qFinderDMM::qFinderDMM(vector<vector<int> > cm, int p): countMatrix(cm), numPartitions(p){
try {
m = MothurOut::getInstance();
numSamples = (int)countMatrix.size();
numOTUs = (int)countMatrix[0].size();
kMeans();
// cout << "done kMeans" << endl;
optimizeLambda();
// cout << "done optimizeLambda" << endl;
double change = 1.0000;
currNLL = 0.0000;
int iter = 0;
while(change > 1.0e-6 && iter < 100){
// cout << "Calc_Z: ";
calculatePiK();
optimizeLambda();
// printf("Iter:%d\t",iter);
for(int i=0;i<numPartitions;i++){
weights[i] = 0.0000;
for(int j=0;j<numSamples;j++){
weights[i] += zMatrix[i][j];
}
// printf("w_%d=%.3f\t",i,weights[i]);
}
double nLL = getNegativeLogLikelihood();
change = abs(nLL - currNLL);
currNLL = nLL;
// printf("NLL=%.5f\tDelta=%.4e\n",currNLL, change);
iter++;
}
error.resize(numPartitions);
logDeterminant = 0.0000;
LinearAlgebra l;
for(currentPartition=0;currentPartition<numPartitions;currentPartition++){
error[currentPartition].assign(numOTUs, 0.0000);
if(currentPartition > 0){
logDeterminant += (2.0 * log(numSamples) - log(weights[currentPartition]));
}
vector<vector<double> > hessian = getHessian();
vector<vector<double> > invHessian = l.getInverse(hessian);
for(int i=0;i<numOTUs;i++){
logDeterminant += log(abs(hessian[i][i]));
error[currentPartition][i] = invHessian[i][i];
}
}
int numParameters = numPartitions * numOTUs + numPartitions - 1;
laplace = currNLL + 0.5 * logDeterminant - 0.5 * numParameters * log(2.0 * 3.14159);
bic = currNLL + 0.5 * log(numSamples) * numParameters;
aic = currNLL + numParameters;
}
catch(exception& e) {
m->errorOut(e, "qFinderDMM", "qFinderDMM");
exit(1);
}
}
qFinderDMM::qFinderDMM(vector<vector<int> > cm, int p) : CommunityTypeFinder() {
try {
//cout << "here" << endl;
numPartitions = p;
countMatrix = cm;
numSamples = (int)countMatrix.size();
numOTUs = (int)countMatrix[0].size();
// if (m->debug) { m->mothurOut("before kmeans\n"); }
findkMeans();
//if (m->debug) { m->mothurOut("done kMeans\n"); }
optimizeLambda();
//if (m->debug) { m->mothurOut("done optimizeLambda\n"); }
double change = 1.0000;
currNLL = 0.0000;
int iter = 0;
while(change > 1.0e-6 && iter < 100){
// if (m->debug) { m->mothurOut("Calc_Z: \n"); }
calculatePiK();
optimizeLambda();
// if (m->debug) { m->mothurOut("Iter: " + toString(iter) + "\n"); }
for(int i=0;i<numPartitions;i++){
weights[i] = 0.0000;
for(int j=0;j<numSamples;j++){
weights[i] += zMatrix[i][j];
}
// printf("w_%d=%.3f\t",i,weights[i]);
}
double nLL = getNegativeLogLikelihood();
change = abs(nLL - currNLL);
currNLL = nLL;
// printf("NLL=%.5f\tDelta=%.4e\n",currNLL, change);
iter++;
}
//if (m->debug) { m->mothurOut("done while loop\n"); }
error.resize(numPartitions);
logDeterminant = 0.0000;
LinearAlgebra l;
for(currentPartition=0;currentPartition<numPartitions;currentPartition++){
error[currentPartition].assign(numOTUs, 0.0000);
if (m->debug) { m->mothurOut("current partition = " + toString(currentPartition) + "\n"); }
if(currentPartition > 0){
logDeterminant += (2.0 * log(numSamples) - log(weights[currentPartition]));
}
//if (m->debug) { m->mothurOut("before hession\n"); }
vector<vector<double> > hessian = getHessian();
//if (m->debug) { m->mothurOut("after hession\n"); }
vector<vector<double> > invHessian = l.getInverse(hessian);
//if (m->debug) { m->mothurOut("after inverse\n"); }
for(int i=0;i<numOTUs;i++){
logDeterminant += log(abs(hessian[i][i]));
error[currentPartition][i] = invHessian[i][i];
}
}
int numParameters = numPartitions * numOTUs + numPartitions - 1;
laplace = currNLL + 0.5 * logDeterminant - 0.5 * numParameters * log(2.0 * 3.14159);
bic = currNLL + 0.5 * log(numSamples) * numParameters;
aic = currNLL + numParameters;
}
catch(exception& e) {
m->errorOut(e, "qFinderDMM", "qFinderDMM");
exit(1);
}
}
