git-svn-id: https://svn.d4science.research-infrastructures.eu/gcube/trunk/data-analysis/EcologicalEngine@57480 82a268e6-3cf1-43bd-a215-b396298e98cf
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@ -36,16 +36,16 @@ import com.rapidminer.tools.OperatorService;
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public class DBScan implements Clusterer{
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AlgorithmConfiguration config;
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String epsilon;
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String minPoints;
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ExampleSet points;
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ArrayList<ArrayList<String>> rows;
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String OccurrencePointsTable;
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String OccurrencePointsClusterTable;
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String FeaturesColumnNames;
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float status;
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private SessionFactory dbHibConnection;
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protected AlgorithmConfiguration config;
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protected String epsilon;
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protected String minPoints;
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protected ExampleSet points;
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protected ArrayList<ArrayList<String>> rows;
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protected String OccurrencePointsTable;
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protected String OccurrencePointsClusterTable;
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protected String FeaturesColumnNames;
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protected float status;
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protected SessionFactory dbHibConnection;
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public static String clusterColumn = "clusterid";
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public static String clusterColumnType = "character varying";
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@ -165,20 +165,20 @@ public class DBScan implements Clusterer{
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if (config!=null)
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config.initRapidMiner();
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AnalysisLogger.getLogger().debug("DBScan: Initialized Rapid Miner ");
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AnalysisLogger.getLogger().debug("DBScan: Initializing Database Connection");
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AnalysisLogger.getLogger().debug("Initialized Rapid Miner ");
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AnalysisLogger.getLogger().debug("Initializing Database Connection");
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dbHibConnection=DatabaseUtils.initDBSession(config);
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//create the final table
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try{
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AnalysisLogger.getLogger().debug("DBScan: dropping table "+OccurrencePointsClusterTable);
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AnalysisLogger.getLogger().debug("dropping table "+OccurrencePointsClusterTable);
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String dropStatement = DatabaseUtils.dropTableStatement(OccurrencePointsClusterTable);
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AnalysisLogger.getLogger().debug("DBScan: dropping table "+dropStatement);
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AnalysisLogger.getLogger().debug("dropping table "+dropStatement);
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DatabaseFactory.executeSQLUpdate(dropStatement, dbHibConnection);
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}catch(Exception e){
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AnalysisLogger.getLogger().debug("DBScan: Could not drop table "+OccurrencePointsClusterTable);
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AnalysisLogger.getLogger().debug("Could not drop table "+OccurrencePointsClusterTable);
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}
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//create Table
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AnalysisLogger.getLogger().debug("DBScan: Creating table "+OccurrencePointsClusterTable);
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AnalysisLogger.getLogger().debug("Creating table "+OccurrencePointsClusterTable);
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String [] features = FeaturesColumnNames.split(AlgorithmConfiguration.getListSeparator());
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String columns = "";
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@ -190,13 +190,13 @@ public class DBScan implements Clusterer{
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String createStatement = "create table "+OccurrencePointsClusterTable+" ( "+columns+")";
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// String createStatement = new DatabaseUtils(dbHibConnection).buildCreateStatement(OccurrencePointsTable,OccurrencePointsClusterTable);
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AnalysisLogger.getLogger().debug("DBScan: "+createStatement);
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AnalysisLogger.getLogger().debug("Statement: "+createStatement);
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DatabaseFactory.executeSQLUpdate(createStatement, dbHibConnection);
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//add two columns one for cluster and another for outliers
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AnalysisLogger.getLogger().debug("DBScan: Adding Columns");
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AnalysisLogger.getLogger().debug("Adding Columns");
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DatabaseFactory.executeSQLUpdate(DatabaseUtils.addColumnStatement(OccurrencePointsClusterTable, clusterColumn, clusterColumnType), dbHibConnection);
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DatabaseFactory.executeSQLUpdate(DatabaseUtils.addColumnStatement(OccurrencePointsClusterTable, outliersColumn, outliersColumnType), dbHibConnection);
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AnalysisLogger.getLogger().debug("DBScan: Getting Samples");
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AnalysisLogger.getLogger().debug("Getting Samples");
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//build samples
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getSamples();
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status = 10f;
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@ -218,7 +218,9 @@ public class DBScan implements Clusterer{
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private void getSamples() throws Exception{
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protected void getSamples() throws Exception{
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System.out.println("->"+DatabaseUtils.getColumnsElementsStatement(OccurrencePointsTable, FeaturesColumnNames, ""));
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FeaturesColumnNames=FeaturesColumnNames.replace(AlgorithmConfiguration.listSeparator, ",");
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List<Object> samples = DatabaseFactory.executeSQLQuery(DatabaseUtils.getColumnsElementsStatement(OccurrencePointsTable, FeaturesColumnNames, ""), dbHibConnection);
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String [] elements = FeaturesColumnNames.split(",");
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int dimensions = elements.length;
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@ -281,10 +283,21 @@ public class DBScan implements Clusterer{
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IOObject[] outputvector = output.getIOObjects();
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BuildClusterTable(outputvector);
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shutdown();
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status = 100f;
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}
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protected void BuildClusterTable(IOObject[] outputvector) throws Exception{
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ClusterModel innermodel = (ClusterModel) outputvector[0];
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ExampleSet es = (ExampleSet) outputvector[1];
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String columnsNames =FeaturesColumnNames+","+clusterColumn+","+outliersColumn;
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int minpoints = Integer.parseInt(minPoints);
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AnalysisLogger.getLogger().debug("Analyzing Cluster ->"+" minpoints"+minpoints);
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int nClusters = innermodel.getClusters().size();
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float statusstep = ((100f-status)/ (float)(nClusters+1));
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@ -296,7 +309,7 @@ public class DBScan implements Clusterer{
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boolean outlier = false;
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//take cluster element indexes
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int npoints = c.getExampleIds().size();
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AnalysisLogger.getLogger().debug("DBScan: Analyzing Cluster ->"+id+" with "+npoints+" "+minpoints);
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AnalysisLogger.getLogger().debug("DBScan: Analyzing Cluster ->"+id+" with "+npoints);
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if (npoints==minpoints)
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outlier=true;
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@ -341,12 +354,8 @@ public class DBScan implements Clusterer{
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float instatus = status + statusstep;
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status = Math.min(95f, instatus);
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AnalysisLogger.getLogger().debug("DBScan: Status: "+status);
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}
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shutdown();
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status = 100f;
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}
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}
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@ -384,8 +393,8 @@ public class DBScan implements Clusterer{
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PrimitiveTypesList p2 = new PrimitiveTypesList(PrimitiveTypes.STRING, "FeaturesColumnNames","Column Names for the features",false);
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ServiceType p3 = new ServiceType(ServiceParameters.RANDOMSTRING, "OccurrencePointsClusterTable","Table name of the distribution","occCluster_");
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PrimitiveType p4 = new PrimitiveType(String.class.getName(), null, PrimitiveTypes.STRING, "epsilon","DBScan epsilon parameter","10");
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PrimitiveType p5 = new PrimitiveType(String.class.getName(), null, PrimitiveTypes.STRING, "minPoints","DBScan minimum points parameter (identifies outliers)","1");
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PrimitiveType p4 = new PrimitiveType(Integer.class.getName(), null, PrimitiveTypes.NUMBER, "epsilon","DBScan epsilon parameter","10");
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PrimitiveType p5 = new PrimitiveType(Integer.class.getName(), null, PrimitiveTypes.NUMBER, "minPoints","DBScan minimum points parameter (identifies outliers)","1");
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DatabaseType p6 = new DatabaseType(DatabaseParameters.DATABASEUSERNAME, "DatabaseUserName", "db user name");
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@ -0,0 +1,173 @@
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package org.gcube.dataanalysis.ecoengine.clustering;
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import java.util.ArrayList;
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import java.util.List;
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import org.gcube.contentmanagement.lexicalmatcher.utils.AnalysisLogger;
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import org.gcube.dataanalysis.ecoengine.configuration.AlgorithmConfiguration;
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import org.gcube.dataanalysis.ecoengine.datatypes.DatabaseType;
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import org.gcube.dataanalysis.ecoengine.datatypes.InputTable;
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import org.gcube.dataanalysis.ecoengine.datatypes.PrimitiveType;
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import org.gcube.dataanalysis.ecoengine.datatypes.PrimitiveTypesList;
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import org.gcube.dataanalysis.ecoengine.datatypes.ServiceType;
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import org.gcube.dataanalysis.ecoengine.datatypes.StatisticalType;
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import org.gcube.dataanalysis.ecoengine.datatypes.enumtypes.DatabaseParameters;
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import org.gcube.dataanalysis.ecoengine.datatypes.enumtypes.PrimitiveTypes;
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import org.gcube.dataanalysis.ecoengine.datatypes.enumtypes.ServiceParameters;
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import org.gcube.dataanalysis.ecoengine.datatypes.enumtypes.TableTemplates;
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import org.gcube.dataanalysis.ecoengine.utils.ResourceFactory;
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import com.rapidminer.operator.IOContainer;
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import com.rapidminer.operator.IOObject;
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import com.rapidminer.tools.OperatorService;
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public class KMeans extends DBScan{
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public static String clusterColumn = "clusterid";
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public static String clusterColumnType = "character varying";
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public static String outliersColumn = "outlier";
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public static String outliersColumnType = "boolean";
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private String kk;
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private String maxRuns;
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private String maxOptimizations;
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public static void main(String[] args) throws Exception{
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long t0 = System.currentTimeMillis();
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AlgorithmConfiguration config = new AlgorithmConfiguration();
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config.setConfigPath("./cfg/");
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config.setPersistencePath("./");
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config.setParam("OccurrencePointsTable","presence_basking_cluster");
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config.setParam("FeaturesColumnNames","centerlat"+AlgorithmConfiguration.getListSeparator()+"centerlong");
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config.setParam("OccurrencePointsClusterTable","occCluster_kmeans");
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config.setParam("k","50");
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config.setParam("max_runs","10");
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config.setParam("max_optimization_steps","10");
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config.setParam("min_points","2");
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config.setParam("DatabaseUserName","gcube");
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config.setParam("DatabasePassword","d4science2");
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config.setParam("DatabaseURL","jdbc:postgresql://146.48.87.169/testdb");
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config.setParam("DatabaseDriver","org.postgresql.Driver");
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KMeans cluster = new KMeans();
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cluster.setConfiguration(config);
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cluster.init();
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cluster.compute();
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System.out.println("ELAPSED "+(System.currentTimeMillis()-t0));
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}
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@Override
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public void setConfiguration(AlgorithmConfiguration config) {
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if (config!=null){
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kk=config.getParam("k");
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maxRuns= config.getParam("max_runs");
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maxOptimizations = config.getParam("max_optimization_steps");
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OccurrencePointsTable = config.getParam("OccurrencePointsTable").toLowerCase();
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OccurrencePointsClusterTable=config.getParam("OccurrencePointsClusterTable").toLowerCase();
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FeaturesColumnNames=config.getParam("FeaturesColumnNames");
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minPoints=config.getParam("min_points");
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this.config=config;
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}
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}
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@Override
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public void compute() throws Exception {
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if ((config==null)||kk==null||maxRuns==null||maxOptimizations==null){
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throw new Exception("KMeans: Error incomplete parameters");
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}
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AnalysisLogger.getLogger().debug("KMeans: Settin up the cluster");
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//take elements and produce example set
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com.rapidminer.operator.clustering.clusterer.KMeans kmeans = (com.rapidminer.operator.clustering.clusterer.KMeans) OperatorService.createOperator("KMeans");
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kmeans.setParameter("k", kk);
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kmeans.setParameter("max_runs",maxRuns);
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kmeans.setParameter("max_optimization_steps", maxOptimizations);
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kmeans.setParameter("keep_example_set", "true");
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kmeans.setParameter("add_cluster_attribute", "true");
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IOContainer innerInput = new IOContainer(points);
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AnalysisLogger.getLogger().debug("KMeans: Clustering...");
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IOContainer output = kmeans.apply(innerInput);
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AnalysisLogger.getLogger().debug("KMeans: ...Clustering Finished");
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status = 70f;
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IOObject[] outputvector = output.getIOObjects();
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BuildClusterTable(outputvector);
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shutdown();
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status = 100f;
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}
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@Override
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public List<StatisticalType> getInputParameters() {
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List<StatisticalType> parameters = new ArrayList<StatisticalType>();
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List<TableTemplates> templateOccs = new ArrayList<TableTemplates>();
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templateOccs.add(TableTemplates.GENERIC);
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InputTable p1 = new InputTable(templateOccs,"OccurrencePointsTable","Occurrence Points Table","occurrences");
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PrimitiveTypesList p2 = new PrimitiveTypesList(PrimitiveTypes.STRING, "FeaturesColumnNames","Column Names for the features",false);
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ServiceType p3 = new ServiceType(ServiceParameters.RANDOMSTRING, "OccurrencePointsClusterTable","Table name of the distribution","occCluster_");
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DatabaseType p6 = new DatabaseType(DatabaseParameters.DATABASEUSERNAME, "DatabaseUserName", "db user name");
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DatabaseType p7 = new DatabaseType(DatabaseParameters.DATABASEPASSWORD, "DatabasePassword", "db password");
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DatabaseType p8 = new DatabaseType(DatabaseParameters.DATABASEDRIVER, "DatabaseDriver", "db driver");
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DatabaseType p9 = new DatabaseType(DatabaseParameters.DATABASEURL, "DatabaseURL", "db url");
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DatabaseType p10 = new DatabaseType(DatabaseParameters.DATABASEDIALECT, "DatabaseDialect", "db dialect");
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DatabaseType p11 = new DatabaseType(DatabaseParameters.DATABASETABLESPACE, "DatabaseTableSpace", "db dialect");
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PrimitiveType p4 = new PrimitiveType(Integer.class.getName(), null, PrimitiveTypes.NUMBER, "k","Expected Number of Clusters","3");
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PrimitiveType p5 = new PrimitiveType(Integer.class.getName(), null, PrimitiveTypes.NUMBER, "max_runs","Max runs of the clustering procedure","10");
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PrimitiveType p12 = new PrimitiveType(Integer.class.getName(), null, PrimitiveTypes.NUMBER, "max_optimization_steps","Max number of internal optimization steps","5");
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PrimitiveType p13 = new PrimitiveType(Integer.class.getName(), null, PrimitiveTypes.NUMBER, "min_points","Minimum number of points to define an outlier set","2");
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parameters.add(p1);
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parameters.add(p2);
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parameters.add(p3);
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parameters.add(p4);
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parameters.add(p5);
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parameters.add(p6);
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parameters.add(p7);
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parameters.add(p8);
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parameters.add(p9);
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parameters.add(p10);
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parameters.add(p11);
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parameters.add(p12);
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return parameters;
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}
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@Override
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public String getDescription() {
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return "Clustering with KMeans";
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}
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ResourceFactory resourceManager;
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public String getResourceLoad() {
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if (resourceManager==null)
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resourceManager = new ResourceFactory();
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return resourceManager.getResourceLoad(1);
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}
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@Override
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public String getResources() {
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return ResourceFactory.getResources(100f);
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}
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}
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