Author: jcouteau
Date: 2009-03-25 13:53:22 +0000 (Wed, 25 Mar 2009)
New Revision: 90
Added:
trunk/sensitivity/SensitivityCalculatorROptimumLHS.java
trunk/sensitivity/SensitivityCalculatorRRandomLHS.java
Log:
First implementation of Random and Optimum Latin Hypercube Sensitivity calculators
Added: trunk/sensitivity/SensitivityCalculatorROptimumLHS.java
===================================================================
--- trunk/sensitivity/SensitivityCalculatorROptimumLHS.java (rev 0)
+++ trunk/sensitivity/SensitivityCalculatorROptimumLHS.java 2009-03-25 13:53:22 UTC (rev 90)
@@ -0,0 +1,333 @@
+/* *##%
+ * Copyright (C) 2006 - 2009 Code Lutin
+ *
+ * This program is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU General Public License
+ * as published by the Free Software Foundation; either version 2
+ * of the License, or (at your option) any later version.
+ *
+ * This program is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with this program; if not, write to the Free Software
+ * Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
+ *##%*/
+
+package sensitivity;
+
+import java.io.File;
+import java.io.Serializable;
+import java.util.List;
+
+import org.apache.commons.logging.Log;
+import org.apache.commons.logging.LogFactory;
+import org.codelutin.j2r.REngine;
+import org.codelutin.j2r.RProxy;
+import org.codelutin.math.matrix.MatrixFactory;
+import org.codelutin.math.matrix.MatrixND;
+import org.codelutin.util.FileUtil;
+
+import fr.ifremer.isisfish.datastore.SimulationStorage;
+import fr.ifremer.isisfish.simulator.sensitivity.DesignPlan;
+import fr.ifremer.isisfish.simulator.sensitivity.Factor;
+import fr.ifremer.isisfish.simulator.sensitivity.Scenario;
+import fr.ifremer.isisfish.simulator.sensitivity.SensitivityCalculator;
+import fr.ifremer.isisfish.simulator.sensitivity.SensitivityException;
+import fr.ifremer.isisfish.simulator.sensitivity.SensitivityScenarios;
+import fr.ifremer.isisfish.simulator.sensitivity.domain.ContinuousDomain;
+import fr.ifremer.isisfish.simulator.sensitivity.domain.EquationContinuousDomain;
+import fr.ifremer.isisfish.simulator.sensitivity.domain.MatrixContinuousDomain;
+
+/**
+ * Implementation of Random Latin Hypercube method using R.
+ *
+ * @author jcouteau
+ * @version $Revision: 1.0 $
+ *
+ */
+
+public class SensitivityCalculatorROptimumLHS implements SensitivityCalculator {
+
+ /** to use log facility, just put in your code: log.info("..."); */
+ static private Log log = LogFactory
+ .getLog(SensitivityCalculatorROptimumLHS.class);
+
+ public int param_simulationNumber;
+
+ public int param_MaxSweeps;
+
+ public int param_eps;
+
+ /**
+ * Retourne vrai si le calculateur sait gerer la cardinalité des facteurs
+ * continue.
+ *
+ * @return <tt>true</tt> s'il sait la gerer
+ */
+ public boolean canManageCardinality() {
+ return true;
+ }
+
+ public SensitivityScenarios compute(DesignPlan plan, File outputdirectory)
+ throws SensitivityException {
+
+ int factornumber = plan.getFactors().size();
+ double[] dataframe = new double[0];
+ SensitivityScenarios thisExperiment = new SensitivityScenarios();
+ List<Scenario> thisExperimentScenarios = thisExperiment.getScenarios();
+
+ REngine engine = new RProxy();
+ try {
+
+ //Load the lhs library
+ engine.voidEval("library(lhs)");
+ log.info("Message sent to R : " + "library(lhs)");
+
+ //Create the scenarios
+ engine.voidEval("x<-optimumLHS(" + param_simulationNumber + ","
+ + factornumber + "," + param_MaxSweeps + "," + param_eps
+ + ")");
+ log.info("Message sent to R : " + "x<-optimumLHS("
+ + param_simulationNumber + "," + factornumber + ","
+ + param_MaxSweeps + "," + param_eps + ")");
+
+ // Get back experiment plan
+ dataframe = (double[]) engine.eval("x");
+ log.info("Message sent to R" + "x");
+
+ // Transform the result from R in a matrix
+ MatrixND morris = MatrixFactory.getInstance().create(dataframe,
+ new int[] { factornumber, param_simulationNumber });
+
+ // Setting up the scenarios.
+ for (int j = 0; j < param_simulationNumber; j++) {
+ Scenario experimentScenario = new Scenario();
+ for (int i = 0; i < factornumber; i++) {
+ Factor<? extends Serializable> factor = plan.getFactors()
+ .get(i);
+ if ((factor.getDomain() instanceof MatrixContinuousDomain)
+ || (factor.getDomain() instanceof EquationContinuousDomain)) {
+ factor
+ .setValueForIdentifier(Double.valueOf(
+ morris.getValue(new int[] { i, j }))
+ .toString());
+ } else {
+ Double value = (Double) ((ContinuousDomain<?>) factor
+ .getDomain()).getMinBound()
+ + ((Double) ((ContinuousDomain<?>) factor
+ .getDomain()).getMaxBound() - (Double) ((ContinuousDomain<?>) factor
+ .getDomain()).getMinBound())
+ * morris.getValue(new int[] { i, j });
+ factor.setValueForIdentifier(value);
+ }
+ experimentScenario.addFactor(factor);
+ }
+ thisExperimentScenarios.add(experimentScenario);
+ thisExperiment.setScenarios(thisExperimentScenarios);
+ }
+
+ //Create the factors vectors
+ for (int j = 0; j < factornumber; j++) {
+ String vector = "factor" + j + "<-c(";
+ for (int i = 0; i < param_simulationNumber; i++) {
+ if (i < (param_simulationNumber - 1)) {
+ vector = vector
+ + thisExperimentScenarios.get(i).getFactors()
+ .get(j).getValue() + ",";
+ } else {
+ vector = vector
+ + thisExperimentScenarios.get(i).getFactors()
+ .get(j).getValue();
+ }
+
+ }
+ vector = vector + ")";
+ engine.voidEval(vector);
+ log.info("Message sent to R : " + vector);
+
+ }
+
+ //Create the data data.frame from the factors
+ String data = "data<-data.frame(";
+ for (int j = 0; j < factornumber; j++) {
+ if (j < factornumber - 1) {
+ data = data + "factor" + j + "=factor(factor" + j + "),";
+ } else {
+ data = data + "factor" + j + "=factor(factor" + j + "))";
+ }
+
+ }
+ engine.voidEval(data);
+ log.info("Message sent to R : " + data);
+
+ // Set output directory
+ engine.voidEval("setwd(\"" + outputdirectory.getAbsolutePath()
+ + "\")");
+ log.info("Message sent to R : " + "setwd(\""
+ + outputdirectory.getAbsolutePath() + "\")");
+
+ // Export the scenario matrix for the second run in a .randomlhs.csv file
+ engine.voidEval("write.csv(data,file=\".optimumlhs.csv\")");
+ log.info("Message sent to R : "
+ + "write.csv(data,file=\".optimumlhs.csv\")");
+
+ engine.terminate();
+
+ } catch (Exception e) {
+ e.printStackTrace();
+ // Error while processing
+ }
+
+ return thisExperiment;
+ }
+
+ public void analyzeResult(List<SimulationStorage> simulationStorages,
+ File outputdirectory) throws SensitivityException {
+
+ REngine engine = new RProxy();
+ try {
+
+ for (int k = 0; k < param_simulationNumber; k++) {
+
+ // Set output directory
+ engine.voidEval("setwd(\"" + outputdirectory.getAbsolutePath()
+ + "\")");
+ log.info("Message sent to R : setwd(\""
+ + outputdirectory.getAbsolutePath() + "\")");
+
+ //Get back the scenarios
+ //engine.voidEval("factors<-read.csv(\".expandgrid.csv\",row.names=1,col.names=1)");
+ engine.voidEval("factors<-read.csv(\".optimumlhs.csv\")");
+ log
+ .info("Message sent to R : factors<-read.csv(\".optimumlhs.csv\")");
+
+ //Get back the factors number
+ int factorNumber = ((Double) engine.eval("length(factors)-1"))
+ .intValue();
+ //factorNumber=factorNumber-1;
+
+ //Create the results vectors
+ String result = "result<-c(";
+ for (int l = 0; l < simulationStorages.size(); l++) {
+ File importFile = new File(simulationStorages.get(l)
+ .getDirectory().toString()
+ + File.separator
+ + SimulationStorage.RESULT_EXPORT_DIRECTORY,
+ simulationStorages.get(l).getParameter()
+ .getSensitivityExport().get(k)
+ .getExportFilename()
+ + simulationStorages.get(l).getParameter()
+ .getSensitivityExport().get(k)
+ .getExtensionFilename());
+ String simulResult = FileUtil.readAsString(importFile);
+ double simulationResult = Double.valueOf(simulResult);
+ if (l < simulationStorages.size() - 1) {
+ result = result + simulationResult + ",";
+ } else {
+ result = result + simulationResult;
+ }
+ }
+ result = result + ")";
+ engine.voidEval(result);
+ log.info("Message sent to R : " + result);
+
+ //Create the dataforaov data.frame
+ String dataframe = "dataforaov<-data.frame(factors,result=result)";
+ engine.voidEval(dataframe);
+ log.info("Message sent to R : " + dataframe);
+
+ //Call aov()
+ String aovCall = "aovresult<-aov(result~";
+ for (int j = 0; j < factorNumber; j++) {
+ if (j < (factorNumber - 1)) {
+ aovCall = aovCall + "factor" + j + "+";
+ } else {
+ aovCall = aovCall + "factor" + j + ",data=dataforaov)";
+ }
+ }
+ engine.voidEval(aovCall);
+ log.info("Message sent to R : " + aovCall);
+
+ /*Export the results
+ *Export format is csv, data separated by ','
+ *Results Export name is sensitivityExportName_Results.csv
+ *Sensitivity Indices export name is sensitivityExportName_SensitivityIndices.csv
+ */
+
+ //Compute Sum of Squares and Sensitivity indices
+ engine.voidEval("SoS<-summary(aovresult)[[1]][1:"
+ + factorNumber + ",2]");
+ log.info("Message sent to R : SoS<-summary(aovresult)[[1]][1:"
+ + factorNumber + ",2]");
+ engine
+ .voidEval("names(SoS)<-dimnames(summary(aovresult)[[1]])[[1]][1:"
+ + factorNumber + "]");
+ log
+ .info("Message sent to R : names(SoS)<-dimnames(summary(aovresult)[[1]])[[1]][1:"
+ + factorNumber + "]");
+ engine.voidEval("IndSensibilite<-SoS/sum(SoS)");
+ log.info("Message sent to R : IndSensibilite<-SoS/sum(SoS)");
+
+ //Create a data.frame to export sensitivity important results in one file.
+ engine.voidEval("exportsensitivity=data.frame(SoS[1:"
+ + factorNumber + "],IndSensibilite[1:" + factorNumber
+ + "])");
+ log
+ .info("Message sent to R : exportsensitivity=data.frame(SoS[1:"
+ + factorNumber
+ + "],IndSensibilite[1:"
+ + factorNumber + "])");
+ engine
+ .voidEval("names(exportsensitivity)<-c(\"Sum Of Squares\",\"Sensitivity indices\")");
+ log
+ .info("Message sent to R : names(exportsensitivity)<-c(\"Sum Of Squares\",\"Sensitivity indices\")");
+
+ /*Set the export directory
+ *Export directory is the first simulation export directory.
+ */
+ engine.voidEval("setwd(\"" + outputdirectory.getAbsolutePath()
+ + "\")");
+ log.info("Message sent to R : setwd(\""
+ + outputdirectory.getAbsolutePath() + "\")");
+
+ //Save the results with the scenarios.
+ engine.voidEval("write.csv(dataforaov,\""
+ + simulationStorages.get(0).getParameter()
+ .getSensitivityExport().get(k)
+ .getExportFilename() + "_Results.csv\")");
+ log.info("Message sent to R : write.csv(dataforaov,\""
+ + simulationStorages.get(0).getParameter()
+ .getSensitivityExport().get(k)
+ .getExportFilename() + "_Results.csv\")");
+
+ //Save the sensitivity indices
+ engine.voidEval("write.csv(exportsensitivity,\""
+ + simulationStorages.get(0).getParameter()
+ .getSensitivityExport().get(k)
+ .getExportFilename()
+ + "_SensitivityIndices.csv\")");
+ log.info("Message sent to R : write.csv(exportsensitivity,\""
+ + simulationStorages.get(0).getParameter()
+ .getSensitivityExport().get(k)
+ .getExportFilename()
+ + "_SensitivityIndices.csv\")");
+ //FIXME export through java to enable export when using Rserve (when distant Rserve).
+ engine.terminate();
+ }
+ log.info("end");
+
+ } catch (Exception e) {
+ e.printStackTrace();
+ // Error while processing
+ }
+
+ }
+
+ public String getDescription() {
+ return "Implementation of Random Latin Hypercube method method using R";
+ }
+
+}
Added: trunk/sensitivity/SensitivityCalculatorRRandomLHS.java
===================================================================
--- trunk/sensitivity/SensitivityCalculatorRRandomLHS.java (rev 0)
+++ trunk/sensitivity/SensitivityCalculatorRRandomLHS.java 2009-03-25 13:53:22 UTC (rev 90)
@@ -0,0 +1,332 @@
+/* *##%
+ * Copyright (C) 2006 - 2009 Code Lutin
+ *
+ * This program is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU General Public License
+ * as published by the Free Software Foundation; either version 2
+ * of the License, or (at your option) any later version.
+ *
+ * This program is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with this program; if not, write to the Free Software
+ * Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
+ *##%*/
+
+package sensitivity;
+
+import java.io.File;
+import java.io.Serializable;
+import java.util.List;
+
+import org.apache.commons.logging.Log;
+import org.apache.commons.logging.LogFactory;
+import org.codelutin.j2r.REngine;
+import org.codelutin.j2r.RProxy;
+import org.codelutin.math.matrix.MatrixFactory;
+import org.codelutin.math.matrix.MatrixND;
+import org.codelutin.util.FileUtil;
+
+import fr.ifremer.isisfish.datastore.SimulationStorage;
+import fr.ifremer.isisfish.simulator.sensitivity.DesignPlan;
+import fr.ifremer.isisfish.simulator.sensitivity.Factor;
+import fr.ifremer.isisfish.simulator.sensitivity.Scenario;
+import fr.ifremer.isisfish.simulator.sensitivity.SensitivityCalculator;
+import fr.ifremer.isisfish.simulator.sensitivity.SensitivityException;
+import fr.ifremer.isisfish.simulator.sensitivity.SensitivityScenarios;
+import fr.ifremer.isisfish.simulator.sensitivity.domain.ContinuousDomain;
+import fr.ifremer.isisfish.simulator.sensitivity.domain.EquationContinuousDomain;
+import fr.ifremer.isisfish.simulator.sensitivity.domain.MatrixContinuousDomain;
+
+/**
+ * Implementation of Random Latin Hypercube method using R.
+ *
+ * @author jcouteau
+ * @version $Revision: 1.0 $
+ *
+ */
+
+public class SensitivityCalculatorRRandomLHS implements SensitivityCalculator {
+
+ /** to use log facility, just put in your code: log.info("..."); */
+ static private Log log = LogFactory
+ .getLog(SensitivityCalculatorRRandomLHS.class);
+
+ public int param_simulationNumber;
+
+ /**
+ * Retourne vrai si le calculateur sait gerer la cardinalité des facteurs
+ * continue.
+ *
+ * @return <tt>true</tt> s'il sait la gerer
+ */
+ public boolean canManageCardinality() {
+ return true;
+ }
+
+ public SensitivityScenarios compute(DesignPlan plan, File outputdirectory)
+ throws SensitivityException {
+
+ int factornumber = plan.getFactors().size();
+ double[] dataframe = new double[0];
+ SensitivityScenarios thisExperiment = new SensitivityScenarios();
+ List<Scenario> thisExperimentScenarios = thisExperiment.getScenarios();
+
+ REngine engine = new RProxy();
+ try {
+
+ //Load the lhs library
+ engine.voidEval("library(lhs)");
+ log.info("Message sent to R : " + "library(lhs)");
+
+ //Create the scenarios
+ engine.voidEval("x<-randomLHS(" + param_simulationNumber + ","
+ + factornumber + ")");
+ log.info("Message sent to R : " + "x<-randomLHS("
+ + param_simulationNumber + "," + factornumber + ")");
+
+ // Get back experiment plan
+ dataframe = (double[]) engine.eval("x");
+ log.info("Message sent to R" + "x");
+
+ // Transform the result from R in a matrix
+ MatrixND morris = MatrixFactory.getInstance().create(dataframe,
+ new int[] { factornumber, param_simulationNumber });
+
+
+ // Setting up the scenarios.
+ for (int j = 0; j < param_simulationNumber; j++) {
+ Scenario experimentScenario = new Scenario();
+ for (int i = 0; i < factornumber; i++) {
+ Factor<? extends Serializable> factor = plan.getFactors()
+ .get(i);
+ if ((factor.getDomain() instanceof MatrixContinuousDomain)
+ || (factor.getDomain() instanceof EquationContinuousDomain)) {
+ factor
+ .setValueForIdentifier(Double.valueOf(
+ morris.getValue(new int[] { i, j }))
+ .toString());
+ } else {
+ Double value = (Double) ((ContinuousDomain<?>) factor
+ .getDomain()).getMinBound()
+ + ((Double) ((ContinuousDomain<?>) factor
+ .getDomain()).getMaxBound() - (Double) ((ContinuousDomain<?>) factor
+ .getDomain()).getMinBound())
+ * morris.getValue(new int[] { i, j });
+ factor.setValueForIdentifier(value);
+ }
+ experimentScenario.addFactor(factor);
+ }
+ thisExperimentScenarios.add(experimentScenario);
+ thisExperiment.setScenarios(thisExperimentScenarios);
+ }
+
+ //Create the factors vectors
+ for (int j = 0; j < factornumber; j++) {
+ String vector = "factor" + j + "<-c(";
+ for (int i = 0; i < param_simulationNumber; i++) {
+ if (i < (param_simulationNumber - 1)) {
+ vector = vector
+ + thisExperimentScenarios.get(i).getFactors()
+ .get(j).getValue() + ",";
+ } else {
+ vector = vector
+ + thisExperimentScenarios.get(i).getFactors()
+ .get(j).getValue();
+ }
+
+ }
+ vector = vector + ")";
+ engine.voidEval(vector);
+ log.info("Message sent to R : " + vector);
+
+ }
+
+ //Create the data data.frame from the factors
+ String data = "data<-data.frame(";
+ for (int j = 0; j < factornumber; j++) {
+ if (j < factornumber - 1) {
+ data = data + "factor" + j + "=factor(factor" + j
+ + "),";
+ } else {
+ data = data + "factor" + j + "=factor(factor" + j
+ + "))";
+ }
+
+ }
+ engine.voidEval(data);
+ log.info("Message sent to R : " + data);
+
+
+
+ // Set output directory
+ engine.voidEval("setwd(\"" + outputdirectory.getAbsolutePath()
+ + "\")");
+ log.info("Message sent to R : " + "setwd(\""
+ + outputdirectory.getAbsolutePath() + "\")");
+
+ // Export the scenario matrix for the second run in a .randomlhs.csv file
+ engine.voidEval("write.csv(data,file=\".randomlhs.csv\")");
+ log.info("Message sent to R : "
+ + "write.csv(data,file=\".randomlhs.csv\")");
+
+ engine.terminate();
+
+ } catch (Exception e) {
+ e.printStackTrace();
+ // Error while processing
+ }
+
+ return thisExperiment;
+ }
+
+ public void analyzeResult(List<SimulationStorage> simulationStorages,
+ File outputdirectory) throws SensitivityException {
+
+ REngine engine = new RProxy();
+ try {
+
+ for (int k = 0; k < param_simulationNumber; k++) {
+
+ // Set output directory
+ engine.voidEval("setwd(\"" + outputdirectory.getAbsolutePath()
+ + "\")");
+ log.info("Message sent to R : setwd(\""
+ + outputdirectory.getAbsolutePath() + "\")");
+
+ //Get back the scenarios
+ //engine.voidEval("factors<-read.csv(\".expandgrid.csv\",row.names=1,col.names=1)");
+ engine.voidEval("factors<-read.csv(\".randomlhs.csv\")");
+ log
+ .info("Message sent to R : factors<-read.csv(\".randomlhs.csv\")");
+
+ //Get back the factors number
+ int factorNumber = ((Double) engine.eval("length(factors)-1"))
+ .intValue();
+ //factorNumber=factorNumber-1;
+
+ //Create the results vectors
+ String result = "result<-c(";
+ for (int l = 0; l < simulationStorages.size(); l++) {
+ File importFile = new File(simulationStorages.get(l)
+ .getDirectory().toString()
+ + File.separator
+ + SimulationStorage.RESULT_EXPORT_DIRECTORY,
+ simulationStorages.get(l).getParameter()
+ .getSensitivityExport().get(k)
+ .getExportFilename()
+ + simulationStorages.get(l).getParameter()
+ .getSensitivityExport().get(k)
+ .getExtensionFilename());
+ String simulResult = FileUtil.readAsString(importFile);
+ double simulationResult = Double.valueOf(simulResult);
+ if (l < simulationStorages.size() - 1) {
+ result = result + simulationResult + ",";
+ } else {
+ result = result + simulationResult;
+ }
+ }
+ result = result + ")";
+ engine.voidEval(result);
+ log.info("Message sent to R : " + result);
+
+ //Create the dataforaov data.frame
+ String dataframe = "dataforaov<-data.frame(factors,result=result)";
+ engine.voidEval(dataframe);
+ log.info("Message sent to R : " + dataframe);
+
+ //Call aov()
+ String aovCall = "aovresult<-aov(result~";
+ for (int j = 0; j < factorNumber; j++) {
+ if (j < (factorNumber - 1)) {
+ aovCall = aovCall + "factor" + j + "+";
+ } else {
+ aovCall = aovCall + "factor" + j + ",data=dataforaov)";
+ }
+ }
+ engine.voidEval(aovCall);
+ log.info("Message sent to R : " + aovCall);
+
+ /*Export the results
+ *Export format is csv, data separated by ','
+ *Results Export name is sensitivityExportName_Results.csv
+ *Sensitivity Indices export name is sensitivityExportName_SensitivityIndices.csv
+ */
+
+ //Compute Sum of Squares and Sensitivity indices
+ engine.voidEval("SoS<-summary(aovresult)[[1]][1:"
+ + factorNumber + ",2]");
+ log.info("Message sent to R : SoS<-summary(aovresult)[[1]][1:"
+ + factorNumber + ",2]");
+ engine
+ .voidEval("names(SoS)<-dimnames(summary(aovresult)[[1]])[[1]][1:"
+ + factorNumber + "]");
+ log
+ .info("Message sent to R : names(SoS)<-dimnames(summary(aovresult)[[1]])[[1]][1:"
+ + factorNumber + "]");
+ engine.voidEval("IndSensibilite<-SoS/sum(SoS)");
+ log.info("Message sent to R : IndSensibilite<-SoS/sum(SoS)");
+
+ //Create a data.frame to export sensitivity important results in one file.
+ engine.voidEval("exportsensitivity=data.frame(SoS[1:"
+ + factorNumber + "],IndSensibilite[1:" + factorNumber
+ + "])");
+ log
+ .info("Message sent to R : exportsensitivity=data.frame(SoS[1:"
+ + factorNumber
+ + "],IndSensibilite[1:"
+ + factorNumber + "])");
+ engine
+ .voidEval("names(exportsensitivity)<-c(\"Sum Of Squares\",\"Sensitivity indices\")");
+ log
+ .info("Message sent to R : names(exportsensitivity)<-c(\"Sum Of Squares\",\"Sensitivity indices\")");
+
+ /*Set the export directory
+ *Export directory is the first simulation export directory.
+ */
+ engine.voidEval("setwd(\"" + outputdirectory.getAbsolutePath()
+ + "\")");
+ log.info("Message sent to R : setwd(\""
+ + outputdirectory.getAbsolutePath() + "\")");
+
+ //Save the results with the scenarios.
+ engine.voidEval("write.csv(dataforaov,\""
+ + simulationStorages.get(0).getParameter()
+ .getSensitivityExport().get(k)
+ .getExportFilename() + "_Results.csv\")");
+ log.info("Message sent to R : write.csv(dataforaov,\""
+ + simulationStorages.get(0).getParameter()
+ .getSensitivityExport().get(k)
+ .getExportFilename() + "_Results.csv\")");
+
+ //Save the sensitivity indices
+ engine.voidEval("write.csv(exportsensitivity,\""
+ + simulationStorages.get(0).getParameter()
+ .getSensitivityExport().get(k)
+ .getExportFilename()
+ + "_SensitivityIndices.csv\")");
+ log.info("Message sent to R : write.csv(exportsensitivity,\""
+ + simulationStorages.get(0).getParameter()
+ .getSensitivityExport().get(k)
+ .getExportFilename()
+ + "_SensitivityIndices.csv\")");
+ //FIXME export through java to enable export when using Rserve (when distant Rserve).
+ engine.terminate();
+ }
+ log.info("end");
+
+ } catch (Exception e) {
+ e.printStackTrace();
+ // Error while processing
+ }
+
+ }
+
+ public String getDescription() {
+ return "Implementation of Random Latin Hypercube method method using R";
+ }
+
+}
