/** calculates HCL on SOTA cells (clusters) */
private void calculateClusterHCL() throws AlgorithmException {
AlgorithmEvent event = null;
event =
new AlgorithmEvent(
this,
AlgorithmEvent.SET_UNITS,
numberOfClusters,
"Calculate Hierarchical Trees SOTA Cluster Members");
fireValueChanged(event);
event.setIntValue(0);
event.setId(AlgorithmEvent.PROGRESS_VALUE);
fireValueChanged(event);
Cluster result_cluster = new Cluster();
NodeList nodeList = result_cluster.getNodeList();
// clusters are formed after growSOT
NodeList resultList = clusters.getNodeList();
Node currNode;
int[] probeIndexes;
for (int i = 0; i < numberOfClusters; i++) {
if (stop) {
throw new AbortException();
}
currNode = resultList.getNode(i);
probeIndexes = currNode.getProbesIndexes();
Node node = new Node(probeIndexes);
nodeList.addNode(node);
node.setValues(
calculateHierarchicalTree(probeIndexes, method, calculate_genes, calculate_experiments));
event.setIntValue(i + 1);
fireValueChanged(event);
}
if (result_cluster != null) inData.addCluster("hcl-result-clusters", result_cluster);
}
/** Calculates HCL tree over all genes */
private void calcFullTreeHCL() throws AlgorithmException {
AlgorithmEvent event = null;
event =
new AlgorithmEvent(
this,
AlgorithmEvent.SET_UNITS,
1,
"Calculate Hierarchical Tree, Clustering Experiments");
fireValueChanged(event);
event.setIntValue(0);
event.setId(AlgorithmEvent.PROGRESS_VALUE);
fireValueChanged(event);
Cluster result_cluster = new Cluster();
NodeList nodeList = result_cluster.getNodeList();
int[] probesIndexes = new int[numberOfGenes];
for (int i = 0; i < numberOfGenes; i++) {
probesIndexes[i] = i;
}
int[] featuresIndexes = new int[numberOfSamples];
for (int i = 0; i < numberOfSamples; i++) {
featuresIndexes[i] = i;
}
if (stop) {
throw new AbortException();
}
Node node = new Node(featuresIndexes);
nodeList.addNode(node);
node.setValues(calculateHierarchicalTree(probesIndexes, method, false, true));
event.setIntValue(1);
fireValueChanged(event);
if (result_cluster != null) inData.addCluster("full-tree-sample-HCL", result_cluster);
}
/** grows SOT */
private void growSOT() {
AlgorithmEvent event = new AlgorithmEvent(this, AlgorithmEvent.SET_UNITS, maxNumCycles);
fireValueChanged(event);
AlgorithmEvent event1 =
new AlgorithmEvent(this, AlgorithmEvent.SET_UNITS, maxNumCycles, "Growing Tree");
fireValueChanged(event1);
event1.setIntValue(0);
event1.setId(AlgorithmEvent.PROGRESS_VALUE);
fireValueChanged(event1);
float currDivFraction;
boolean stopGrowing = false;
float lowerLim;
if (!useClusterVariance && mostDiverseCell.cellDiversity < endCriteria) stopGrowing = true;
else if (useClusterVariance && mostVariableCell.cellVariance < endCriteria) stopGrowing = true;
for (cycleNum = 0; cycleNum < maxNumCycles && !stopGrowing; cycleNum++) {
runCycle(maxNumEpochs, epochCriteria);
// training has optimized the sub-system centroids, trained node's profiles have been
// distributed
// among potentially any cluster (closest chosen)
// now with all profiles assigned to cells, get the cell's and tree's diversity (Resource)
setDiversities();
cycleDiversity.add(new Float(treeDiversity));
if (!useClusterVariance) {
if (mostDiverseCell.cellDiversity > endCriteria
&& cycleNum < maxNumCycles - 1) // don't split if last cycle in maxNumCycles
divideCell(mostDiverseCell);
else stopGrowing = true;
} else { // using variabliity
if (mostVariableCell.cellVariance > endCriteria
&& cycleNum < maxNumCycles - 1) // don't split if last cycle in maxNumCycles
divideCell(mostVariableCell);
else stopGrowing = true;
}
// advance monitor
event1.setId(AlgorithmEvent.PROGRESS_VALUE);
event1.setIntValue(cycleNum + 1);
fireValueChanged(event1);
// calculate and set monitor values
// event.setId(AlgorithmEvent.MONITOR_VALUE);
/* DISABLED OPTIONAL DIVERSITY MONITOR
if(myFactor == 1)
currDivFraction = (float)(treeDiversity/initDivSum);
else{
lowerLim = numberOfGenes * myFactor;
currDivFraction = (float)((treeDiversity + Math.abs(lowerLim))/(initDivSum+Math.abs(lowerLim)));
}
if(cycleNum < 245){ //monitor values limit is 245
event.setIntValue((int)(100*currDivFraction));
fireValueChanged(event);
}
*/
}
trainLeaves(
maxNumEpochs,
epochCriteria); // reached stopping criteria, now just need to train leaves so centroids
// migrate to center
getResults(); // add results to AlgorithmData (inData)
return;
}