/*To assess the optimal number of clusters our dataset was most robustly partitioned into, we used the Calinski-Harabasz (CH) Index that has shown good performance in recovering the number of clusters. It is defined as:
CHk=Bk/(k−1)/Wk/(n−k)
where Bk is the between-cluster sum of squares (i.e. the squared distances between all points i and j, for which i and j are not in the same cluster) and Wk is the within-clusters sum of squares (i.e. the squared distances between all points i and j, for which i and j are in the same cluster). This measure implements the idea that the clustering is more robust when between-cluster distances are substantially larger than within-cluster distances. Consequently, we chose the number of clusters k such that CHk was maximal.*/
double CommunityTypeFinder::calcCHIndex(vector< vector< double> > dists){
try {
double CH = 0.0;
if (numPartitions < 2) { return CH; }
map<int, int> clusterMap; //map sample to partition
for (int j = 0; j < numSamples; j++) {
double maxValue = -1e6;
for (int i = 0; i < numPartitions; i++) {
if (m->control_pressed) { return 0.0; }
if (zMatrix[i][j] > maxValue) { //for kmeans zmatrix contains values for each sample in each partition. partition with highest value for that sample is the partition where the sample should be
clusterMap[j] = i;
maxValue = zMatrix[i][j];
}
}
}
//make countMatrix a relabund
vector<vector<double> > relativeAbundance(numSamples); //[numSamples][numOTUs]
//get relative abundance
for(int i=0;i<numSamples;i++){
if (m->control_pressed) { return 0; }
int groupTotal = 0;
relativeAbundance[i].assign(numOTUs, 0.0);
for(int j=0;j<numOTUs;j++){
groupTotal += countMatrix[i][j];
}
for(int j=0;j<numOTUs;j++){
relativeAbundance[i][j] = countMatrix[i][j] / (double)groupTotal;
}
}
//find centers
vector<vector<double> > centers = calcCenters(dists, clusterMap, relativeAbundance);
if (m->control_pressed) { return 0.0; }
double allMeanDist = rMedoid(relativeAbundance, dists);
if (m->debug) { m->mothurOut("[DEBUG]: allMeandDist = " + toString(allMeanDist) + "\n"); }
for (int i = 0; i < relativeAbundance.size(); i++) {//numSamples
for (int j = 0; j < relativeAbundance[i].size(); j++) { //numOtus
if (m->control_pressed) { return 0; }
//x <- (x - centers[cl, ])^2
relativeAbundance[i][j] = ((relativeAbundance[i][j] - centers[clusterMap[i]][j])*(relativeAbundance[i][j] - centers[clusterMap[i]][j]));
}
}
double wgss = 0.0;
for (int j = 0; j < numOTUs; j++) {
for(int i=0;i<numSamples;i++){
if (m->control_pressed) { return 0.0; }
wgss += relativeAbundance[i][j];
}
}
double bgss = allMeanDist - wgss;
CH = (bgss / (double)(numPartitions - 1)) / (wgss / (double) (numSamples - numPartitions));
return CH;
}
catch(exception& e){
m->errorOut(e, "CommunityTypeFinder", "calcCHIndex");
exit(1);
}
}
void qFinderDMM::kMeans(){
try {
vector<vector<double> > relativeAbundance(numSamples);
vector<vector<double> > alphaMatrix;
alphaMatrix.resize(numPartitions);
lambdaMatrix.resize(numPartitions);
for(int i=0;i<numPartitions;i++){
alphaMatrix[i].assign(numOTUs, 0);
lambdaMatrix[i].assign(numOTUs, 0);
}
//get relative abundance
for(int i=0;i<numSamples;i++){
if (m->control_pressed) { return; }
int groupTotal = 0;
relativeAbundance[i].assign(numOTUs, 0.0);
for(int j=0;j<numOTUs;j++){
groupTotal += countMatrix[i][j];
}
for(int j=0;j<numOTUs;j++){
relativeAbundance[i][j] = countMatrix[i][j] / (double)groupTotal;
}
}
//randomly assign samples into partitions
zMatrix.resize(numPartitions);
for(int i=0;i<numPartitions;i++){
zMatrix[i].assign(numSamples, 0);
}
for(int i=0;i<numSamples;i++){
zMatrix[rand()%numPartitions][i] = 1;
}
double maxChange = 1;
int maxIters = 1000;
int iteration = 0;
weights.assign(numPartitions, 0);
while(maxChange > 1e-6 && iteration < maxIters){
if (m->control_pressed) { return; }
//calcualte average relative abundance
maxChange = 0.0000;
for(int i=0;i<numPartitions;i++){
double normChange = 0.0;
weights[i] = 0;
for(int j=0;j<numSamples;j++){
weights[i] += (double)zMatrix[i][j];
}
vector<double> averageRelativeAbundance(numOTUs, 0);
for(int j=0;j<numOTUs;j++){
for(int k=0;k<numSamples;k++){
averageRelativeAbundance[j] += zMatrix[i][k] * relativeAbundance[k][j];
}
}
for(int j=0;j<numOTUs;j++){
averageRelativeAbundance[j] /= weights[i];
double difference = averageRelativeAbundance[j] - alphaMatrix[i][j];
normChange += difference * difference;
alphaMatrix[i][j] = averageRelativeAbundance[j];
}
normChange = sqrt(normChange);
if(normChange > maxChange){ maxChange = normChange; }
}
//calcualte distance between each sample in partition adn the average relative abundance
for(int i=0;i<numSamples;i++){
if (m->control_pressed) { return; }
double normalizationFactor = 0;
vector<double> totalDistToPartition(numPartitions, 0);
for(int j=0;j<numPartitions;j++){
for(int k=0;k<numOTUs;k++){
double difference = alphaMatrix[j][k] - relativeAbundance[i][k];
totalDistToPartition[j] += difference * difference;
}
totalDistToPartition[j] = sqrt(totalDistToPartition[j]);
normalizationFactor += exp(-50.0 * totalDistToPartition[j]);
}
for(int j=0;j<numPartitions;j++){
zMatrix[j][i] = exp(-50.0 * totalDistToPartition[j]) / normalizationFactor;
}
}
iteration++;
// cout << "K means: " << iteration << '\t' << maxChange << endl;
}
// cout << "Iter:-1";
for(int i=0;i<numPartitions;i++){
weights[i] = 0.0000;
for(int j=0;j<numSamples;j++){
weights[i] += zMatrix[i][j];
}
// printf("\tw_%d=%.3f", i, weights[i]);
}
// cout << endl;
for(int i=0;i<numOTUs;i++){
if (m->control_pressed) { return; }
for(int j=0;j<numPartitions;j++){
if(alphaMatrix[j][i] > 0){
lambdaMatrix[j][i] = log(alphaMatrix[j][i]);
}
else{
lambdaMatrix[j][i] = -10.0;
}
}
}
}
catch(exception& e){
m->errorOut(e, "qFinderDMM", "kMeans");
exit(1);
}
}
int CommunityTypeFinder::findkMeans(){
try {
error.resize(numPartitions); for (int i = 0; i < numPartitions; i++) { error[i].resize(numOTUs, 0.0); }
vector<vector<double> > relativeAbundance(numSamples);
vector<vector<double> > alphaMatrix;
alphaMatrix.resize(numPartitions);
lambdaMatrix.resize(numPartitions);
for(int i=0;i<numPartitions;i++){
alphaMatrix[i].assign(numOTUs, 0);
lambdaMatrix[i].assign(numOTUs, 0);
}
//get relative abundance
for(int i=0;i<numSamples;i++){
if (m->control_pressed) { return 0; }
int groupTotal = 0;
relativeAbundance[i].assign(numOTUs, 0.0);
for(int j=0;j<numOTUs;j++){
groupTotal += countMatrix[i][j];
}
for(int j=0;j<numOTUs;j++){
relativeAbundance[i][j] = countMatrix[i][j] / (double)groupTotal;
}
}
//randomly assign samples into partitions
zMatrix.resize(numPartitions);
for(int i=0;i<numPartitions;i++){
zMatrix[i].assign(numSamples, 0);
}
//randomize samples
vector<int> temp;
for (int i = 0; i < numSamples; i++) { temp.push_back(i); }
random_shuffle(temp.begin(), temp.end());
//assign each partition at least one random sample
int numAssignedSamples = 0;
for (int i = 0; i < numPartitions; i++) {
zMatrix[i][temp[numAssignedSamples]] = 1;
numAssignedSamples++;
}
//assign rest of samples to partitions
int count = 0;
for(int i=numAssignedSamples;i<numSamples;i++){
zMatrix[count%numPartitions][temp[i]] = 1;
count++;
}
double maxChange = 1;
int maxIters = 1000;
int iteration = 0;
weights.assign(numPartitions, 0);
while(maxChange > 1e-6 && iteration < maxIters){
if (m->control_pressed) { return 0; }
//calcualte average relative abundance
maxChange = 0.0000;
for(int i=0;i<numPartitions;i++){
double normChange = 0.0;
weights[i] = 0;
for(int j=0;j<numSamples;j++){
weights[i] += (double)zMatrix[i][j];
}
vector<double> averageRelativeAbundance(numOTUs, 0);
for(int j=0;j<numOTUs;j++){
for(int k=0;k<numSamples;k++){
averageRelativeAbundance[j] += zMatrix[i][k] * relativeAbundance[k][j];
}
}
for(int j=0;j<numOTUs;j++){
averageRelativeAbundance[j] /= weights[i];
double difference = averageRelativeAbundance[j] - alphaMatrix[i][j];
normChange += difference * difference;
alphaMatrix[i][j] = averageRelativeAbundance[j];
}
normChange = sqrt(normChange);
if(normChange > maxChange){ maxChange = normChange; }
}
//calcualte distance between each sample in partition and the average relative abundance
for(int i=0;i<numSamples;i++){
if (m->control_pressed) { return 0; }
double normalizationFactor = 0;
vector<double> totalDistToPartition(numPartitions, 0);
for(int j=0;j<numPartitions;j++){
for(int k=0;k<numOTUs;k++){
double difference = alphaMatrix[j][k] - relativeAbundance[i][k];
totalDistToPartition[j] += difference * difference;
}
totalDistToPartition[j] = sqrt(totalDistToPartition[j]);
normalizationFactor += exp(-50.0 * totalDistToPartition[j]);
}
for(int j=0;j<numPartitions;j++){
zMatrix[j][i] = exp(-50.0 * totalDistToPartition[j]) / normalizationFactor;
}
}
iteration++;
// cout << "K means: " << iteration << '\t' << maxChange << endl;
}
// cout << "Iter:-1";
for(int i=0;i<numPartitions;i++){
weights[i] = 0.0000;
for(int j=0;j<numSamples;j++){
weights[i] += zMatrix[i][j];
}
// printf("\tw_%d=%.3f", i, weights[i]);
}
// cout << endl;
for(int i=0;i<numOTUs;i++){
if (m->control_pressed) { return 0; }
for(int j=0;j<numPartitions;j++){
if(alphaMatrix[j][i] > 0){
lambdaMatrix[j][i] = log(alphaMatrix[j][i]);
}
else{
lambdaMatrix[j][i] = -10.0;
}
}
}
return 0;
}
catch(exception& e){
m->errorOut(e, "CommunityTypeFinder", "kMeans");
exit(1);
}
}
