int main()
{
const int N_SAMPLES = 32;
uint8_t samples[] = {
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26 ,27 ,28 ,29, 30, 31
};
complex freq[] = { {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},{0,0},{0,0} };
for (int i=0; i<N_SAMPLES; i++) {
sdft(samples[i], freq, coeffs);
uint8_t out = isdft(freq, coeffs);
printf("%d \n", out);
}
}
void RBD::doSA()
{
/* 1. Determine sampling size */
if (N_ < 2*omega_ + 1)
throw SAException(ERROR_RBD_SMALL_SAMPLE_SIZE);
int N = N_ % 2 == 1 ? N_ : N_ + 1; /* Makes N an odd number (it is not important) */
if (useFFT_)
N = len4FFT(N_);
/* 2. Allocates data */
int k = m_InputList->size();
std::unique_ptr<DMatrix> x(new DMatrix(N, k)); /* Input data */
std::unique_ptr<IMatrix> p(new IMatrix(k, N)); /* Array of permutation vectors */
std::unique_ptr<ResultMatrix> y(new ResultMatrix(N, m_NumOutputs)); /* Output data */
double** xdata = x->getData();
int** pdata = p->getData();
double** ydata = y->getData();
/* 3. Sets up permuation vectors */
for (int iK=0; iK<k; ++iK)
{
for (int iN=0; iN<N; ++iN)
{
pdata[iK][iN] = iN;
}
m_RNG.shuffle(pdata[iK], N);
}
/* 4. Inits the pilot sample */
std::unique_ptr<double[]> x0ptr(new double[2*N]); /* pilot sample */
double* x0 = x0ptr.get();
double* s = &x0[N];
for (int i=0; i<N; ++i)
{
s[i] = MY_PI*(2*i+1-N)/N;
x0[i] = 0.5 + asin(sin(omega_*s[i]));
}
/* 5. Samples input data */
for (int iK=0; iK<k; ++iK)
{
for (int iN=0; iN<N; ++iN)
{
xdata[pdata[iK][iN]][iK] = x0[iN];
}
}
/* 6. Converts input data to target distributions */
m_RNG.convert(*x, m_InputList);
/* 7. Runs simulation */
simulate(*x, *y);
/* 8. Estimates sensitivity indices */
std::unique_ptr<double[]> ytmp_ptr(new double[N]);
double* ytmp = ytmp_ptr.get();
int* labels = y->getLabels();
for (int iK=0; iK<k; ++iK)
{
for (int iOut=0; iOut< m_NumOutputs; ++iOut)
{
for (int iN=0; iN<N; ++iN)
{
ytmp[iN] = ydata[pdata[iK][iN]][iOut];
}
interpolate(ytmp, labels, N);
if (useFFT_) { /* Estimate Fourier coeffients using FFT */
sdft(ytmp, N);
double V=0;
for (int iN=1; iN<N/2; ++iN)
{
V += ytmp[iN];
}
double Vi = 0;
for (int iM=1; iM<M_+1; ++iM)
{
Vi += ytmp[iM*omega_];
}
m_Sens->getRow(iK)[iOut]= Vi/V;
}
else /* Estimates Fourier coefficients directly */
{
double V=0, f0=0;
/* estimates total variance */
for (int iN=0; iN<N; ++iN)
{
f0 += ytmp[iN];
V += ytmp[iN] * ytmp[iN];
}
f0 /= N;
V /= N;
V -= f0*f0;
/* estimate Fourier coefficients and Vi */
double Vi = 0;
for (int iM=1; iM<M_+1; ++iM)
{
double A=0, B=0;
for (int iN = 0; iN<N; ++iN)
{
A += ytmp[iN] * cos(iM*omega_*s[iN]);
B += ytmp[iN] * sin(iM*omega_*s[iN]);
}
A /= N;
B /= N;
Vi += A*A + B*B;
}
m_Sens->getRow(iK)[iOut] = 2*Vi/V;
}
}
}
/* 9. Retains input/output data if needs */
if (m_SaveInput)
m_InputData = std::move(x);
if (m_SaveOutput)
m_OutputData = std::move(y);
}
