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我正在尝试修复一个我发现的程序，因此它需要的值与它作为自身测试的值不同。程序应该能够获取一系列值，这些值将数学函数表示为信号，输出应该是对该信号的快速傅立叶变换。以下是我已经有固定的代码：无法将复杂的<double>转换为双倍
#include <complex>
#include <iostream>
#include <valarray>
#define fnc(x) (x)
const double PI = 3.141592653589793238460;
typedef std::valarray<double> CArray;
union{
double d;
int i;
}num,i;
void fft(CArray& x)
{
const size_t N = x.size();
if (N <= 1) return;
// divide
CArray even = x[std::slice(0, N/2, 2)];
CArray odd = x[std::slice(1, N/2, 2)];
// conquer
fft(even);
fft(odd);
// combine
for (size_t k = 0; k < N/2; ++k)
{
double t = std::polar(1.0, -2 * PI * k/N) * odd[k];
x[k ] = even[k] + t;
x[k+N/2] = even[k] - t;
}
}
//Complex f = 1.0/sqrt(N);
//for (unsigned int i = 0; i < N; i++)
// x[i] *= f;
int main()
{
num.d=513;
double test[num.i];
for(i.i=1; i.i < num.i;++i.i)
test[i.i] = (double)fnc(i.i);
CArray data(test, num.d);
// forward fft
fft(data);
std::cout << "fft" << std::endl;
for (i.i = 0; i.i < num.i; ++i.i)
{
std::cout << data[i.i] << std::endl;
}
return 0;
}
当我尝试编译TI显示我在34行的样带
error: cannot convert 'std::complex' to 'double' in initialization|
，就标志着这部分行：
for (size_t k = 0; k < N/2; ++k)
{
double t = std::polar(1.0, -2 * PI * k/N) * odd[k];
x[k ] = even[k] + t;
x[k+N/2] = even[k] - t;
}
pesizaly这一个：
double t = std::polar(1.0, -2 * PI * k/N) * odd[k];
如果有人能告诉我如何解决这个问题，我会非常冷静。 为了更好的参考，这是原始代码，以防万一任何人可以告诉我一个更好的方法来解决它，所以它会记住我想要的。
#include <complex>
#include <iostream>
#include <valarray>
const double PI = 3.141592653589793238460;
typedef std::complex<double> Complex;
typedef std::valarray<Complex> CArray;
// Cooley–Tukey FFT (in-place, divide-and-conquer)
// Higher memory requirements and redundancy although more intuitive
void fft(CArray& x)
{
const size_t N = x.size();
if (N <= 1) return;
// divide
CArray even = x[std::slice(0, N/2, 2)];
CArray odd = x[std::slice(1, N/2, 2)];
// conquer
fft(even);
fft(odd);
// combine
for (size_t k = 0; k < N/2; ++k)
{
Complex t = std::polar(1.0, -2 * PI * k/N) * odd[k];
x[k ] = even[k] + t;
x[k+N/2] = even[k] - t;
}
}
// Cooley-Tukey FFT (in-place, breadth-first, decimation-in-frequency)
// Better optimized but less intuitive
// !!! Warning : in some cases this code make result different from not optimased version above (need to fix bug)
// The bug is now fixed @2017/05/30
void fft(CArray &x)
{
// DFT
unsigned int N = x.size(), k = N, n;
double thetaT = 3.14159265358979323846264338328L/N;
Complex phiT = Complex(cos(thetaT), -sin(thetaT)), T;
while (k > 1)
{
n = k;
k >>= 1;
phiT = phiT * phiT;
T = 1.0L;
for (unsigned int l = 0; l < k; l++)
{
for (unsigned int a = l; a < N; a += n)
{
unsigned int b = a + k;
Complex t = x[a] - x[b];
x[a] += x[b];
x[b] = t * T;
}
T *= phiT;
}
}
// Decimate
unsigned int m = (unsigned int)log2(N);
for (unsigned int a = 0; a < N; a++)
{
unsigned int b = a;
// Reverse bits
b = (((b & 0xaaaaaaaa) >> 1) | ((b & 0x55555555) << 1));
b = (((b & 0xcccccccc) >> 2) | ((b & 0x33333333) << 2));
b = (((b & 0xf0f0f0f0) >> 4) | ((b & 0x0f0f0f0f) << 4));
b = (((b & 0xff00ff00) >> 8) | ((b & 0x00ff00ff) << 8));
b = ((b >> 16) | (b << 16)) >> (32 - m);
if (b > a)
{
Complex t = x[a];
x[a] = x[b];
x[b] = t;
}
}
//// Normalize (This section make it not working correctly)
//Complex f = 1.0/sqrt(N);
//for (unsigned int i = 0; i < N; i++)
// x[i] *= f;
}
// inverse fft (in-place)
void ifft(CArray& x)
{
// conjugate the complex numbers
x = x.apply(std::conj);
// forward fft
fft(x);
// conjugate the complex numbers again
x = x.apply(std::conj);
// scale the numbers
x /= x.size();
}
int main()
{
const Complex test[] = { 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0 };
CArray data(test, 8);
// forward fft
fft(data);
std::cout << "fft" << std::endl;
for (int i = 0; i < 8; ++i)
{
std::cout << data[i] << std::endl;
}
// inverse fft
ifft(data);
std::cout << std::endl << "ifft" << std::endl;
for (int i = 0; i < 8; ++i)
{
std::cout << data[i] << std::endl;
}
return 0;
}
Ps。如果有人知道我需要的更好的代码，我也可以使用它。
使用['M_PI'在math.h中]（HTTPS：//计算器.com/q/1727881/995714）而不是定义你自己的 –