exit(1);
}
}
-/**************************************************************************************************/
-//The silhouette width S(i)of individual data points i is calculated using the following formula:
-/*
- s(i) = b(i) - a(i)
- -----------
- max(b(i),a(i))
- where a(i) is the average dissimilarity (or distance) of sample i to all other samples in the same cluster, while b(i) is the average dissimilarity (or distance) to all objects in the closest other cluster.
-
- The formula implies -1 =< S(i) =< 1 . A sample which is much closer to its own cluster than to any other cluster has a high S(i) value, while S(i) close to 0 implies that the given sample lies somewhere between two clusters. Large negative S(i) values indicate that the sample was assigned to the wrong cluster.
- */
-vector<double> Pam::calcSilhouettes(vector<vector<double> > dists) {
- try {
- vector<double> silhouettes; silhouettes.resize(numSamples, 0.0);
- if (numPartitions < 2) { return silhouettes; }
-
- vector<int> whoDp;
- for (int i = 0; i < numSamples; i++) { // assumes at least 2 partitions
- vector<seqDist> tempMeds;
- for (set<int>::iterator it = medoids.begin(); it != medoids.end(); it++) {
- if (m->control_pressed) { break; }
- seqDist temp(*it, *it, dists[i][*it]); //medoid, medoid, distance from medoid to sample
- tempMeds.push_back(temp);
- }
- sort(tempMeds.begin(), tempMeds.end(), compareSequenceDistance); //sort by distance
- whoDp.push_back(tempMeds[1].seq1); //second closest medoid
- }
-
-
- vector<double> a, b; a.resize(numSamples, 0.0); b.resize(numSamples, 0.0);
-
- //calc a - all a[i] are the same in the same partition
- for (int k = 0; k < numPartitions; k++) {
- if (m->control_pressed) { break; }
-
- int count = 0;
- double totalA = 0.0;
- for (int i = 0; i < numSamples; i++) {
- for (int j = 0; j < numSamples; j++) {
- if (m->control_pressed) { break; }
- if ((zMatrix[k][i] != 0) && (zMatrix[k][j] != 0)){ //are both samples in the partition, if so add there distance
- totalA += dists[j][i]; //distance from this sample to the other samples in the partition
- count++;
- }
- }
- }
- totalA /= (double) count;
-
- //set a[i] to average for cluster
- for (int i = 0; i < numSamples; i++) {
- if (zMatrix[k][i] != 0) { a[i] = totalA; }
- }
- }
-
- //calc b
- for (int i = 0; i < numSamples; i++) {
- if (m->control_pressed) { break; }
-
- int nextClosestMedoid = whoDp[i];
- int thisPartition = medoid2Partition[nextClosestMedoid];
- int count = 0;
- double totalB = 0.0;
- for (int j = 0; j < numSamples; j++) {
- if (zMatrix[thisPartition][j] != 0) { //this sample is in this partition
- totalB += dists[i][j];
- count++;
- }
- }
- b[i] = totalB / (double) count;
- }
-
- //calc silhouettes
- for (int i = 0; i < numSamples; i++) {
- if (m->control_pressed) { break; }
-
- double denom = a[i];
- if (b[i] > denom) { denom = b[i]; } //max(a[i],b[i])
-
- silhouettes[i] = (b[i] - a[i]) / denom;
-
- //cout << "silhouettes " << i << '\t' << silhouettes[i] << endl;
- }
-
- return silhouettes;
- }
- catch(exception& e) {
- m->errorOut(e, "Pam", "calcSilhouettes");
- exit(1);
- }
-}
/**************************************************************************************************/
/*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.*/
+//based on R index.G1.r function
double Pam::calcCHIndex(vector< vector<double> > dists){ //countMatrix = [numSamples][numOtus]
try {
double CH = 0.0;
}
//make countMatrix a relabund
- vector<vector<double> > relativeAbundance(numSamples);
+ vector<vector<double> > relativeAbundance(numSamples); //[numSamples][numOTUs]
//get relative abundance
for(int i=0;i<numSamples;i++){
if (m->control_pressed) { return 0; }
countPartitions++;
}
- double sumBetweenCluster = 0.0;
- double sumWithinClusters = 0.0;
+ //centers.clear();
+ //centers = calcCenters(dists, clusterMap, relativeAbundance);
+
+ 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]));
+ }
+ }
- for (int i = 0; i < numSamples; i++) { //lt
- for (int j = 0; j < 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; }
- int partitionI = clusterMap[i];
- int partitionJ = clusterMap[j];
-
- if (partitionI == partitionJ) { //they are from the same cluster so this distance is added to Wk
- sumWithinClusters += (dists[i][j] * dists[i][j]);
- }else { //they are NOT from the same cluster so this distance is added to Bk
- sumBetweenCluster += (dists[i][j] * dists[i][j]);
- }
+ wgss += relativeAbundance[i][j];
}
}
- //cout << numPartitions << '\t' << sumWithinClusters << '\t' << sumBetweenCluster << '\t' << (numSamples - numPartitions) << endl;
- CH = (sumBetweenCluster / (double)(numPartitions - 1)) / (sumWithinClusters / (double) (numSamples - numPartitions));
+ double bgss = allMeanDist - wgss;
+
+ CH = (bgss / (double)(numPartitions - 1)) / (wgss / (double) (numSamples - numPartitions));
return CH;
}
/**************************************************************************************************/
-
projects / mothur.git / blobdiff