/* $RCSfile$
* $Author$
* $Date$
* $Revision$
*
* Copyright (C) 2004-2008 Rajarshi Guha <rajarshi.guha@gmail.com>
*
* Contact: cdk-devel@lists.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* 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 Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.cdk.qsar.model.R;
/**
* A class that wraps the return value from the R function, predict.nnet for classification models.
*
* This is an internal class used by R to return the result of
* the call to <a href="http://stat.ethz.ch/R-manual/R-patched/library/nnet/html/predict.nnet.html">predict.nnet</a>.
* As a result it should not be instantiated by the user. The actual modeling
* class, <code>CNNClassificationModel</code>, provides acess to the various
* fields of this object.
*
*
* @author Rajarshi Guha
* @cdk.require r-project
* @cdk.module qsar
* @cdk.githash
* @deprecated
*/
public class CNNClassificationModelPredict {
private int noutput;
private double[][] predvalraw;
private String[] predvalclass;
private double[][] vectorToMatrix(double[] v, int nrow, int ncol) {
double[][] m = new double[nrow][ncol];
for (int i = 0; i < ncol; i++) {
for (int j = 0; j < nrow; j++) {
m[j][i] = v[j + i*nrow];
}
}
return(m);
}
/**
* Create an object to hold predictions from a previously built CNN model.
*
* This class should not be accessed directly
*
* @param noutput The number of predicted variables
* @param values The predicted probabilities
*/
public CNNClassificationModelPredict(int noutput, double[] values) {
this.noutput = noutput;
int nrow = values.length / noutput;
setPredictedRaw(vectorToMatrix(values,nrow,noutput));
}
/**
* Create an object to hold predictions from a previously built CNN model.
*
* This class should not be accessed directly. Required for the case of a single
* predicted value.
*
* @param noutput The number of predicted variables
* @param values The predicted probabilities
*/
public CNNClassificationModelPredict(int noutput, double values) {
this.noutput = noutput;
setPredictedRaw(new double[][] { {values} });
}
/**
* Create an object to hold predictions from a previously built CNN model.
*
* This class should not be accessed directly
*
* @param values An array of String containing the predicted class
*/
public CNNClassificationModelPredict(String[] values) {
this.predvalclass = new String[values.length];
for (int i = 0; i < values.length; i++) {
this.predvalclass[i] = values[i];
}
}
/**
* Create an object to hold predictions from a previously built CNN model.
*
* This class should not be accessed directly. Required for the
* case of a single predicted value
*
* @param values An array of String containing the predicted class
*/
public CNNClassificationModelPredict(String values) {
this.predvalclass = new String[1];
this.predvalclass[1] = values;
}
/**
* Get the raw probabilities of the classification result.
*
* This class should not be accessed directly
*
* @return A 2-dimensional array containing the predicted probabilities. The rows
* contain the observations and the columns contain the predicted variables
* @see #setPredictedRaw
*/
public double[][] getPredictedRaw() { return(this.predvalraw); }
/**
* Get the raw probabilities of the classification result.
*
* This class should not be accessed directly
*
* @param predicted A 2-dimensional array containing the predicted probabilities. The rows
* contain the observations and the columns contain the predicted variables
* @see #getPredictedRaw
*/
public void setPredictedRaw(double[][] predicted) {
this.predvalraw = new double[predicted.length][this.noutput];
for (int i = 0; i < predicted.length; i++) {
for (int j = 0; j < this.noutput; j++) {
this.predvalraw[i][j] = predicted[i][j];
}
}
}
/**
* Get the predicted classes.
*
* This class should not be accessed directly
*
* @return An array of String containing the predicted classes
*/
public String[] getPredictedClass() { return(this.predvalclass); };
}