// Note that leaves are threaded from left to right.
// This means that if displayed top to bottom, centroids would be reversed
// Therefore, accumulate in reverse order into AlgorithmData
private void getResults() {
SOTACell curr = head;
int numCells = 0;
FloatMatrix centroidFM = new FloatMatrix(numberOfClusters, numberOfSamples);
FloatMatrix varianceFM = new FloatMatrix(numberOfClusters, numberOfSamples);
int[] clusterSize = new int[numberOfClusters];
FloatMatrix clusterDiversity = new FloatMatrix(numberOfClusters, 1);
int numDiv = cycleDiversity.size();
FloatMatrix cycleDivFM = new FloatMatrix(numDiv, 1);
int[] clusterOrder = new int[numberOfClusters];
clusters = new Cluster();
NodeList nodeList = clusters.getNodeList();
Node newNode;
int[] clusterMembership;
int clusterPop;
// move to tail
while (curr.succ != null) curr = curr.succ;
// now curr is at the tail
while (numCells <= numberOfClusters && curr != null) {
for (int i = 0; i < numberOfSamples; i++) {
centroidFM.set(numCells, i, curr.centroidGene.get(0, i));
varianceFM.set(numCells, i, curr.getColumnVar(i));
}
clusterPop = curr.members.size();
clusterSize[numCells] = clusterPop;
clusterDiversity.set(
numCells,
0,
(float) curr.cellDiversity
* (float) myFactor); // alter poloarity by myFactor based on metric
clusterOrder[numCells] = numCells;
// accumulate cluster probe indicies
clusterMembership = new int[clusterPop];
for (int i = 0; i < clusterPop; i++) {
clusterMembership[i] = ((Integer) (curr.members.elementAt(i))).intValue();
}
newNode = new Node();
newNode.setProbesIndexes(clusterMembership);
nodeList.addNode(newNode);
numCells++;
curr = curr.pred;
}
// now accumlate cycle divresity information
if (myFactor == 1) {
float initDiv = ((Float) (cycleDiversity.elementAt(0))).floatValue();
for (int i = 0; i < numDiv; i++) {
cycleDivFM.set(i, 0, (((Float) (cycleDiversity.elementAt(i))).floatValue()) / initDiv);
}
} else {
float lowerLim = numberOfGenes * myFactor;
float initDiv = ((Float) (cycleDiversity.elementAt(0))).floatValue() + Math.abs(lowerLim);
for (int i = 0; i < numDiv; i++) {
cycleDivFM.set(
i,
0,
(((Float) (cycleDiversity.elementAt(i))).floatValue() + Math.abs(lowerLim)) / initDiv);
}
}
// put all important information into AlgorithmData
inData.addParam("cycles", String.valueOf(numberOfClusters));
inData.addCluster("cluster", clusters);
inData.addMatrix("centroid-matrix", centroidFM);
inData.addMatrix("cluster-variances", varianceFM);
inData.addMatrix("cluster-diversity", clusterDiversity);
inData.addMatrix("cycle-diversity", cycleDivFM);
inData.addIntArray("cluster-population", clusterSize);
// Additions to AlgorithmData to allow drawing arrays
float[] nodeHeight = new float[numberOfClusters * 2];
int[] nodePopulation = new int[numberOfClusters * 2];
int[] leftChild = new int[nodeHeight.length * 2];
int[] rightChild = new int[nodeHeight.length * 2];
initializeReturnValues(nodeHeight, nodePopulation, leftChild, rightChild);
utilCounter = 0;
loadReturnValues(root, 0, nodeHeight, nodePopulation, leftChild, rightChild);
inData.addMatrix("node-heights", new FloatMatrix(nodeHeight, nodeHeight.length));
inData.addIntArray("left-child", leftChild);
inData.addIntArray("right-child", rightChild);
inData.addIntArray("node-population", nodePopulation);
if (useClusterVariance) inData.addParam("computed-var-cutoff", String.valueOf(endCriteria));
return;
}
