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///////////////////////////////////////////////////////////////////////////////
// For information as to what this class does, see the Javadoc, below. //
// Copyright (C) 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, //
// 2007, 2008, 2009, 2010, 2014, 2015, 2022 by Peter Spirtes, Richard //
// Scheines, Joseph Ramsey, and Clark Glymour. //
// //
// 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 edu.cmu.tetradapp.model;
import edu.cmu.tetrad.data.*;
import edu.cmu.tetrad.graph.Graph;
import edu.cmu.tetrad.util.Parameters;
import edu.cmu.tetradapp.session.ParamsResettable;
import java.io.Serial;
import java.util.List;
/**
* Implements a stub that basic algorithm wrappers can extend if they take either a dataModel model or a workbench model
* as parent. Contains basic methods for executing algorithm and returning results.
*
* @author josephramsey
* @version $Id: $Id
*/
public abstract class AbstractMimRunner implements MimRunner, ParamsResettable {
@Serial
private static final long serialVersionUID = 23L;
/**
* Keeps a reference to the dataModel source that has been provided (hopefully either a dataModel model or a
* graph).
*/
private final transient DataModel dataModel;
/**
* The name of the algorithm.
*/
private String name;
/**
* The parameters guiding this search (when executed).
*/
private Parameters params;
/**
* Clusters resulting from the last run of the algorithm.
*/
private Clusters clusters = new Clusters();
/**
* Keeps a reference to the source graph, if there is one.
*/
private Graph sourceGraph;
/**
* Keeps a reference to the result graph for the algorithm.
*/
private Graph resultGraph;
/**
* The result structure graph, if there is one. Otherwise, null.
*/
private Graph structureGraph;
/**
* Constructs a wrapper for the given DataWrapper. The DatWrapper must contain a DataSet that is either a DataSet or
* a DataSet or a DataList containing either a DataSet or a DataSet as its selected model.
*/
private DataWrapper dataWrapper;
//===========================CONSTRUCTORS===========================//
/**
* Constructs a wrapper for the given DataWrapper. The DatWrapper must contain a DataSet that is either a DataSet or
* a DataSet or a DataList containing either a DataSet or a DataSet as its selected model.
*/
AbstractMimRunner(DataWrapper dataWrapper, Clusters clusters, Parameters params) {
if (dataWrapper == null) {
throw new NullPointerException();
}
if (params == null) {
throw new NullPointerException();
}
this.dataWrapper = dataWrapper;
this.params = params;
setClusters(clusters);
this.sourceGraph = dataWrapper.getSourceGraph();
DataModel data = getDataModel(dataWrapper);
getParams().set("knowledge", dataWrapper.getKnowledge());
List names = data.getVariableNames();
transferVarNamesToParams(names);
this.dataModel = data;
}
AbstractMimRunner(MeasurementModelWrapper wrapper, Clusters clusters, Parameters params) {
if (wrapper == null) {
throw new NullPointerException();
}
if (params == null) {
throw new NullPointerException();
}
this.params = params;
setClusters(clusters);
// this.sourceGraph = wrapper.getSourceGraph();
DataModel data = wrapper.getData();
List names = data.getVariableNames();
transferVarNamesToParams(names);
this.dataModel = data;
}
AbstractMimRunner(MimRunner runner, Parameters params) {
if (runner == null) {
throw new NullPointerException();
}
this.params = params;
this.params.set("clusters", runner.getClusters());
this.sourceGraph = runner.getSourceGraph();
DataModel dataSource = runner.getData();
List names = dataSource.getVariableNames();
transferVarNamesToParams(names);
this.dataModel = dataSource;
}
//============================PUBLIC METHODS==========================//
/**
* Getter for the field resultGraph.
*
* @return a {@link edu.cmu.tetrad.graph.Graph} object
*/
public final Graph getResultGraph() {
return this.resultGraph;
}
void setResultGraph(Graph graph) {
this.resultGraph = graph;
}
/**
* Getter for the field clusters.
*
* @return a {@link edu.cmu.tetrad.data.Clusters} object
*/
public Clusters getClusters() {
return this.clusters;
}
void setClusters(Clusters clusters) {
if (clusters == null) {
throw new NullPointerException();
}
this.clusters = clusters;
}
/**
* Getter for the field structureGraph.
*
* @return a {@link edu.cmu.tetrad.graph.Graph} object
*/
public Graph getStructureGraph() {
return this.structureGraph;
}
void setStructureGraph(Graph graph) {
this.structureGraph = graph;
}
/**
* getFullGraph.
*
* @return a {@link edu.cmu.tetrad.graph.Graph} object
*/
public Graph getFullGraph() {
return null;
}
/**
* Getter for the field sourceGraph.
*
* @return a {@link edu.cmu.tetrad.graph.Graph} object
*/
public final Graph getSourceGraph() {
return this.sourceGraph;
}
/**
* getData.
*
* @return a {@link edu.cmu.tetrad.data.DataModel} object
*/
public final DataModel getData() {
if (this.dataWrapper != null) {
DataModelList dataModelList = this.dataWrapper.getDataModelList();
if (dataModelList.size() == 1) {
return dataModelList.get(0);
} else {
return dataModelList;
}
} else if (this.dataModel != null) {
return this.dataModel;
} else {
throw new IllegalArgumentException();
}
}
//===========================PROTECTED METHODS========================//
/**
* Getter for the field params.
*
* @return a {@link edu.cmu.tetrad.util.Parameters} object
*/
public final Parameters getParams() {
return this.params;
}
/**
* {@inheritDoc}
*/
public void resetParams(Object params) {
this.params = (Parameters) params;
}
/**
* getResettableParams.
*
* @return a {@link java.lang.Object} object
*/
public Object getResettableParams() {
return this.params;
}
//===========================PRIVATE METHODS==========================//
/**
* Find the dataModel model. (If it's a list, take the one that's selected.)
*/
private DataModel getDataModel(DataWrapper dataWrapper) {
DataModel dataModel = dataWrapper.getSelectedDataModel();
if (dataModel instanceof DataModelList dataModelList) {
dataModel = dataModelList.getSelectedModel();
}
if (dataModel instanceof DataSet dataSet) {
if (dataSet.isDiscrete()) {
return dataSet;
} else if (dataSet.isContinuous()) {
return dataSet;
}
throw new IllegalArgumentException("" +
"This dataModel set contains a mixture of discrete and continuous " +
"
columns; there are no algorithm in Tetrad currently to " +
"
search over such data sets." + "");
} else if (dataModel instanceof ICovarianceMatrix) {
return dataModel;
} else if (dataModel instanceof TimeSeriesData) {
return dataModel;
}
throw new IllegalArgumentException(
"Unexpected dataModel source: " + dataModel);
}
private void transferVarNamesToParams(List names) {
getParams().set("varNames", names);
}
/**
* Getter for the field name.
*
* @return a {@link java.lang.String} object
*/
public String getName() {
return this.name;
}
/**
* {@inheritDoc}
*/
public void setName(String name) {
this.name = name;
}
}
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