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//
// metrolognormal.c
// Mothur
//
// Created by Sarah Westcott on 4/25/19.
// Copyright © 2019 Schloss Lab. All rights reserved.
//
#include "metrolognormal.hpp"
/*constants for calculated compound Poisson lognormal*/
/***********************************************************************/
MetroLogNormal::MetroLogNormal(int fi, double sigx, double sigy, double sigS, int n, string st) : sigmaX(sigx), sigmaY(sigy), sigmaS(sigS), nIters(n), outFileStub(st), fitIters(fi), DiversityCalculator(false) {}
/***********************************************************************/
#ifdef USE_GSL
/***********************************************************************/
double nLogLikelihood1(const gsl_vector * x, void * params)
{
MothurOut* m = MothurOut::getInstance();
try {
double dMDash = gsl_vector_get(x,0), dV = gsl_vector_get(x,1);
int nS = (int) floor(gsl_vector_get(x, 2));
t_Data *ptData = (t_Data *) params;
int i = 0;
double dLogL = 0.0;
DiversityUtils dutils("metroln");
for(i = 0; i < ptData->nNA; i++){
if (m->getControl_pressed()) { break; }
double dLogP = 0.0;
int nA = ptData->aanAbund[i][0];
if(nA < 100){
dLogP = dutils.logLikelihoodQuad(nA, dMDash, dV);
}
else{
dLogP = dutils.logLikelihoodRampal(nA, dMDash, dV);
}
dLogL += ((double) ptData->aanAbund[i][1])*dLogP;
dLogL -= gsl_sf_lnfact(ptData->aanAbund[i][1]);
}
dLogL += (nS - ptData->nL)*dutils.logLikelihoodQuad(0, dMDash, dV);
dLogL -= gsl_sf_lnfact(nS - ptData->nL);
dLogL += gsl_sf_lnfact(nS);
/*return*/
return -dLogL;
}catch(exception& e) {
m->errorOut(e, "MetroLogNormal", "nLogLikelihood1");
exit(1);
}
}
/***********************************************************************/
double negLogLikelihood1(double dMDash, double dV, int nS, void * params)
{
MothurOut* m = MothurOut::getInstance();
try {
t_Data *ptData = (t_Data *) params;
int i = 0;
double dLog0 = 0.0, dLogL = 0.0;
DiversityUtils dutils("metroln");
for(i = 0; i < ptData->nNA; i++){
if (m->getControl_pressed()) { break; }
double dLogP = 0.0;
int nA = ptData->aanAbund[i][0];
if(nA < 100){
dLogP = dutils.logLikelihoodQuad(nA, dMDash, dV);
}
else{
dLogP = dutils.logLikelihoodRampal(nA, dMDash, dV);
}
dLogL += ((double) ptData->aanAbund[i][1])*dLogP;
dLogL -= gsl_sf_lnfact(ptData->aanAbund[i][1]);
}
dLog0 = dutils.logLikelihoodQuad(0, dMDash, dV);
if(nS > ptData->nL){
dLogL += (nS - ptData->nL)*dLog0;
}
dLogL -= gsl_sf_lnfact(nS - ptData->nL);
dLogL += gsl_sf_lnfact(nS);
return -dLogL;
}catch(exception& e) {
m->errorOut(e, "MetroLogNormal", "negLogLikelihood1");
exit(1);
}
}
/***********************************************************************/
void* metropolis1 (void * pvInitMetro) {
MothurOut* m = MothurOut::getInstance();
try {
t_MetroInit *ptMetroInit = (t_MetroInit *) pvInitMetro;
gsl_vector *ptX = ptMetroInit->ptX;
t_Data *ptData = ptMetroInit->ptData;
t_Params *ptParams = ptMetroInit->ptParams;
gsl_vector *ptXDash = gsl_vector_alloc(3); /*proposal*/
const gsl_rng_type *T;
gsl_rng *ptGSLRNG;
int nS = 0, nSDash = 0,nIter = 0;
double dRand = 0.0, dNLL = 0.0;
void *pvRet = nullptr;
double dM = 0.0, dV = 0.0;
double dMDash = 0.0, dVDash = 0.0;
double dXDash = 0.0, dX = 0.0;
/*set up random number generator*/
T = gsl_rng_default;
ptGSLRNG = gsl_rng_alloc (T);
nS = (int) floor(gsl_vector_get(ptX,2));
dNLL = negLogLikelihood1(gsl_vector_get(ptX,0), gsl_vector_get(ptX,1), nS,(void*) ptData);
dM = gsl_vector_get(ptX,0); dV = gsl_vector_get(ptX,1);
gsl_vector_set(ptX,0,dM + 0.5*dV);
string filename = ptParams->szOutFileStub + "_" + toString(ptMetroInit->nThread) + ".sample";
ofstream out; Utils util; util.openOutputFile(filename, out);
out.setf(ios::fixed, ios::floatfield); out.setf(ios::showpoint);
/*seed random number generator*/
gsl_rng_set(ptGSLRNG, ptMetroInit->lSeed);
DiversityUtils dutils("metroln");
/*now perform simple Metropolis algorithm*/
while(nIter < ptParams->nIter){
double dA = 0.0, dNLLDash = 0.0;
if (m->getControl_pressed()) { break; }
dutils.getProposal(ptGSLRNG, ptXDash, ptX, &nSDash, nS,ptParams);
dXDash = gsl_vector_get(ptXDash,0); dVDash = gsl_vector_get(ptXDash,1);
dMDash = dXDash - 0.5*dVDash;
dNLLDash = negLogLikelihood1(dMDash, dVDash, nSDash, (void*) ptData);
dA = exp(dNLL - dNLLDash);
if(dA > 1.0){
dA = 1.0;
}
dRand = gsl_rng_uniform(ptGSLRNG);
if(dRand < dA){
ptMetroInit->nAccepted++;
gsl_vector_memcpy(ptX, ptXDash);
nS = nSDash;
dNLL = dNLLDash;
}
if(nIter % 10 == 0){
dX = gsl_vector_get(ptX,0); dV = gsl_vector_get(ptX,1);
dM = dX - 0.5*dV;
out << nIter << "," << dM << "," << dV << "," << nS << "," << dNLL << endl;
}
nIter++;
}
out.close();
/*free up allocated memory*/
gsl_vector_free(ptXDash);
gsl_rng_free(ptGSLRNG);
return pvRet;
}catch(exception& e) {
m->errorOut(e, "MetroLogNormal", "metropolis1");
exit(1);
}
}
#endif
/***********************************************************************/
vector<string> MetroLogNormal::getValues(SAbundVector* rank){
try {
t_Params tParams; tParams.nIter = nIters; tParams.dSigmaX = sigmaX; tParams.dSigmaY = sigmaY; tParams.dSigmaS = sigmaS; tParams.szOutFileStub = outFileStub; tParams.lSeed = m->getRandomSeed();
t_Data tData;
int bestSample = 0;
#ifdef USE_GSL
DiversityUtils dutils("metroln");
dutils.loadAbundance(&tData, rank);
gsl_vector* ptX = gsl_vector_alloc(3);
gsl_rng_env_setup();
gsl_set_error_handler_off();
dutils.loadAbundance(&tData, rank);
int sampled = rank->getNumSeqs(); //nj
int numOTUs = rank->getNumBins(); //nl
gsl_vector_set(ptX, 0, 1.0); //INIT_M_DASH
gsl_vector_set(ptX, 1, 1.0); //INIT_V
gsl_vector_set(ptX, 2, numOTUs*2);
double chaoResult = dutils.chao(&tData);
m->mothurOut("\nMetroLogNormal - D = " + toString(numOTUs) + " L = " + toString(sampled) + " Chao = " + toString(chaoResult) + "\n");
dutils.minimiseSimplex(ptX, 3, (void*) &tData, &nLogLikelihood1, 1.0, 1.0e-2, 100000);
vector<double> parameterResults = dutils.outputResults(ptX, &tData, &nLogLikelihood1);
if(tParams.nIter > 0){
vector<double> acceptanceRates = dutils.mcmc(&tParams, &tData, ptX, &metropolis1); //sigmaX 0.1
if (fitIters != 0) { bestSample = dutils.fitSigma(acceptanceRates, parameterResults, fitIters, &tParams, &tData, ptX, &metropolis1); }
}
/*free up allocated memory*/
gsl_vector_free(ptX);
dutils.freeAbundance(&tData);
#endif
outputs.push_back(outFileStub + "_" + toString(bestSample) + ".sample");
if (bestSample == 0) { outputs.push_back(outFileStub + "_1.sample"); outputs.push_back(outFileStub + "_2.sample"); }
else if (bestSample == 1) { outputs.push_back(outFileStub + "_0.sample"); outputs.push_back(outFileStub + "_2.sample"); }
else if (bestSample == 2) { outputs.push_back(outFileStub + "_0.sample"); outputs.push_back(outFileStub + "_1.sample"); }
return outputs;
}
catch(exception& e) {
m->errorOut(e, "MetroLogNormal", "getValues");
exit(1);
}
}
/***********************************************************************/
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