List of usage examples for weka.clusterers HierarchicalClusterer clusterInstance
@Override
public int clusterInstance(Instance instance) throws Exception
From source file:gr.iit.demokritos.cru.cps.ai.KeyphraseClustering.java
License:Open Source License
public ArrayList<String> getClusters() throws Exception { System.out.println("Clustering......"); // int[] clusters_size = new int[clusters]; HierarchicalClusterer cl = new HierarchicalClusterer(); // EM em=new EM(); // XMeans xm = new XMeans(); no nominal attributes // DBSCAN db= new DBSCAN(); not our distance function // CascadeSimpleKMeans c = new CascadeSimpleKMeans(); not our distance function cl.setNumClusters(this.clusters); if (language.equals("en")) { // cl.setDistanceFunction(wd); //xm.setDistanceF(wd); cl.setDistanceFunction(wd);//ww w .j a va 2s . com } else if (language.equals("de")) { cl.setDistanceFunction(wdde); //c.setDistanceFunction(wdde); //xm.setDistanceF(wdde); } else if (language.equals("el")) { cl.setDistanceFunction(wdel); //c.setDistanceFunction(wdel); // xm.setDistanceF(wdel); } cl.buildClusterer(data); //xm.buildClusterer(data); //c.setMaxIterations(5); ArrayList<String> clustersList = new ArrayList<String>(); for (int i = 0; i < cl.numberOfClusters(); i++) { clustersList.add(""); } //cl.buildClusterer(data); //em.buildClusterer(data); // xm.buildClusterer(data); for (int j = 0; j < data.numInstances(); j++) { //double[] prob = c.distributionForInstance(data.instance(j)); //double[] prob = cl.distributionForInstance(data.instance(j)); String clusterLine = data.instance(j).stringValue(0); int clust = cl.clusterInstance(data.instance(j)); clustersList.set(clust, clustersList.get(clust).concat(clusterLine + ";")); //take the probabilities prob[i] that it is in the coresponding cluster i /*for (int i = 0; i < prob.length; i++) { //keep the cluster that has prob>0.9, as this is the cluster that the word is in if (prob[i] > 0.9) { //keep for every cluster its terms clustersList.set(i, clustersList.get(i).concat(clusterLine + ";")); //keep the size of cluster i // clusters_size[i] = clusters_size[i] + 1; } }*/ } return clustersList; }
From source file:jmetal.problems.SurvivalAnalysis.java
License:Open Source License
/** * Evaluates a solution /*from w w w . j a v a 2s . co m*/ * @param solution The solution to evaluate */ public void evaluate(Solution solution) { Binary variable; int counterSelectedFeatures; DataSource source; double testStatistic = Double.MAX_VALUE; double pValue = Double.MAX_VALUE; double ArithmeticHarmonicCutScore = Double.MAX_VALUE; //double statScore; REXP x; variable = ((Binary) solution.getDecisionVariables()[0]); counterSelectedFeatures = 0; try { // read the data file source = new DataSource(this.dataFileName); Instances data = source.getDataSet(); //System.out.print("Data read successfully. "); //System.out.print("Number of attributes: " + data.numAttributes()); //System.out.println(". Number of instances: " + data.numInstances()); // save the attribute 'T' and 'Censor' attTime = data.attribute(data.numAttributes() - 2); attCensor = data.attribute(data.numAttributes() - 1); // First filter the attributes based on chromosome Instances tmpData = this.filterByChromosome(data, solution); // Now filter the attribute 'T' and 'Censor' Remove filter = new Remove(); // remove the two last attributes : 'T' and 'Censor' filter.setAttributeIndices("" + (tmpData.numAttributes() - 1) + "," + tmpData.numAttributes()); //System.out.println("After chromosome filtering no of attributes: " + tmpData.numAttributes()); filter.setInputFormat(tmpData); Instances dataClusterer = Filter.useFilter(tmpData, filter); // filtering complete /* // debug: write the filtered dataset ArffSaver saver = new ArffSaver(); saver.setInstances(dataClusterer); saver.setFile(new File("filteered-data.arff")); saver.writeBatch(); // end debug */ // train hierarchical clusterer HierarchicalClusterer clusterer = new HierarchicalClusterer(); clusterer.setOptions(new String[] { "-L", this.HC_LinkType }); // complete linkage clustering //Link type (Single, Complete, Average, Mean, Centroid, Ward, Adjusted complete, Neighbor Joining) //[SINGLE|COMPLETE|AVERAGE|MEAN|CENTROID|WARD|ADJCOMPLETE|NEIGHBOR_JOINING] //clusterer.setDebug(true); clusterer.setNumClusters(2); clusterer.setDistanceFunction(new EuclideanDistance()); clusterer.setDistanceIsBranchLength(false); // ?? Should it be changed to false? (Noman) clusterer.buildClusterer(dataClusterer); double[][] distanceMatrix = clusterer.getDistanceMatrix(); // save the cluster assignments if (this.re == null) { // we are not calling R functions. Therefore parallelization possible int[] clusterAssignment = new int[dataClusterer.numInstances()]; int classOneCnt = 0; int classTwoCnt = 0; for (int i = 0; i < dataClusterer.numInstances(); ++i) { clusterAssignment[i] = clusterer.clusterInstance(dataClusterer.get(i)); if (clusterAssignment[i] == 0) { ++classOneCnt; } else if (clusterAssignment[i] == 1) { ++classTwoCnt; } //System.out.println("Instance " + i + ": " + clusterAssignment[i]); } //System.out.println("Class 1 cnt: " + classOneCnt + " Class 2 cnt: " + classTwoCnt); // create arrays with time (event occurrence time) and censor data for use with jstat LogRankTest double[] time1 = new double[classOneCnt]; double[] censor1 = new double[classOneCnt]; double[] time2 = new double[classTwoCnt]; double[] censor2 = new double[classTwoCnt]; //data = source.getDataSet(); for (int i = 0, cnt1 = 0, cnt2 = 0; i < dataClusterer.numInstances(); ++i) { //clusterAssignment[i] = clusterer.clusterInstance(dataClusterer.get(i)); if (clusterAssignment[i] == 0) { time1[cnt1] = data.get(i).value(attTime); censor1[cnt1++] = data.get(i).value(attCensor); //System.out.println("i: " + i + " T: " + time1[cnt1-1]); } else if (clusterAssignment[i] == 1) { time2[cnt2] = data.get(i).value(attTime); //System.out.println("i: " + i + " T: " + time2[cnt2-1]); censor2[cnt2++] = data.get(i).value(attCensor); ; } //System.out.println("Instance " + i + ": " + clusterAssignment[i]); } //Instances[] classInstances = separateClassInstances(clusterAssignment, this.dataFileName,solution); //System.out.println("Class instances seperated"); // calculate log rank test and p values LogRankTest testclass1 = new LogRankTest(time1, time2, censor1, censor2); double[] scores = testclass1.logRank(); testStatistic = scores[0]; pValue = scores[2]; ArithmeticHarmonicCutScore = this.getArithmeticHarmonicCutScore(distanceMatrix, clusterAssignment); //debug: //System.out.println("Calculation by myLibrary: testStatistic: " + scores[0] + " pValue: " + scores[2]); //end debug //WilcoxonTest testclass1 = new WilcoxonTest(time1, censor1, time2, censor2); //testStatistic = testclass1.testStatistic; //pValue = testclass1.pValue;true } else { // We are calling R for Log Rank test, Parallelization not possible String strT = "time <- c("; String strC = "censor <- c("; String strG = "group <- c("; for (int i = 0; i < dataClusterer.numInstances() - 1; ++i) { strT = strT + (int) data.get(i).value(attTime) + ","; strG = strG + clusterer.clusterInstance(dataClusterer.get(i)) + ","; strC = strC + (int) data.get(i).value(attCensor) + ","; } int tmpi = dataClusterer.numInstances() - 1; strT = strT + (int) data.get(tmpi).value(attTime) + ")"; strG = strG + clusterer.clusterInstance(dataClusterer.get(tmpi)) + ")"; strC = strC + (int) data.get(tmpi).value(attCensor) + ")"; this.re.eval(strT); this.re.eval(strC); this.re.eval(strG); //debug //System.out.println(strT); //System.out.println(strC); //System.out.println(strG); //end debug /** If you are calling surv_test from coin library */ /*v re.eval("library(coin)"); re.eval("grp <- factor (group)"); re.eval("result <- surv_test(Surv(time,censor)~grp,distribution=\"exact\")"); x=re.eval("statistic(result)"); testStatistic = x.asDouble(); //x=re.eval("pvalue(result)"); //pValue = x.asDouble(); //System.out.println("StatScore: " + statScore + "pValue: " + pValue); */ /** If you are calling survdiff from survival library (much faster) */ re.eval("library(survival)"); re.eval("res2 <- survdiff(Surv(time,censor)~group,rho=0)"); x = re.eval("res2$chisq"); testStatistic = x.asDouble(); //System.out.println(x); x = re.eval("pchisq(res2$chisq, df=1, lower.tail = FALSE)"); //x = re.eval("1.0 - pchisq(res2$chisq, df=1)"); pValue = x.asDouble(); //debug: //System.out.println("Calculation by R: StatScore: " + testStatistic + "pValue: " + pValue); //end debug } } catch (Exception e) { // TODO Auto-generated catch block System.err.println("Can't open the data file."); e.printStackTrace(); System.exit(1); } /********** * Current Implementation considers two objectives * 1. pvalue to be minimized / statistical score to be maximized * 2. Number of Features to be maximized/minimized */ // Currently this section implements the OneZeroMax problem - need to modify it for (int i = 0; i < variable.getNumberOfBits(); i++) if (variable.bits_.get(i)) counterSelectedFeatures++; // OneZeroMax is a maximization problem: multiply by -1 to minimize /* if (Double.isNaN(testStatistic)){ solution.setObjective(0,Double.MAX_VALUE); } else{ solution.setObjective(0, testStatistic); } */ if (this.pValueFlag) { solution.setObjective(0, pValue); // pValue to be minimized } else { solution.setObjective(0, -1.0 * testStatistic); // statistic score to be maximized } if (this.featureMax) { solution.setObjective(1, -1.0 * counterSelectedFeatures); // feature maximized } else { solution.setObjective(1, counterSelectedFeatures); // feature minimized } if (this.numberOfObjectives_ == 3) { solution.setObjective(2, -1.0 * ArithmeticHarmonicCutScore); // feature maximized } }
From source file:jmetal.test.survivalanalysis.GenerateSurvivalGraph.java
License:Open Source License
/** * Evaluates a solution //from ww w. ja va 2 s . c o m * @param solution The solution to evaluate */ public void evaluate(Solution solution) { Binary variable; int counterSelectedFeatures; DataSource source; double testStatistic = Double.MAX_VALUE; double pValue = Double.MAX_VALUE; double ArithmeticHarmonicCutScore = Double.MAX_VALUE; //double statScore; REXP x; variable = ((Binary) solution.getDecisionVariables()[0]); counterSelectedFeatures = 0; try { // read the data file source = new DataSource(this.dataFileName); Instances data = source.getDataSet(); //System.out.print("Data read successfully. "); //System.out.print("Number of attributes: " + data.numAttributes()); //System.out.println(". Number of instances: " + data.numInstances()); // save the attribute 'T' and 'Censor' attTime = data.attribute(data.numAttributes() - 2); attCensor = data.attribute(data.numAttributes() - 1); // First filter the attributes based on chromosome Instances tmpData = this.filterByChromosome(data, solution); // Now filter the attribute 'T' and 'Censor' Remove filter = new Remove(); // remove the two last attributes : 'T' and 'Censor' filter.setAttributeIndices("" + (tmpData.numAttributes() - 1) + "," + tmpData.numAttributes()); //System.out.println("After chromosome filtering no of attributes: " + tmpData.numAttributes()); filter.setInputFormat(tmpData); Instances dataClusterer = Filter.useFilter(tmpData, filter); // filtering complete // List the selected features/attributes Enumeration<Attribute> attributeList = dataClusterer.enumerateAttributes(); System.out.println("Selected attributes/features: "); while (attributeList.hasMoreElements()) { Attribute att = attributeList.nextElement(); System.out.print(att.name() + ","); } System.out.println(); /* // debug: write the filtered dataset ArffSaver saver = new ArffSaver(); saver.setInstances(dataClusterer); saver.setFile(new File("filteered-data.arff")); saver.writeBatch(); // end debug */ // train hierarchical clusterer HierarchicalClusterer clusterer = new HierarchicalClusterer(); clusterer.setOptions(new String[] { "-L", this.HC_LinkType }); //Link type (Single, Complete, Average, Mean, Centroid, Ward, Adjusted complete, Neighbor Joining) //[SINGLE|COMPLETE|AVERAGE|MEAN|CENTROID|WARD|ADJCOMPLETE|NEIGHBOR_JOINING] //clusterer.setDebug(true); clusterer.setNumClusters(2); clusterer.setDistanceFunction(new EuclideanDistance()); clusterer.setDistanceIsBranchLength(false); // ?? Should it be changed to false? (Noman) clusterer.buildClusterer(dataClusterer); double[][] distanceMatrix = clusterer.getDistanceMatrix(); // Cluster evaluation: ClusterEvaluation eval = new ClusterEvaluation(); eval.setClusterer(clusterer); if (this.testDataFileName != null) { DataSource testSource = new DataSource(this.testDataFileName); Instances tmpTestData = testSource.getDataSet(); tmpTestData.setClassIndex(tmpTestData.numAttributes() - 1); //testSource. // First filter the attributes based on chromosome Instances testData = this.filterByChromosome(tmpTestData, solution); //String[] options = new String[2]; //options[0] = "-t"; //options[1] = "/some/where/somefile.arff"; //eval. //System.out.println(eval.evaluateClusterer(testData, options)); eval.evaluateClusterer(testData); System.out.println("\nCluster evluation for this solution(" + this.testDataFileName + "): " + eval.clusterResultsToString()); } // First analyze using my library function // save the cluster assignments int[] clusterAssignment = new int[dataClusterer.numInstances()]; int classOneCnt = 0; int classTwoCnt = 0; for (int i = 0; i < dataClusterer.numInstances(); ++i) { clusterAssignment[i] = clusterer.clusterInstance(dataClusterer.get(i)); if (clusterAssignment[i] == 0) { ++classOneCnt; } else if (clusterAssignment[i] == 1) { ++classTwoCnt; } //System.out.println("Instance " + i + ": " + clusterAssignment[i]); } System.out.println("Class 1 cnt: " + classOneCnt + " Class 2 cnt: " + classTwoCnt); // create arrays with time (event occurrence time) and censor data for use with jstat LogRankTest double[] time1 = new double[classOneCnt]; double[] censor1 = new double[classOneCnt]; double[] time2 = new double[classTwoCnt]; double[] censor2 = new double[classTwoCnt]; //data = source.getDataSet(); for (int i = 0, cnt1 = 0, cnt2 = 0; i < dataClusterer.numInstances(); ++i) { //clusterAssignment[i] = clusterer.clusterInstance(dataClusterer.get(i)); if (clusterAssignment[i] == 0) { time1[cnt1] = data.get(i).value(attTime); censor1[cnt1++] = data.get(i).value(attCensor); //System.out.println("i: " + i + " T: " + time1[cnt1-1]); } else if (clusterAssignment[i] == 1) { time2[cnt2] = data.get(i).value(attTime); //System.out.println("i: " + i + " T: " + time2[cnt2-1]); censor2[cnt2++] = data.get(i).value(attCensor); ; } //System.out.println("Instance " + i + ": " + clusterAssignment[i]); } //Instances[] classInstances = separateClassInstances(clusterAssignment, this.dataFileName,solution); //System.out.println("Class instances seperated"); // calculate log rank test and p values LogRankTest testclass1 = new LogRankTest(time1, time2, censor1, censor2); double[] scores = testclass1.logRank(); testStatistic = scores[0]; pValue = scores[2]; ArithmeticHarmonicCutScore = this.getArithmeticHarmonicCutScore(distanceMatrix, clusterAssignment); //debug: System.out.println("Calculation by myLibrary:\n testStatistic: " + scores[0] + " pValue: " + scores[2] + " Arithmetic Harmonic Cut Score: " + ArithmeticHarmonicCutScore); //end debug //WilcoxonTest testclass1 = new WilcoxonTest(time1, censor1, time2, censor2); //testStatistic = testclass1.testStatistic; //pValue = testclass1.pValue;true // Now analyze calling R for Log Rank test, Parallelization not possible String strT = "time <- c("; String strC = "censor <- c("; String strG = "group <- c("; for (int i = 0; i < dataClusterer.numInstances() - 1; ++i) { strT = strT + (int) data.get(i).value(attTime) + ","; strG = strG + clusterer.clusterInstance(dataClusterer.get(i)) + ","; strC = strC + (int) data.get(i).value(attCensor) + ","; } int tmpi = dataClusterer.numInstances() - 1; strT = strT + (int) data.get(tmpi).value(attTime) + ")"; strG = strG + clusterer.clusterInstance(dataClusterer.get(tmpi)) + ")"; strC = strC + (int) data.get(tmpi).value(attCensor) + ")"; this.re.eval(strT); this.re.eval(strC); this.re.eval(strG); //debug //System.out.println(strT); //System.out.println(strC); //System.out.println(strG); //end debug /** If you are calling surv_test from coin library */ /*v re.eval("library(coin)"); re.eval("grp <- factor (group)"); re.eval("result <- surv_test(Surv(time,censor)~grp,distribution=\"exact\")"); x=re.eval("statistic(result)"); testStatistic = x.asDouble(); //x=re.eval("pvalue(result)"); //pValue = x.asDouble(); //System.out.println("StatScore: " + statScore + "pValue: " + pValue); */ /** If you are calling survdiff from survival library (much faster) */ re.eval("library(survival)"); re.eval("res2 <- survdiff(Surv(time,censor)~group,rho=0)"); x = re.eval("res2$chisq"); testStatistic = x.asDouble(); //System.out.println(x); x = re.eval("pchisq(res2$chisq, df=1, lower.tail = FALSE)"); //x = re.eval("1.0 - pchisq(res2$chisq, df=1)"); pValue = x.asDouble(); //debug: //System.out.println("Calculation by R: StatScore: " + testStatistic + "pValue: " + pValue); //end debug System.out.println("Calculation by R:"); System.out.println("StatScore: " + testStatistic + " pValue: " + pValue); re.eval("timestrata1.surv <- survfit( Surv(time, censor)~ strata(group), conf.type=\"log-log\")"); re.eval("timestrata1.surv1 <- survfit( Surv(time, censor)~ 1, conf.type=\"none\")"); String evalStr = "jpeg('SurvivalPlot-" + this.SolutionID + ".jpg')"; re.eval(evalStr); re.eval("plot(timestrata1.surv, col=c(2,3), xlab=\"Time\", ylab=\"Survival Probability\")"); re.eval("par(new=T)"); re.eval("plot(timestrata1.surv1,col=1)"); re.eval("legend(0.2, c(\"Group1\",\"Group2\",\"Whole\"))"); re.eval("dev.off()"); System.out.println("\nCluster Assignments:"); for (int i = 0; i < dataClusterer.numInstances(); ++i) { System.out.println("Instance " + i + ": " + clusterAssignment[i]); } } catch (Exception e) { // TODO Auto-generated catch block System.err.println("Can't open the data file."); e.printStackTrace(); System.exit(1); } }
From source file:jmetal.test.survivalanalysis.GenerateSurvivalGraphOld.java
License:Open Source License
/** * Evaluates a solution - actually generate the survival graph * @param solution The solution to evaluate *//*from ww w . j a v a 2 s.com*/ public void evaluate(Solution solution) { Binary variable; int counterSelectedFeatures; DataSource source; double testStatistic = Double.MAX_VALUE; double pValue = Double.MAX_VALUE; //double statScore; REXP x; variable = ((Binary) solution.getDecisionVariables()[0]); counterSelectedFeatures = 0; System.out.println("\nSolution ID " + this.SolutionID); try { // read the data file source = new DataSource(this.dataFileName); Instances data = source.getDataSet(); //System.out.print("Data read successfully. "); //System.out.print("Number of attributes: " + data.numAttributes()); //System.out.println(". Number of instances: " + data.numInstances()); // save the attribute 'T' and 'Censor' attTime = data.attribute(data.numAttributes() - 2); attCensor = data.attribute(data.numAttributes() - 1); // First filter the attributes based on chromosome Instances tmpData = this.filterByChromosome(data, solution); // Now filter the attribute 'T' and 'Censor' Remove filter = new Remove(); // remove the two last attributes : 'T' and 'Censor' filter.setAttributeIndices("" + (tmpData.numAttributes() - 1) + "," + tmpData.numAttributes()); //System.out.println("After chromosome filtering no of attributes: " + tmpData.numAttributes()); filter.setInputFormat(tmpData); Instances dataClusterer = Filter.useFilter(tmpData, filter); Enumeration<Attribute> attributeList = dataClusterer.enumerateAttributes(); System.out.println("Selected attributes: "); while (attributeList.hasMoreElements()) { Attribute att = attributeList.nextElement(); System.out.print(att.name() + ","); } System.out.println(); // filtering complete // Debug: write the filtered dataset /* ArffSaver saver = new ArffSaver(); saver.setInstances(dataClusterer); saver.setFile(new File("filteered-data.arff")); saver.writeBatch(); */ // train hierarchical clusterer HierarchicalClusterer clusterer = new HierarchicalClusterer(); clusterer.setOptions(new String[] { "-L", "COMPLETE" }); // complete linkage clustering //clusterer.setDebug(true); clusterer.setNumClusters(2); clusterer.setDistanceFunction(new EuclideanDistance()); //clusterer.setDistanceFunction(new ChebyshevDistance()); clusterer.setDistanceIsBranchLength(false); clusterer.buildClusterer(dataClusterer); // Cluster evaluation: ClusterEvaluation eval = new ClusterEvaluation(); eval.setClusterer(clusterer); if (this.testDataFileName != null) { DataSource testSource = new DataSource(this.testDataFileName); Instances tmpTestData = testSource.getDataSet(); tmpTestData.setClassIndex(tmpTestData.numAttributes() - 1); //testSource. // First filter the attributes based on chromosome Instances testData = this.filterByChromosome(tmpTestData, solution); //String[] options = new String[2]; //options[0] = "-t"; //options[1] = "/some/where/somefile.arff"; //eval. //System.out.println(eval.evaluateClusterer(testData, options)); eval.evaluateClusterer(testData); System.out.println("\nCluster evluation for this solution: " + eval.clusterResultsToString()); } // Print the cluster assignments: // save the cluster assignments //if (printClusterAssignment==true){ int[] clusterAssignment = new int[dataClusterer.numInstances()]; int classOneCnt = 0; int classTwoCnt = 0; for (int i = 0; i < dataClusterer.numInstances(); ++i) { clusterAssignment[i] = clusterer.clusterInstance(dataClusterer.get(i)); if (clusterAssignment[i] == 0) { ++classOneCnt; } else if (clusterAssignment[i] == 1) { ++classTwoCnt; } //System.out.println("Instance " + i + ": " + clusterAssignment[i]); } System.out.println("Class 1 cnt: " + classOneCnt + " Class 2 cnt: " + classTwoCnt); //} /* // create arrays with time (event occurrence time) and censor data for use with jstat LogRankTest double[] time1 = new double[classOneCnt]; double[] censor1 = new double[classOneCnt]; double[] time2 = new double[classTwoCnt]; double[] censor2 = new double[classTwoCnt]; //data = source.getDataSet(); for (int i=0, cnt1=0, cnt2=0; i<dataClusterer.numInstances(); ++i){ clusterAssignment[i] = clusterer.clusterInstance(dataClusterer.get(i)); if (clusterAssignment[i]==0){ time1[cnt1] = data.get(i).value(attTime); censor1[cnt1++] = 1; //System.out.println("i: " + i + " T: " + time1[cnt1-1]); } else if (clusterAssignment[i]==1){ time2[cnt2] = data.get(i).value(attTime); //System.out.println("i: " + i + " T: " + time2[cnt2-1]); censor2[cnt2++] = 1; } //System.out.println("Instance " + i + ": " + clusterAssignment[i]); } //Instances[] classInstances = separateClassInstances(clusterAssignment, this.dataFileName,solution); //System.out.println("Class instances seperated"); // calculate log rank test and p values //LogRankTest testclass1 = new LogRankTest(time1, censor1, time2, censor2); //testStatistic = testclass1.testStatistic; //pValue = testclass1.pValue; WilcoxonTest testclass1 = new WilcoxonTest(time1, censor1, time2, censor2); testStatistic = testclass1.testStatistic; pValue = testclass1.pValue;true */ String strT = "time1 <- c("; String strC = "censor1 <- c("; String strG = "group1 <- c("; for (int i = 0; i < dataClusterer.numInstances() - 1; ++i) { strT = strT + (int) data.get(i).value(attTime) + ","; strG = strG + clusterer.clusterInstance(dataClusterer.get(i)) + ","; strC = strC + (int) data.get(i).value(attCensor) + ","; } int tmpi = dataClusterer.numInstances() - 1; strT = strT + (int) data.get(tmpi).value(attTime) + ")"; strG = strG + clusterer.clusterInstance(dataClusterer.get(tmpi)) + ")"; strC = strC + (int) data.get(tmpi).value(attCensor) + ")"; this.re.eval(strT); this.re.eval(strC); this.re.eval(strG); // for MyLogRankTest double[] time1 = new double[classOneCnt]; double[] time2 = new double[classTwoCnt]; double[] censor1 = new double[classOneCnt]; double[] censor2 = new double[classTwoCnt]; int i1 = 0, i2 = 0; for (int i = 0; i < dataClusterer.numInstances(); ++i) { strT = strT + (int) data.get(i).value(attTime) + ","; strG = strG + clusterer.clusterInstance(dataClusterer.get(i)) + ","; strC = strC + (int) data.get(i).value(attCensor) + ","; if (clusterer.clusterInstance(dataClusterer.get(i)) == 0) { time1[i1] = data.get(i).value(attTime); censor1[i1] = data.get(i).value(attCensor); ++i1; } else { time2[i2] = data.get(i).value(attTime); censor2[i2] = data.get(i).value(attCensor); ++i2; } } /** If you are calling surv_test from coin library */ /*v re.eval("library(coin)"); re.eval("grp <- factor (group)"); re.eval("result <- surv_test(Surv(time,censor)~grp,distribution=\"exact\")"); x=re.eval("statistic(result)"); testStatistic = x.asDouble(); //x=re.eval("pvalue(result)"); //pValue = x.asDouble(); //System.out.println("StatScore: " + statScore + "pValue: " + pValue); */ /** If you are calling survdiff from survival library (much faster) */ re.eval("library(survival)"); re.eval("res21 <- survdiff(Surv(time1,censor1)~group1,rho=0)"); x = re.eval("res21$chisq"); testStatistic = x.asDouble(); //System.out.println(x); x = re.eval("pchisq(res21$chisq, df=1, lower.tail = FALSE)"); //x = re.eval("1.0 - pchisq(res2$chisq, df=1)"); pValue = x.asDouble(); System.out.println("Results from R:"); System.out.println("StatScore: " + testStatistic + " pValue: " + pValue); re.eval("timestrata1.surv <- survfit( Surv(time1, censor1)~ strata(group1), conf.type=\"log-log\")"); re.eval("timestrata1.surv1 <- survfit( Surv(time1, censor1)~ 1, conf.type=\"none\")"); String evalStr = "jpeg('SurvivalPlot-" + this.SolutionID + ".jpg')"; re.eval(evalStr); re.eval("plot(timestrata1.surv, col=c(2,3), xlab=\"Time\", ylab=\"Survival Probability\")"); re.eval("par(new=T)"); re.eval("plot(timestrata1.surv1,col=1)"); re.eval("legend(0.2, c(\"Group1\",\"Group2\",\"Whole\"))"); re.eval("dev.off()"); System.out.println("Results from my code: "); LogRankTest lrt = new LogRankTest(time1, time2, censor1, censor2); double[] results = lrt.logRank(); System.out.println("Statistics: " + results[0] + " variance: " + results[1] + " pValue: " + results[2]); } catch (Exception e) { // TODO Auto-generated catch block System.err.println("Can't open the data file."); e.printStackTrace(); System.exit(1); } /********** * Current Implementation considers two objectives * 1. pvalue to be minimized / statistical score to be maximized * 2. Number of Features to be maximized/minimized */ }
From source file:org.iobserve.analysis.behavior.clustering.hierarchical.ClusteringResultsBuilder.java
License:Apache License
/** * Build clustering result list of clustered instances and their assigned clusters. * * @param instances// w ww . ja v a 2 s. co m * Input data that is clustered. * * @param hierarchicalClusterer * Weka-clusterer that performs the hierarchical clustering. * * @return clusteringResults */ public static Map<Integer, List<Pair<Instance, Double>>> buildClusteringResults(final Instances instances, final HierarchicalClusterer hierarchicalClusterer) { /* * An entry of the map looks like this: <clusterID, <instance, 1.0>>. The 1,0 is the * probability that the instance is in this cluster. It is needed for unity of clustering * output data. */ final Map<Integer, List<Pair<Instance, Double>>> clusteringResults = new HashMap<>(); // NOPMD; for (int i = 0; i < instances.numInstances(); i++) { final Instance currentInstance = instances.instance(i); try { final int cluster = hierarchicalClusterer.clusterInstance(currentInstance); final double probability = hierarchicalClusterer.distributionForInstance(currentInstance)[cluster]; if (clusteringResults.get(cluster) == null) { clusteringResults.put(cluster, new LinkedList<Pair<Instance, Double>>()); } clusteringResults.get(cluster).add(new Pair<>(currentInstance, probability)); } catch (final Exception e) { // NOPMD NOCS api dependency ClusteringResultsBuilder.LOGGER.error("Failed building hierarchical clustering.", e); } } return clusteringResults; }