const double PI =3.1415926536; template<classIter_T> voidfft(Iter_T a, Iter_T b, int log2n){ typedeftypenameiterator_traits<iter_t>::value_type complex; const complex J(0,1); int n =1<< log2n; for(unsigned int i =0; i < n; ++i) { b[bitReverse(...
下面是一个简单的迭代FFT算法的C++代码: #include<complex>#include<iostream>#include<climits>usingnamespacestd;usingComplex=complex<double>;// 使用标准库的复数constexprdoubleHINT_PI=3.1415926535897932384626433832795;constexprdoubleHINT_2PI=HINT_PI*2;// 二进制逆序template<typenameT>voidbinary_inverse_swap(T...
#include<cstdio>#include<cstring>#include<algorithm>usingnamespacestd;#defineN 2000050#definell long long#defineMOD 998244353template<typename T>inlinevoidread(T&x) { T f=1,c=0;charch=getchar();while(ch<'0'||ch>'9'){if(ch=='-')f=-1;ch=getchar();}while(ch>='0'&&ch<='9'){...
fix(math/poly/fft): fix fft template fixed when on equals -1, y[i].y is not divided by lenLoading branch information Cheuring authored Nov 27, 2024 1 parent afa036c commit f3109ec Showing 1 changed file with 2 additions and 0 deletions. Whitespace Ignore whitespace Split Unified 2 ...
templatevoid fft(Iter_T a, Iter_T b, int log2n) { typedef typename iterator_traits::value_type complex; const complex J(0, 1); int n = 1 << log2n; for (unsigned int i = 0; i < n; ++i) { b[bitReverse(i, log2n)] = a[i]; ...
template<class T>inline void read(T &x){Finish_read=0;x=0;int f=1;char ch=getchar();while(!isdigit(ch)){if(ch=='-')f=-1;if(ch==EOF)return;ch=getchar();}while(isdigit(ch))x=x*10+ch-'0',ch=getchar();x*=f;Finish_read=1;} ...
*/ #include <complex> #include <iterator> #include <cmath> #include <cassert> #include <cstddef> template<typename iter_t> #if __cplusplus > 201703L requires std::random_access_iterator<iter_t> #endif inline void fft_in_place(iter_t arr_begin, iter_t arr_end, const bool is_...
template <typename T> inline T read(){ T x=0;char ch=getchar();bool fl=false; while(!isdigit(ch)){if(ch=='-')fl=true;ch=getchar();} while(isdigit(ch)){ x=(x<<3)+(x<<1)+(ch^48);ch=getchar(); } return fl?-x:x; ...
关键步骤是将点值表示快速转化为系数表示,快速插值。 FFT 就是利用了单位根的特殊性质,通过分治加速运算。 其主要优化技巧是将各次项按照奇偶性分组,同时利用单位根的特殊性质简化递归计算。 奇偶性分组 对于f(x)=a0+a1x+a2x2+⋯+a7x7。 建立新函数G(x)=a0+a2x+a4x2+a6x3,H(x)=a1+a3x+a5x2+a7x...
You can change its parameters in LogPolarFFTTemplateMatch method. Usage example: #include "fftm.hpp" using namespace std; using namespace cv; //--- // //---