static inline int CheckSet(DataSet_1D const& ds) {
if (ds.Size() < 1) {
mprinterr("Error: Set '%s' is empty.\n", ds.legend());
return Analysis::ERR;
}
mprintf("\t%s (%zu points).\n", ds.legend(), ds.Size());
return 0;
}
int KDE::CalcKDE(DataSet_double& Out, DataSet_1D const& Pdata,
std::vector<double> const& Increments,
HistBin const& Xdim, double bandwidth) const
{
int inSize = (int)Pdata.Size();
// Allocate output set, set all to zero.
Out.Zero( Xdim.Bins() );
Out.SetDim( Dimension::X, Xdim );
int outSize = (int)Out.Size();
int frame, bin;
double increment, val;
double total = 0.0;
# ifdef _OPENMP
int original_num_threads;
# pragma omp parallel
{
# pragma omp master
{
original_num_threads = omp_get_num_threads();
}
}
// Ensure we only execute with the desired number of threads
if (numthreads_ < original_num_threads)
omp_set_num_threads( numthreads_ );
# endif
// Calculate KDE, loop over input data
# ifdef _OPENMP
int mythread;
double **P_thread;
# pragma omp parallel private(frame, bin, val, increment, mythread) reduction(+:total)
{
mythread = omp_get_thread_num();
// Prevent race conditions by giving each thread its own histogram
# pragma omp master
{
P_thread = new double*[ numthreads_ ];
for (int nt = 0; nt < numthreads_; nt++) {
P_thread[nt] = new double[ outSize ];
std::fill(P_thread[nt], P_thread[nt] + outSize, 0.0);
}
}
# pragma omp barrier
# pragma omp for
# endif
for (frame = 0; frame < inSize; frame++) {
val = Pdata.Dval(frame);
increment = Increments[frame];
total += increment;
// Apply kernel across histogram
for (bin = 0; bin < outSize; bin++)
# ifdef _OPENMP
P_thread[mythread][bin] +=
# else
Out[bin] +=
# endif
(increment * (this->*Kernel_)( (Xdim.Coord(bin) - val) / bandwidth ));
}
# ifdef _OPENMP
} // END parallel block
// Combine results from each thread histogram into Out
for (int i = 0; i < numthreads_; i++) {
for (int j = 0; j < outSize; j++)
Out[j] += P_thread[i][j];
delete[] P_thread[i];
}
delete[] P_thread;
// Restore original number of threads
if (original_num_threads != numthreads_)
omp_set_num_threads( original_num_threads );
# endif
// Normalize
for (unsigned int j = 0; j < Out.Size(); j++)
Out[j] /= (total * bandwidth);
return 0;
}
int KDE::CalcKDE(DataSet_double& Out, DataSet_1D const& Pdata) const {
if (Pdata.Size() < 2) {
mprinterr("Error: Not enough data for KDE.\n");
return 1;
}
// Automatically determine min, max, step, and bin values.
// std::vector<double> data;
// data.reserve( Pdata.Size() );
double N = 0.0;
double mean = 0.0;
double M2 = 0.0;
double min = Pdata.Dval(0);
double max = min;
for (unsigned int i = 0; i != Pdata.Size(); i++) {
double x = Pdata.Dval(i);
min = std::min(min, x);
max = std::max(max, x);
N++;
double delta = x - mean;
mean += delta / N;
M2 += delta * (x - mean);
// data.push_back( x );
}
M2 /= (N - 1.0);
double stdev = sqrt(M2);
double step = 0.0;
int bins = (int)sqrt((double)Pdata.Size());
/*
std::sort(data.begin(), data.end());
double min = data.front();
double max = data.back();
unsigned int upperidx, loweridx;
if ( (data.size() % 2) == 0 ) {
// Even number of points. Get Q1 as median of lower and Q3 as median of upper.
unsigned int halfsize = data.size() / 2;
loweridx = ((halfsize - 1) / 2);
upperidx = loweridx + halfsize;
} else {
// Odd number of points. Include the median in both halves
unsigned int lsize = (data.size() + 1) / 2;
loweridx = ((lsize - 1) / 2);
unsigned int usize = (data.size() - 1) / 2;
upperidx = loweridx + usize;
}
double Q1 = data[loweridx];
double Q3 = data[upperidx];
double step = 2 * ((Q3 - Q1) / pow(data.size(), 1/3));
int bins = 0;
mprintf("DEBUG: Q1= %g, Q3= %g, step= %g, min= %g, max= %g, mean= %g, stdev= %g\n",
Q1, Q3, step, min, max, mean, stdev);
if (max - min < step) {
// Would only be 1 bin. Probably noisy.
mprintf("Warning: Data set is very sparse.\n");
bins = (int)Pdata.Size() / 10;
step = 0;
}
*/
mprintf("DEBUG: mean= %g, stdev= %g\n", mean, stdev);
HistBin Xdim;
if (Xdim.CalcBinsOrStep(min, max, step, bins, Pdata.Meta().Legend()))
return 1;
Xdim.PrintHistBin();
// Automatically determine bandwidth
double bandwidth = 1.06 * stdev * BandwidthFactor(Pdata.Size());
mprintf("\tBandwidth: %f\n", bandwidth);
std::vector<double> Increments(Pdata.Size(), 1.0);
return CalcKDE(Out, Pdata, Increments, Xdim, bandwidth);
}
int KDE::CalcKDE(DataSet_double& Out, DataSet_1D const& Pdata,
HistBin const& Xdim, double bandwidth) const
{
std::vector<double> Increments(Pdata.Size(), 1.0);
return CalcKDE(Out, Pdata, Increments, Xdim, bandwidth);
}
