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feat: Implemented Fast Fourier Transform algorithm (TheAlgorithms#1700)
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* feat ; Implemented Fast Fourier Transform

* feat : Implemented Fast Fourier Transform

* fix : added comments to header

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* fix : updated the documentation

* fix : removed bad practice of using namespace td

* Update numerical_methods/fast_fourier_transform.cpp

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* fix : update code as per the namespace numerical_methods

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* fix : updated documentation

* fix : fixing warnings

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* [Brent Method Extrema](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/numerical_methods/brent_method_extrema.cpp)
* [Durand Kerner Roots](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/numerical_methods/durand_kerner_roots.cpp)
* [False Position](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/numerical_methods/false_position.cpp)
* [Fast Fourier Transform](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/numerical_methods/fast_fourier_transform.cpp)
* [Gaussian Elimination](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/numerical_methods/gaussian_elimination.cpp)
* [Golden Search Extrema](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/numerical_methods/golden_search_extrema.cpp)
* [Lu Decompose](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/numerical_methods/lu_decompose.cpp)
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165 changes: 165 additions & 0 deletions numerical_methods/fast_fourier_transform.cpp
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/**
* @file
* @brief [A fast Fourier transform
* (FFT)](https://medium.com/@aiswaryamathur/understanding-fast-fouriertransform-from-scratch-to-solve-polynomial-multiplication-8018d511162f)
* is an algorithm that computes the
* discrete Fourier transform (DFT) of a sequence, or its inverse (IDFT).
* @details
* This
* algorithm has application in use case scenario where a user wants to find points of a
* function
* in a short time by just using the coefficients of the polynomial
* function.
* It can be also used to find inverse fourier transform by just switching the
value of omega.
* Time complexity
* this algorithm computes the DFT in O(nlogn) time in comparison to traditional
O(n^2).
* @author [Ameya Chawla](https://github.com/ameyachawlaggsipu)
*/

#include <cassert> /// for assert
#include <cmath> /// for mathematical-related functions
#include <complex> /// for storing points and coefficents
#include <iostream> /// for IO operations
#include <vector> /// for std::vector

/**
* @namespace numerical_methods
* @brief Numerical algorithms/methods
*/
namespace numerical_methods {
/**
* @brief FastFourierTransform is a recursive function which returns list of
* complex numbers
* @param p List of Coefficents in form of complex numbers
* @param n Count of elements in list p
* @returns p if n==1
* @returns y if n!=1
*/
std::complex<double> *FastFourierTransform(std::complex<double> *p, uint8_t n) {
if (n == 1) {
return p; /// Base Case To return
}

double pi = 2 * asin(1.0); /// Declaring value of pi

std::complex<double> om = std::complex<double>(
cos(2 * pi / n), sin(2 * pi / n)); /// Calculating value of omega

auto *pe = new std::complex<double>[n / 2]; /// Coefficients of even power

auto *po = new std::complex<double>[n / 2]; /// Coefficients of odd power

int k1 = 0, k2 = 0;
for (int j = 0; j < n; j++) {
if (j % 2 == 0) {
pe[k1++] = p[j]; /// Assigning values of even Coefficients

} else
po[k2++] = p[j]; /// Assigning value of odd Coefficients
}

std::complex<double> *ye =
FastFourierTransform(pe, n / 2); /// Recursive Call

std::complex<double> *yo =
FastFourierTransform(po, n / 2); /// Recursive Call

auto *y = new std::complex<double>[n]; /// Final value representation list

k1 = 0, k2 = 0;

for (int i = 0; i < n / 2; i++) {
y[i] =
ye[k1] + pow(om, i) * yo[k2]; /// Updating the first n/2 elements
y[i + n / 2] =
ye[k1] - pow(om, i) * yo[k2]; /// Updating the last n/2 elements

k1++;
k2++;
}

if(n!=2){

delete[] pe;
delete[] po;

}

delete[] ye; /// Deleting dynamic array ye
delete[] yo; /// Deleting dynamic array yo
return y;
}

} // namespace numerical_methods

/**
* @brief Self-test implementations
* @details
* Declaring two test cases and checking for the error
* in predicted and true value is less than 0.000000000001.
* @returns void
*/
static void test() {
/* descriptions of the following test */

auto *t1 = new std::complex<double>[2]; /// Test case 1
auto *t2 = new std::complex<double>[4]; /// Test case 2

t1[0] = {1, 0};
t1[1] = {2, 0};
t2[0] = {1, 0};
t2[1] = {2, 0};
t2[2] = {3, 0};
t2[3] = {4, 0};

uint8_t n1 = 2;
uint8_t n2 = 4;
std::vector<std::complex<double>> r1 = {
{3, 0}, {-1, 0}}; /// True Answer for test case 1

std::vector<std::complex<double>> r2 = {
{10, 0}, {-2, -2}, {-2, 0}, {-2, 2}}; /// True Answer for test case 2

std::complex<double> *o1 = numerical_methods::FastFourierTransform(t1, n1);
std::complex<double> *t3=o1; /// Temporary variable used to delete memory location of o1
std::complex<double> *o2 = numerical_methods::FastFourierTransform(t2, n2);
std::complex<double> *t4=o2; /// Temporary variable used to delete memory location of o2
for (uint8_t i = 0; i < n1; i++) {
assert((r1[i].real() - o1->real() < 0.000000000001) &&
(r1[i].imag() - o1->imag() <
0.000000000001)); /// Comparing for both real and imaginary
/// values for test case 1
o1++;
}

for (uint8_t i = 0; i < n2; i++) {
assert((r2[i].real() - o2->real() < 0.000000000001) &&
(r2[i].imag() - o2->imag() <
0.000000000001)); /// Comparing for both real and imaginary
/// values for test case 2
o2++;
}


delete[] t1;
delete[] t2;
delete[] t3;
delete[] t4;
std::cout << "All tests have successfully passed!\n";
}

/**
* @brief Main function
* @param argc commandline argument count (ignored)
* @param argv commandline array of arguments (ignored)
* calls automated test function to test the working of fast fourier transform.
* @returns 0 on exit
*/

int main(int argc, char const *argv[]) {
test(); // run self-test implementations
// with 2 defined test cases
return 0;
}

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