How to resolve the algorithm Fast Fourier transform step by step in the Lambdatalk programming language
Published on 12 May 2024 09:40 PM
How to resolve the algorithm Fast Fourier transform step by step in the Lambdatalk programming language
Table of Contents
Problem Statement
Calculate the FFT (Fast Fourier Transform) of an input sequence. The most general case allows for complex numbers at the input and results in a sequence of equal length, again of complex numbers. If you need to restrict yourself to real numbers, the output should be the magnitude (i.e.: sqrt(re2 + im2)) of the complex result. The classic version is the recursive Cooley–Tukey FFT. Wikipedia has pseudo-code for that. Further optimizations are possible but not required.
Let's start with the solution:
Step by Step solution about How to resolve the algorithm Fast Fourier transform step by step in the Lambdatalk programming language
Source code in the lambdatalk programming language
1) the function fft
{def fft
{lambda {:s :x}
{if {= {list.length :x} 1}
then :x
else {let { {:s :s}
{:ev {fft :s {evens :x}} }
{:od {fft :s {odds :x}} } }
{let { {:ev :ev} {:t {rotate :s :od 0 {list.length :od}}} }
{list.append {list.map Cadd :ev :t}
{list.map Csub :ev :t}} }}}}}
{def rotate
{lambda {:s :f :k :N}
{if {list.null? :f}
then nil
else {cons {Cmul {car :f} {Cexp {Cnew 0 {/ {* :s {PI} :k} :N}}}}
{rotate :s {cdr :f} {+ :k 1} :N}}}}}
2) functions for lists
We add to the existing {lambda talk}'s list primitives a small set of functions required by the function fft.
{def evens
{lambda {:l}
{if {list.null? :l}
then nil
else {cons {car :l} {evens {cdr {cdr :l}}}}}}}
{def odds
{lambda {:l}
{if {list.null? {cdr :l}}
then nil
else {cons {car {cdr :l}} {odds {cdr {cdr :l}}}}}}}
{def list.map
{def list.map.r
{lambda {:f :a :b :c}
{if {list.null? :a}
then :c
else {list.map.r :f {cdr :a} {cdr :b}
{cons {:f {car :a} {car :b}} :c}} }}}
{lambda {:f :a :b}
{list.map.r :f {list.reverse :a} {list.reverse :b} nil}}}
{def list.append
{def list.append.r
{lambda {:a :b}
{if {list.null? :b}
then :a
else {list.append.r {cons {car :b} :a} {cdr :b}}}}}
{lambda {:a :b}
{list.append.r :b {list.reverse :a}} }}
3) functions for Cnumbers
{lambda talk} has no primitive functions working on complex numbers. We add the minimal set required by the function fft.
{def Cnew
{lambda {:x :y}
{cons :x :y} }}
{def Cnorm
{lambda {:c}
{sqrt {+ {* {car :c} {car :c}}
{* {cdr :c} {cdr :c}}}} }}
{def Cadd
{lambda {:x :y}
{cons {+ {car :x} {car :y}}
{+ {cdr :x} {cdr :y}}} }}
{def Csub
{lambda {:x :y}
{cons {- {car :x} {car :y}}
{- {cdr :x} {cdr :y}}} }}
{def Cmul
{lambda {:x :y}
{cons {- {* {car :x} {car :y}} {* {cdr :x} {cdr :y}}}
{+ {* {car :x} {cdr :y}} {* {cdr :x} {car :y}}}} }}
{def Cexp
{lambda {:x}
{cons {* {exp {car :x}} {cos {cdr :x}}}
{* {exp {car :x}} {sin {cdr :x}}}} }}
{def Clist
{lambda {:s}
{list.new {map {lambda {:i} {cons :i 0}} :s}}}}
4) testing
Applying the fft function on such a sample (1 1 1 1 0 0 0 0) where numbers have been promoted as complex
{list.disp {fft -1 {Clist 1 1 1 1 0 0 0 0}}} ->
(4 0)
(1 -2.414213562373095)
(0 0)
(1 -0.4142135623730949)
(0 0)
(0.9999999999999999 0.4142135623730949)
(0 0)
(0.9999999999999997 2.414213562373095)
A more usefull example can be seen in http://lambdaway.free.fr/lambdaspeech/?view=zorg
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