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/* |
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Copyright @ 1999-2003, The Institute for Genomic Research (TIGR). |
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All rights reserved. |
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*/ |
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/* |
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* $RCSfile: FOM.java,v $ |
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* $Revision: 1.5 $ |
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* $Date: 2005/03/10 15:45:27 $ |
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* $Author: braistedj $ |
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* $State: Exp $ |
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*/ |
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package org.tigr.microarray.mev.cluster.algorithm.impl; |
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|
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import org.tigr.microarray.mev.cluster.Cluster; |
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import org.tigr.microarray.mev.cluster.NodeList; |
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import org.tigr.microarray.mev.cluster.algorithm.AbortException; |
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import org.tigr.microarray.mev.cluster.algorithm.AbstractAlgorithm; |
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import org.tigr.microarray.mev.cluster.algorithm.Algorithm; |
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import org.tigr.microarray.mev.cluster.algorithm.AlgorithmData; |
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import org.tigr.microarray.mev.cluster.algorithm.AlgorithmEvent; |
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import org.tigr.microarray.mev.cluster.algorithm.AlgorithmException; |
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import org.tigr.microarray.mev.cluster.algorithm.AlgorithmParameters; |
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import org.tigr.util.FloatMatrix; |
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|
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public class FOM extends AbstractAlgorithm { |
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|
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private boolean stop = false; |
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private boolean clusterGenes; |
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/** |
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* This method execute calculation and return result, |
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* stored in <code>AlgorithmData</code> class. |
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* |
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* @param data the data to be calculated. |
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*/ |
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public AlgorithmData execute(AlgorithmData data) throws AlgorithmException { |
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|
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AlgorithmParameters map = data.getParams(); |
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|
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int function = map.getInt("distance-function", EUCLIDEAN); |
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float factor = map.getFloat("distance-factor", 1.0f); |
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boolean absolute = map.getBoolean("distance-absolute", false); |
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|
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clusterGenes = map.getBoolean("cluster-genes", true); |
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int fomIterations = map.getInt("fom-iterations", 1); |
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int method = map.getInt("method", 2); |
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float interval = map.getFloat("interval", 0.1f); |
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int iterations = map.getInt("iterations", 50); |
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int maxNumClusters = map.getInt("number-of-clusters", 20); |
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boolean average = map.getBoolean("average", true); |
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boolean calculateMeans = map.getBoolean("calculate-means", true); |
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|
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FloatMatrix expMatrix = data.getMatrix("experiment"); |
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|
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int number_of_genes = expMatrix.getRowDimension(); |
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int number_of_samples = expMatrix.getColumnDimension(); |
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|
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Algorithm sub_algo = null; |
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if (method == 2) sub_algo = new KMC(); |
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else sub_algo = new CastClust(true); |
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|
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AlgorithmData sub_algo_data = new AlgorithmData(); |
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sub_algo_data.addMatrix("experiment", expMatrix); |
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sub_algo_data.addParam("distance-factor", String.valueOf(factor)); |
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sub_algo_data.addParam("distance-absolute", String.valueOf(absolute)); |
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sub_algo_data.addParam("distance-function", String.valueOf(function)); |
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sub_algo_data.addParam("number-of-iterations", String.valueOf(iterations)); |
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sub_algo_data.addParam("calculate-means", String.valueOf(calculateMeans)); |
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|
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/* |
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AlgorithmEvent event = new AlgorithmEvent(this, AlgorithmEvent.SET_UNITS, iterations); |
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fireValueChanged(event); |
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event.setId(AlgorithmEvent.PROGRESS_VALUE); |
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*/ |
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AlgorithmData sub_algo_result = null; |
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Cluster sub_algo_clusters = new Cluster(); |
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float[] fom_values = null; |
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FloatMatrix fomResults = null; |
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int times = 0; |
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int[] numOfCastClusters = new int[1]; // just to initialize |
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|
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if (method == 2) { //FOM FOR K-MEANS |
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AlgorithmEvent event = new AlgorithmEvent(this, AlgorithmEvent.SET_UNITS, maxNumClusters); |
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fireValueChanged(event); |
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event.setId(AlgorithmEvent.PROGRESS_VALUE); |
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sub_algo_data.addParam("kmc-cluster-genes", String.valueOf(clusterGenes)); |
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fom_values = new float[maxNumClusters]; |
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|
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fomResults = new FloatMatrix(fomIterations, maxNumClusters); |
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for (int fomi = 0; fomi < fomIterations; fomi++) { |
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event.setDescription("Calculating FOMs for FOM iteration "+String.valueOf(fomi+1)+" of "+String.valueOf(fomIterations)+" iterations."); |
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fireValueChanged(event); |
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for (int i=0; i<maxNumClusters; i++) { |
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if (stop) { |
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throw new AbortException(); |
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} |
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|
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event.setIntValue(i); |
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fireValueChanged(event); |
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|
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sub_algo_data.addParam("number-of-clusters", String.valueOf(i+1)); |
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sub_algo_result = sub_algo.execute(sub_algo_data); |
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|
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sub_algo_clusters = sub_algo_result.getCluster("cluster"); |
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fomResults.set(fomi, i, (float)getFOM(expMatrix, sub_algo_clusters, number_of_genes, number_of_samples)); |
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} |
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} |
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} else { //FOM FOR CAST |
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times = (int)(1/interval); |
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AlgorithmEvent event = new AlgorithmEvent(this, AlgorithmEvent.SET_UNITS, times); |
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fireValueChanged(event); |
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event.setId(AlgorithmEvent.PROGRESS_VALUE); |
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//sub_algo.runInside = true; |
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//System.out.println("times = " + times); |
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sub_algo_data.addParam("cast-cluster-genes", String.valueOf(clusterGenes)); |
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numOfCastClusters = new int[times]; |
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float threshold = 0; |
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fom_values = new float[times]; |
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for (int i = 0; i < times; i++) { |
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threshold = threshold + interval; |
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if (threshold > 1.0f) threshold = 1.0f; |
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|
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if (stop) { |
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throw new AbortException(); |
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} |
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event.setIntValue(i); |
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event.setDescription("calculating for threshold of "+String.valueOf(threshold)); |
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fireValueChanged(event); |
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|
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sub_algo_data.addParam("threshold", String.valueOf(threshold)); |
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sub_algo_result = sub_algo.execute(sub_algo_data); |
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sub_algo_clusters = sub_algo_result.getCluster("cluster"); |
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|
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fom_values[i] = (float)getFOM(expMatrix, sub_algo_clusters, number_of_genes, number_of_samples); |
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numOfCastClusters[i] = sub_algo_clusters.getNodeList().getSize(); |
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} |
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} |
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|
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AlgorithmData result = new AlgorithmData(); |
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if(method == 2) |
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result.addMatrix("fom-matrix", fomResults); |
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else |
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result.addMatrix("fom-values", new FloatMatrix(fom_values, 1)); |
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result.addIntArray("numOfCastClusters", numOfCastClusters); |
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return result; |
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} |
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|
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public double getFOM(FloatMatrix expMatrix, Cluster clusters, final int number_of_genes, final int number_of_samples) { |
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|
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int[] cluster; |
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float value; |
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int n = 0; |
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NodeList nodeList = clusters.getNodeList(); |
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final int number_of_clusters = nodeList.getSize(); |
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double[][] means = new double[number_of_samples][number_of_clusters]; |
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for (int i=0; i<number_of_samples; i++) { |
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for (int j=0; j<number_of_clusters; j++) { |
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means[i][j] = 0.0; |
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cluster = nodeList.getNode(j).getFeaturesIndexes(); |
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n = 0; |
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for (int p=0; p<cluster.length; p++) { |
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value = expMatrix.get(cluster[p], i); |
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if (!Float.isNaN(value)) { |
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means[i][j] += (double)value; |
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n++; |
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} |
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} |
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if(n > 0) |
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means[i][j] /= (double)n; |
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else |
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means[i][j] = 0; |
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} |
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} |
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|
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double[] tFOM = new double[number_of_samples]; |
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double factor; |
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|
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/* |
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if(number_of_clusters == 2) { |
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System.out.println("***********************"); |
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float [][] m = new float[means.length][means[0].length]; |
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for(int i = 0; i < means.length; i++) { |
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for(int j = 0; j < means[i].length; j++){ |
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m[i][j] = (float)means[i][j]; |
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} |
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} |
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FloatMatrix meanFM = new FloatMatrix(m); |
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meanFM.print(2,5); |
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} |
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*/ |
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//if(this.clusterGenes) |
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// factor = Math.sqrt((double)(number_of_genes-number_of_samples)/(double)number_of_genes); |
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// else |
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// factor = Math.sqrt((double)(number_of_samples-number_of_genes)/(double)number_of_samples); |
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// factor = Math.sqrt(((double)(number_of_genes-number_of_clusters))/(double)number_of_genes); |
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factor = 1; |
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//float tot = 0; |
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for (int i=0; i<number_of_samples; i++) { |
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tFOM[i] = 0.0; |
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for (int j=0; j<number_of_clusters; j++) { |
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cluster = nodeList.getNode(j).getFeaturesIndexes(); |
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for (int p=0; p<cluster.length; p++) { |
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value = expMatrix.get(cluster[p], i); |
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if (!Float.isNaN(value)) { |
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tFOM[i] += Math.pow(((double)value-means[i][j]), 2); |
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|
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//check sos, tot += Math.pow(((double)value-means[i][j]), 2); |
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} |
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} |
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} |
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tFOM[i] = Math.sqrt(tFOM[i]/(double)number_of_genes); |
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} |
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|
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double fom_value = 0d; |
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for (int i=0; i<number_of_samples; i++) { |
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fom_value += tFOM[i]; |
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} |
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|
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/* |
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if(number_of_clusters == 2){ |
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System.out.println("tot ="+ tot); |
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System.out.println("fom value ="+fom_value); |
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} |
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*/ |
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return fom_value; |
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} |
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|
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/** |
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* This method should interrupt the calculation. |
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*/ |
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public void abort() { |
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this.stop = true; |
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} |
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} |