{2 pi}{N}l n} = sum_{n=0}^{N1} ({e^{ifrac{2 pi}{N}k n}})e^{ifrac{2 pi}{N}l n} = 0
] {#eqdft2}
Let’s take, for instance, (mathbf{w}^{2n}_N) and (mathbf{w}^{3n}_N). Certainly, their dot product evaluates to zero.
[
begin{bmatrix}
1 & 1 & 1 & 1
end{bmatrix}
begin{bmatrix}
1
i
1
i
end{bmatrix}
=
1 + i + (1) + (i) = 0
]
Now, we’re about to see how the orthogonality of the Fourier foundation considerably simplifies the calculation of the DFT. Did you discover the similarity between these foundation vectors and the way in which we wrote the instance sign? Right here it’s once more:
[
f(x) = cos(frac{2 pi}{N} k x)
]
If we handle to characterize this operate when it comes to the idea vectors (mathbf{w}^{kn}_N = e^{ifrac{2 pi}{N}ok n}), the interior product between the operate and every foundation vector shall be both zero (the “default”) or a a number of of 1 (in case the operate has a element matching the idea vector in query). Fortunately, sines and cosines can simply be transformed into advanced exponentials. In our instance, that is how that goes:
[
begin{aligned}
mathbf{x}_n &= cos(frac{2 pi}{64} n)
&= frac{1}{2} (e^{ifrac{2 pi}{64} n} + e^{ifrac{2 pi}{64} n})
&= frac{1}{2} (e^{ifrac{2 pi}{64} n} + e^{ifrac{2 pi}{64} 63n})
&= frac{1}{2} (mathbf{w}^{1n}_N + mathbf{w}^{63n}_N)
end{aligned}
]
Right here step one straight outcomes from Euler’s components, and the second displays the truth that the Fourier coefficients are periodic, with frequency 1 being the identical as 63, 2 equaling 62, and so forth.
Now, the (ok)th Fourier coefficient is obtained by projecting the sign onto foundation vector (ok).
Because of the orthogonality of the idea vectors, solely two coefficients is not going to be zero: these for (mathbf{w}^{1n}_N) and (mathbf{w}^{63n}_N). They’re obtained by computing the interior product between the operate and the idea vector in query, that’s, by summing over (n). For every (n) ranging between (0) and (N1), we have now a contribution of (frac{1}{2}), leaving us with a remaining sum of (32) for each coefficients. For instance, for (mathbf{w}^{1n}_N):
[
begin{aligned}
X_1 &= langle mathbf{w}^{1n}_N, mathbf{x}_n rangle
&= langle mathbf{w}^{1n}_N, frac{1}{2} (mathbf{w}^{1n}_N + mathbf{w}^{63n}_N) rangle
&= frac{1}{2} * 64
&= 32
end{aligned}
]
And analogously for (X_{63}).
Now, wanting again at what torch_fft_fft()
gave us, we see we had been in a position to arrive on the identical outcome. And we’ve realized one thing alongside the way in which.
So long as we stick with indicators composed of a number of foundation vectors, we will compute the DFT on this approach. On the finish of the chapter, we’ll develop code that may work for all indicators, however first, let’s see if we will dive even deeper into the workings of the DFT. Three issues we’ll need to discover:

What would occur if frequencies modified – say, a melody had been sung at a better pitch?

What about amplitude adjustments – say, the music had been performed twice as loud?

What about part – e.g., there have been an offset earlier than the piece began?
In all instances, we’ll name torch_fft_fft()
solely as soon as we’ve decided the outcome ourselves.
And eventually, we’ll see how advanced sinusoids, made up of various elements, can nonetheless be analyzed on this approach, offered they are often expressed when it comes to the frequencies that make up the idea.
Various frequency
Assume we quadrupled the frequency, giving us a sign that seemed like this:
[
mathbf{x}_n = cos(frac{2 pi}{N}*4*n)
]
Following the identical logic as above, we will categorical it like so:
[
mathbf{x}_n = frac{1}{2} (mathbf{w}^{4n}_N + mathbf{w}^{60n}_N)
]
We already see that nonzero coefficients shall be obtained just for frequency indices (4) and (60). Choosing the previous, we acquire
[
begin{aligned}
X_4 &= langle mathbf{w}^{4n}_N, mathbf{x}_n rangle
&= langle mathbf{w}^{4n}_N, frac{1}{2} (mathbf{w}^{4n}_N + mathbf{w}^{60n}_N) rangle
&= 32
end{aligned}
]
For the latter, we’d arrive on the identical outcome.
Now, let’s make certain our evaluation is appropriate. The next code snippet incorporates nothing new; it generates the sign, calculates the DFT, and plots them each.
x < torch_cos(frequency(4, N) * sample_positions)
plot_ft < operate(x) p_imag) /
(p_magnitude
plot_ft(x)
This does certainly verify our calculations.
A particular case arises when sign frequency rises to the best one “allowed”, within the sense of being detectable with out aliasing. That would be the case at one half of the variety of sampling factors. Then, the sign will appear like so:
[
mathbf{x}_n = frac{1}{2} (mathbf{w}^{32n}_N + mathbf{w}^{32n}_N)
]
Consequently, we find yourself with a single coefficient, comparable to a frequency of 32 revolutions per pattern interval, of double the magnitude (64, thus). Listed here are the sign and its DFT:
x < torch_cos(frequency(32, N) * sample_positions)
plot_ft(x)
Various amplitude
Now, let’s take into consideration what occurs once we fluctuate amplitude. For instance, say the sign will get twice as loud. Now, there shall be a multiplier of two that may be taken outdoors the interior product. In consequence, the one factor that adjustments is the magnitude of the coefficients.
Let’s confirm this. The modification is predicated on the instance we had earlier than the final one, with 4 revolutions over the sampling interval:
x < 2 * torch_cos(frequency(4, N) * sample_positions)
plot_ft(x)
Up to now, we have now not as soon as seen a coefficient with nonzero imaginary half. To alter this, we add in part.
Including part
Altering the part of a sign means shifting it in time. Our instance sign is a cosine, a operate whose worth is 1 at (t=0). (That additionally was the – arbitrarily chosen – place to begin of the sign.)
Now assume we shift the sign ahead by (frac{pi}{2}). Then the height we had been seeing at zero strikes over to (frac{pi}{2}); and if we nonetheless begin “recording” at zero, we should discover a worth of zero there. An equation describing that is the next. For comfort, we assume a sampling interval of (2 pi) and (ok=1), in order that the instance is an easy cosine:
[
f(x) = cos(x – phi)
]
The minus signal might look unintuitive at first. Nevertheless it does make sense: We now need to acquire a worth of 1 at (x=frac{pi}{2}), so (x – phi) ought to consider to zero. (Or to any a number of of (pi).) Summing up, a delay in time will seem as a destructive part shift.
Now, we’re going to calculate the DFT for a shifted model of our instance sign. However in case you like, take a peek on the phaseshifted model of the timedomain image now already. You’ll see {that a} cosine, delayed by (frac{pi}{2}), is nothing else than a sine beginning at 0.
To compute the DFT, we comply with our familiarbynow technique. The sign now appears like this:
[
mathbf{x}_n = cos(frac{2 pi}{N}*4*x – frac{pi}{2})
]
First, we categorical it when it comes to foundation vectors:
[
begin{aligned}
mathbf{x}_n &= cos(frac{2 pi}{64} 4 n – frac{pi}{2})
&= frac{1}{2} (e^{ifrac{2 pi}{64} 4n – frac{pi}{2}} + e^{ifrac{2 pi}{64} 60n – frac{pi}{2}})
&= frac{1}{2} (e^{ifrac{2 pi}{64} 4n} e^{i frac{pi}{2}} + e^{ifrac{2 pi}{64} 60n} e^{ifrac{pi}{2}})
&= frac{1}{2} (e^{i frac{pi}{2}} mathbf{w}^{4n}_N + e^{i frac{pi}{2}} mathbf{w}^{60n}_N)
end{aligned}
]
Once more, we have now nonzero coefficients just for frequencies (4) and (60). However they’re advanced now, and each coefficients are not similar. As an alternative, one is the advanced conjugate of the opposite. First, (X_4):
[
begin{aligned}
X_4 &= langle mathbf{w}^{4n}_N, mathbf{x}_n rangle
&=langle mathbf{w}^{4n}_N, frac{1}{2} (e^{i frac{pi}{2}} mathbf{w}^{4n}_N + e^{i frac{pi}{2}} mathbf{w}^{60n}_N) rangle
&= 32 *e^{i frac{pi}{2}}
&= 32i
end{aligned}
]
And right here, (X_{60}):
[
begin{aligned}
X_{60} &= langle mathbf{w}^{60n}_N, mathbf{x}_N rangle
&= 32 *e^{i frac{pi}{2}}
&= 32i
end{aligned}
]
As ordinary, we examine our calculation utilizing torch_fft_fft()
.
x < torch_cos(frequency(4, N) * sample_positions  pi / 2)
plot_ft(x)
For a pure sine wave, the nonzero Fourier coefficients are imaginary. The part shift within the coefficients, reported as (frac{pi}{2}), displays the time delay we utilized to the sign.
Lastly – earlier than we write some code – let’s put all of it collectively, and have a look at a wave that has greater than a single sinusoidal element.
Superposition of sinusoids
The sign we assemble should still be expressed when it comes to the idea vectors, however it’s not a pure sinusoid. As an alternative, it’s a linear mixture of such:
[
begin{aligned}
mathbf{x}_n &= 3 sin(frac{2 pi}{64} 4n) + 6 cos(frac{2 pi}{64} 2n) +2cos(frac{2 pi}{64} 8n)
end{aligned}
]
I received’t undergo the calculation intimately, however it’s no completely different from the earlier ones. You compute the DFT for every of the three elements, and assemble the outcomes. With none calculation, nonetheless, there’s fairly a number of issues we will say:
 For the reason that sign consists of two pure cosines and one pure sine, there shall be 4 coefficients with nonzero actual elements, and two with nonzero imaginary elements. The latter shall be advanced conjugates of one another.
 From the way in which the sign is written, it’s straightforward to find the respective frequencies, as effectively: The allreal coefficients will correspond to frequency indices 2, 8, 56, and 62; the allimaginary ones to indices 4 and 60.
 Lastly, amplitudes will outcome from multiplying with (frac{64}{2}) the scaling components obtained for the person sinusoids.
Let’s examine:
Now, how can we calculate the DFT for much less handy indicators?
Coding the DFT
Fortuitously, we already know what must be executed. We need to venture the sign onto every of the idea vectors. In different phrases, we’ll be computing a bunch of interior merchandise. Logicwise, nothing adjustments: The one distinction is that generally, it is not going to be attainable to characterize the sign when it comes to just some foundation vectors, like we did earlier than. Thus, all projections will really should be calculated. However isn’t automation of tedious duties one factor we have now computer systems for?
Let’s begin by stating enter, output, and central logic of the algorithm to be applied. As all through this chapter, we keep in a single dimension. The enter, thus, is a onedimensional tensor, encoding a sign. The output is a onedimensional vector of Fourier coefficients, of the identical size because the enter, every holding details about a frequency. The central concept is: To acquire a coefficient, venture the sign onto the corresponding foundation vector.
To implement that concept, we have to create the idea vectors, and for each, compute its interior product with the sign. This may be executed in a loop. Surprisingly little code is required to perform the purpose:
dft < operate(x) {
n_samples < size(x)
n < torch_arange(0, n_samples  1)$unsqueeze(1)
Ft < torch_complex(
torch_zeros(n_samples), torch_zeros(n_samples)
)
for (ok in 0:(n_samples  1)) {
w_k < torch_exp(1i * 2 * pi / n_samples * ok * n)
dot < torch_matmul(w_k, x$to(dtype = torch_cfloat()))
Ft[k + 1] < dot
}
Ft
}
To check the implementation, we will take the final sign we analysed, and examine with the output of torch_fft_fft()
.
[1] 0 0 192 0 0 0 0 0 64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[29] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[57] 64 0 0 0 0 0 192 0
[1] 0 0 0 0 96 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[29] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[57] 0 0 0 0 96 0 0 0
Reassuringly – in case you look again – the outcomes are the identical.
Above, did I say “little code”? In actual fact, a loop shouldn’t be even wanted. As an alternative of working with the idea vectors onebyone, we will stack them in a matrix. Then every row will maintain the conjugate of a foundation vector, and there shall be (N) of them. The columns correspond to positions (0) to (N1); there shall be (N) of them as effectively. For instance, that is how the matrix would search for (N=4):
[
mathbf{W}_4
=
begin{bmatrix}
e^{ifrac{2 pi}{4}* 0 * 0} & e^{ifrac{2 pi}{4}* 0 * 1} & e^{ifrac{2 pi}{4}* 0 * 2} & e^{ifrac{2 pi}{4}* 0 * 3}
e^{ifrac{2 pi}{4}* 1 * 0} & e^{ifrac{2 pi}{4}* 1 * 1} & e^{ifrac{2 pi}{4}* 1 * 2} & e^{ifrac{2 pi}{4}* 1 * 3}
e^{ifrac{2 pi}{4}* 2 * 0} & e^{ifrac{2 pi}{4}* 2 * 1} & e^{ifrac{2 pi}{4}* 2 * 2} & e^{ifrac{2 pi}{4}* 2 * 3}
e^{ifrac{2 pi}{4}* 3 * 0} & e^{ifrac{2 pi}{4}* 3 * 1} & e^{ifrac{2 pi}{4}* 3 * 2} & e^{ifrac{2 pi}{4}* 3 * 3}
end{bmatrix}
] {#eqdft3}
Or, evaluating the expressions:
[
mathbf{W}_4
=
begin{bmatrix}
1 & 1 & 1 & 1
1 & i & 1 & i
1 & 1 & 1 & 1
1 & i & 1 & i
end{bmatrix}
]
With that modification, the code appears much more elegant:
dft_vec < operate(x) {
n_samples < size(x)
n < torch_arange(0, n_samples  1)$unsqueeze(1)
ok < torch_arange(0, n_samples  1)$unsqueeze(2)
mat_k_m < torch_exp(1i * 2 * pi / n_samples * ok * n)
torch_matmul(mat_k_m, x$to(dtype = torch_cfloat()))
}
As you’ll be able to simply confirm, the outcome is identical.
Thanks for studying!
Picture by Trac Vu on Unsplash