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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.tetrad.data;
import edu.cmu.tetrad.graph.Node;
import edu.cmu.tetrad.util.Matrix;
import edu.cmu.tetrad.util.MatrixUtils;
import org.apache.commons.math3.util.FastMath;
import java.util.Collections;
import java.util.LinkedList;
import java.util.List;
/**
* Stores a correlation matrix together with variable names and sample size; intended as a representation of a data
* set.
*
* @author josephramsey
*/
public final class CorrelationMatrix extends CovarianceMatrix {
private static final long serialVersionUID = 23L;
/**
* Constructs a new correlation matrix using the covariances in the given covariance matrix.
*/
public CorrelationMatrix(ICovarianceMatrix matrix) {
this(matrix.getVariables(), MatrixUtils.convertCovToCorr(matrix.getMatrix()), matrix.getSampleSize());
}
/**
* Constructs a new correlation matrix from the the given DataSet.
*/
public CorrelationMatrix(DataSet dataSet) {
super(Collections.unmodifiableList(dataSet.getVariables()),
dataSet.getCorrelationMatrix(), dataSet.getNumRows());
// These checks break testwise deletion
}
/**
* Constructs a correlation matrix data set using the given information. The matrix matrix is internally converted
* to a correlation matrix.
*/
public CorrelationMatrix(List variables, Matrix matrix,
int sampleSize) {
super(variables, MatrixUtils.convertCovToCorr(matrix).copy(), sampleSize);
}
/**
* Generates a simple exemplar of this class to test serialization.
*/
public static CorrelationMatrix serializableInstance() {
return new CorrelationMatrix(new LinkedList<>(),
new Matrix(0, 0), 1);
}
public void setMatrix(Matrix matrix) {
if (!matrix.isSquare()) {
throw new IllegalArgumentException("Matrix must be square.");
}
for (int i = 0; i < matrix.getNumRows(); i++) {
if (FastMath.abs(matrix.get(i, i) - 1.0) > 1.e-5) {
throw new IllegalArgumentException(
"For a correlation matrix, " +
"variances (diagonal elements) must be 1.0");
}
}
super.setMatrix(matrix);
}
@Override
public Matrix getSelection(int[] rows, int[] cols) {
return getMatrix().getSelection(rows, cols);
}
/**
* @return a submatrix, returning as a correlation matrix, with variables in the given order.
*/
public CorrelationMatrix getSubCorrMatrix(String[] submatrixVarNames) {
ICovarianceMatrix covarianceMatrix = getSubmatrix(submatrixVarNames);
return new CorrelationMatrix(covarianceMatrix);
}
}