/** Given an ArgList containing name,[min,max,step,bins,col,N], set up a
* coordinate with that name and parameters min, max, step, bins.
* If '*' or not specified, a default value will be set.
* \return 1 if error occurs, 0 otherwise.
*/
int Analysis_Hist::setupDimension(ArgList &arglist, DataSet_1D const& dset, size_t& offset) {
bool minArg = false;
bool maxArg = false;
bool stepArg = false;
bool binsArg = false;
if (debug_>1)
arglist.PrintList();
// Set up dimension name
// NOTE: arglist[0] should be same as dset name from CheckDimension
std::string const& dLabel = arglist[0];
// Cycle through coordinate arguments. Any argument left blank will be
// assigned a default value later.
double dMin = 0.0;
double dMax = 0.0;
double dStep = 0.0;
int dBins = -1;
for (int i = 1; i < arglist.Nargs(); i++) {
if (debug_>1) mprintf("DEBUG: setupCoord: Token %i (%s)\n", i, arglist[i].c_str());
// '*' means default explicitly requested
if (arglist[i] == "*") continue;
switch (i) {
case 1 : dMin = convertToDouble( arglist[i]); minArg = true; break;
case 2 : dMax = convertToDouble( arglist[i]); maxArg = true; break;
case 3 : dStep = convertToDouble( arglist[i]); stepArg = true; break;
case 4 : dBins = convertToInteger(arglist[i]); binsArg = true; break;
}
}
// If no min arg and no default min arg, get min from dataset
if (!minArg) {
if (!minArgSet_)
dMin = dset.Min();
else
dMin = default_min_;
}
// If no max arg and no default max arg, get max from dataset
if (!maxArg) {
if (!maxArgSet_)
dMax = dset.Max();
else
dMax = default_max_;
}
// If bins/step not specified, use default
if (!binsArg)
dBins = default_bins_;
if (!stepArg)
dStep = default_step_;
// Calculate dimension from given args.
HistBin dim;
if (dim.CalcBinsOrStep( dMin, dMax, dStep, dBins, dLabel )) {
mprinterr("Error: Could not set up histogram dimension '%s'\n", dLabel.c_str());
return 1;
}
dim.PrintHistBin();
dimensions_.push_back( dim );
// Recalculate offsets for all dimensions starting at farthest coord. This
// follows row major ordering.
size_t last_offset = 1UL; // For checking overflow.
offset = 1UL;
binOffsets_.resize( dimensions_.size() );
OffType::iterator bOff = binOffsets_.begin();
for (HdimType::const_iterator rd = dimensions_.begin();
rd != dimensions_.end(); ++rd, ++bOff)
{
if (debug_>0) mprintf("\tHistogram: %s offset is %zu\n", rd->label(), offset);
*bOff = (long int)offset;
offset *= rd->Bins();
// Check for overflow.
if ( offset < last_offset ) {
mprinterr("Error: Too many bins for histogram. Try reducing the number of bins and/or\n"
"Error: the number of dimensions.\n");
return 1;
}
last_offset = offset;
}
// offset should now be equal to the total number of bins across all dimensions
if (debug_>0) mprintf("\tHistogram: Total Bins = %zu\n",offset);
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);
}
// Analysis_KDE::Analyze()
Analysis::RetType Analysis_KDE::Analyze() {
DataSet_1D const& Pdata = static_cast<DataSet_1D const&>( *data_ );
int inSize = (int)Pdata.Size();
// Set output set dimensions from input set if necessary.
if (!minArgSet_) {
mprintf("\tNo minimum specified, determining from input data.\n");
if (q_data_ != 0)
default_min_ = std::max(((DataSet_1D*)q_data_)->Min(), Pdata.Min());
else
default_min_ = Pdata.Min();
}
if (!maxArgSet_) {
mprintf("\tNo maximum specified, determining from input data.\n");
if (q_data_ != 0)
default_max_ = std::min(((DataSet_1D*)q_data_)->Max(), Pdata.Max());
else
default_max_ = Pdata.Max();
}
HistBin Xdim;
if (Xdim.CalcBinsOrStep(default_min_, default_max_, default_step_,
default_bins_, Pdata.Meta().Legend()))
return Analysis::ERR;
Xdim.PrintHistBin();
output_->SetDim( Dimension::X, Xdim );
// Allocate output set
DataSet_double& P_hist = static_cast<DataSet_double&>( *output_ );
P_hist.Resize( Xdim.Bins() );
int outSize = (int)P_hist.Size();
// Estimate bandwidth from normal distribution approximation if necessary.
if (bandwidth_ < 0.0) {
double stdev;
Pdata.Avg( stdev );
double N_to_1_over_5 = pow( (double)inSize, (-1.0/5.0) );
bandwidth_ = 1.06 * stdev * N_to_1_over_5;
mprintf("\tDetermined bandwidth from normal distribution approximation: %f\n", bandwidth_);
}
// Set up increments
std::vector<double> Increments(inSize, 1.0);
if (amddata_ != 0) {
DataSet_1D& AMD = static_cast<DataSet_1D&>( *amddata_ );
if ((int)AMD.Size() != inSize) {
if ((int)AMD.Size() < inSize) {
mprinterr("Error: Size of AMD data set %zu < input data set iu\n",
AMD.Size(), inSize);
return Analysis::ERR;
} else {
mprintf("Warning: Size of AMD data set %zu > input data set %i\n",
AMD.Size(), inSize);
}
}
for (int i = 0; i < inSize; i++)
Increments[i] = exp( AMD.Dval(i) );
}
int frame, bin;
double increment;
double total = 0.0;
# ifdef _OPENMP
int numthreads;
# pragma omp parallel
{
# pragma omp master
{
numthreads = omp_get_num_threads();
mprintf("\tParallelizing calculation with %i threads\n", numthreads);
}
}
# endif
if (q_data_ == 0) {
double val;
// 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
P_hist[bin] +=
# endif
(increment * (this->*Kernel_)( (Xdim.Coord(bin) - val) / bandwidth_ ));
}
# ifdef _OPENMP
} // END parallel block
// Combine results from each thread histogram into P_hist
for (int i = 0; i < numthreads; i++) {
for (int j = 0; j < outSize; j++)
P_hist[j] += P_thread[i][j];
delete[] P_thread[i];
}
delete[] P_thread;
# endif
} else {
// Calculate Kullback-Leibler divergence vs time
DataSet_1D const& Qdata = static_cast<DataSet_1D const&>( *q_data_ );
if (inSize != (int)Qdata.Size()) {
mprintf("Warning: Size of %s (%zu) != size of %s (%zu)\n",
Pdata.legend(), Pdata.Size(), Qdata.legend(), Qdata.Size());
inSize = std::min( inSize, (int)Qdata.Size() );
mprintf("Warning: Only using %i data points.\n", inSize);
}
DataSet_double& klOut = static_cast<DataSet_double&>( *kldiv_ );
std::vector<double> Q_hist( Xdim.Bins(), 0.0 ); // Raw Q histogram.
klOut.Resize( inSize ); // Hold KL div vs time
double val_p, val_q, KL, xcrd, Pnorm, Qnorm, normP, normQ;
bool Pzero, Qzero;
// Loop over input P and Q data
unsigned int nInvalid = 0, validPoint;
for (frame = 0; frame < inSize; frame++) {
//mprintf("DEBUG: Frame=%i Outsize=%i\n", frame, outSize);
increment = Increments[frame];
total += increment;
// Apply kernel across P and Q, calculate KL divergence as we go.
val_p = Pdata.Dval(frame);
val_q = Qdata.Dval(frame);
normP = 0.0;
normQ = 0.0;
validPoint = 0; // 0 in this context means true
# ifdef _OPENMP
# pragma omp parallel private(bin, xcrd) reduction(+:normP, normQ)
{
# pragma omp for
# endif
for (bin = 0; bin < outSize; bin++) {
xcrd = Xdim.Coord(bin);
P_hist[bin] += (increment * (this->*Kernel_)( (xcrd - val_p) / bandwidth_ ));
normP += P_hist[bin];
Q_hist[bin] += (increment * (this->*Kernel_)( (xcrd - val_q) / bandwidth_ ));
normQ += Q_hist[bin];
}
# ifdef _OPENMP
} // End first parallel block
# endif
if (normP > std::numeric_limits<double>::min())
normP = 1.0 / normP;
if (normQ > std::numeric_limits<double>::min())
normQ = 1.0 / normQ;
KL = 0.0;
# ifdef _OPENMP
# pragma omp parallel private(bin, Pnorm, Qnorm, Pzero, Qzero) reduction(+:KL, validPoint)
{
# pragma omp for
# endif
for (bin = 0; bin < outSize; bin++) {
// KL only defined when Q and P are non-zero, or both zero.
if (validPoint == 0) {
// Normalize for this frame
Pnorm = P_hist[bin] * normP;
Qnorm = Q_hist[bin] * normQ;
//mprintf("Frame %8i Bin %8i P=%g Q=%g Pnorm=%g Qnorm=%g\n",frame,bin,P_hist[bin],Q_hist[bin],normP,normQ);
Pzero = (Pnorm <= std::numeric_limits<double>::min());
Qzero = (Qnorm <= std::numeric_limits<double>::min());
if (!Pzero && !Qzero)
KL += ( log( Pnorm / Qnorm ) * Pnorm );
else if ( Pzero != Qzero )
validPoint++;
}
}
# ifdef _OPENMP
} // End second parallel block
# endif
if (validPoint == 0) {
klOut[frame] = KL;
} else {
//mprintf("Warning:\tKullback-Leibler divergence is undefined for frame %i\n", frame+1);
nInvalid++;
}
} // END KL divergence calc loop over frames
if (nInvalid > 0)
mprintf("Warning:\tKullback-Leibler divergence was undefined for %u frames.\n", nInvalid);
}
// Normalize
for (unsigned int j = 0; j < P_hist.Size(); j++)
P_hist[j] /= (total * bandwidth_);
// Calc free E
if (calcFreeE_) {
double KT = (-Constants::GASK_KCAL * Temp_);
double minFreeE = 0.0;
for (unsigned int j = 0; j < P_hist.Size(); j++) {
P_hist[j] = log( P_hist[j] ) * KT;
if (j == 0)
minFreeE = P_hist[j];
else if (P_hist[j] < minFreeE)
minFreeE = P_hist[j];
}
for (unsigned int j = 0; j < P_hist.Size(); j++)
P_hist[j] -= minFreeE;
}
return Analysis::OK;
}
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;
}
