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#ifndef __GC_FRAG_MODEL__
#define __GC_FRAG_MODEL__
#include "DistributionUtils.hpp"
#include "SalmonMath.hpp"
#include "Eigen/Dense"
#include <boost/iostreams/filtering_stream.hpp>
#include <vector>
#include <iostream>
struct GCDesc {
int32_t fragFrac;
int32_t contextFrac;
// assumes 101 bins
int32_t fragBin() { return fragFrac; }
int32_t contextBin() { return contextFrac; }
int32_t fragBin(int32_t n) {
double w = (100.0 / n);
return std::min(n-1, static_cast<int32_t>(fragFrac / w));
}
int32_t contextBin(int32_t n) {
double w = (100.0 / n);
return std::min(n-1, static_cast<int32_t>(contextFrac / w));
}
};
class GCFragModel {
public:
GCFragModel(size_t condBins=3,
size_t numGCBins=101,
distribution_utils::DistributionSpace dspace=distribution_utils::DistributionSpace::LOG) :
condBins_(condBins),
numGCBins_(numGCBins),
dspace_(dspace),
normalized_(false)
{
counts_ = Eigen::MatrixXd(condBins_, numGCBins_);
if (dspace_ == distribution_utils::DistributionSpace::LOG) {
counts_.setOnes();
counts_ *= salmon::math::LOG_0;
} else {
counts_.setZero();
}
}
bool writeBinary(boost::iostreams::filtering_ostream& out) const {
auto* mutThis = const_cast<GCFragModel*>(this);
int32_t dtype = (dspace_ == distribution_utils::DistributionSpace::LINEAR) ? 0 : 1;
out.write(reinterpret_cast<char*>(&dtype), sizeof(dtype));
typename Eigen::MatrixXd::Index rows= counts_.rows(), cols= counts_.cols();
out.write(reinterpret_cast<char*>(&rows), sizeof(typename Eigen::MatrixXd::Index));
out.write(reinterpret_cast<char*>(&cols), sizeof(typename Eigen::MatrixXd::Index));
out.write(reinterpret_cast<char*>(mutThis->counts_.data()), rows*cols*sizeof(typename Eigen::MatrixXd::Scalar));
return true;
}
GCFragModel(const GCFragModel&) = default;
GCFragModel(GCFragModel&&) = default;
GCFragModel& operator=(const GCFragModel&) = default;
GCFragModel& operator=(GCFragModel&&) = default;
void reset(distribution_utils::DistributionSpace dspace=distribution_utils::DistributionSpace::LOG) {
normalized_ = false;
dspace_=dspace;
if (dspace_ == distribution_utils::DistributionSpace::LOG) {
counts_.setOnes();
counts_ *= salmon::math::LOG_0;
} else {
counts_.setZero();
}
}
GCFragModel ratio(GCFragModel& other, double maxRatio) {
if (!normalized_) { normalize(); }
if (!other.normalized_) { other.normalize(); }
double minRatio = 1.0 / maxRatio;
GCFragModel ratioModel(condBins_, numGCBins_, dspace_);
for (size_t r = 0; r <condBins_; ++r) {
for (size_t c = 0; c < numGCBins_; ++c) {
double rat = (counts_(r,c) / other.counts_(r,c));
if (rat > maxRatio) { rat = maxRatio; }
if (rat < minRatio ) { rat = minRatio; }
ratioModel.counts_(r,c) = rat;
}
}
return ratioModel;
}
void inc(
GCDesc desc,
double fragWeight //< the weight associated with this fragment
) {
auto ctx = (condBins_ > 1) ? desc.contextBin(condBins_) : 0;
auto frag = (numGCBins_ != 101) ? desc.fragBin(numGCBins_) : desc.fragBin();
if (dspace_ == distribution_utils::DistributionSpace::LOG) {
counts_(ctx, frag) = salmon::math::logAdd(counts_(ctx, frag), fragWeight);
} else {
counts_(ctx, frag) += fragWeight;
}
}
double get(GCDesc desc) {
auto ctx = (condBins_ > 1) ? desc.contextBin(condBins_) : 0;
auto frag = (numGCBins_ != 101) ? desc.fragBin(numGCBins_) : desc.fragBin();
return counts_(ctx, frag);
}
distribution_utils::DistributionSpace distributionSpace() const { return dspace_; }
void combineCounts(const GCFragModel& other) {
if (dspace_ != other.dspace_) {
std::cerr << "Cannot combine distributions that live in a different space!\n";
std::exit(1);
}
if (dspace_ == distribution_utils::DistributionSpace::LOG) {
for (size_t r = 0; r <condBins_; ++r) {
for (size_t c = 0; c < numGCBins_; ++c) {
counts_(r,c) = salmon::math::logAdd(counts_(r,c), other.counts_(r,c));
}
}
} else {
for (size_t r = 0; r <condBins_; ++r) {
for (size_t c = 0; c < numGCBins_; ++c) {
counts_(r,c) += other.counts_(r,c);
}
}
}
}
/**
* NOTE: Improve interface --- also converts out of log space
*/
void normalize(double prior=0.1) {
if (!normalized_){
if (dspace_ == distribution_utils::DistributionSpace::LOG) {
prior = std::log(prior);
for (size_t r = 0; r < condBins_; ++r) {
double rowMass{salmon::math::LOG_0};
for (size_t c = 0; c < numGCBins_; ++c) {
rowMass = salmon::math::logAdd(prior, salmon::math::logAdd(rowMass, counts_(r,c)));
}
if (!salmon::math::isLog0(rowMass)) {
for (size_t c = 0; c < numGCBins_; ++c) {
counts_(r,c) = std::exp(salmon::math::logAdd(prior, counts_(r,c)) - rowMass);
}
}
}
} else {
for (size_t r = 0; r < condBins_; ++r) {
double rowMass = 0.0;
for (size_t c = 0; c < numGCBins_; ++c) {
rowMass += (prior + counts_(r,c));
}
if (rowMass > 0.0) {
double norm = 1.0 / rowMass;
for (size_t c = 0; c < numGCBins_; ++c) {
counts_(r,c) = (prior + counts_(r,c)) * norm;
}
}
}
}
normalized_ = true;
dspace_ = distribution_utils::DistributionSpace::LINEAR;
}
}
private:
size_t condBins_;
size_t numGCBins_;
distribution_utils::DistributionSpace dspace_;
bool normalized_;
Eigen::MatrixXd counts_;
};
#endif //__GC_FRAG_MODEL__
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