/* $RCSfile$
* $Author$
* $Date$
* $Revision$
*
* 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 R function, nnet().
*
* 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/nnet.html">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 CNNClassificationModelFit {
private int noutput; // number of output neurons (== number of classes)
private double[] weights;
private double[][] fitted;
private double[][] residuals;
private double value;
private double[][] hessian = null;
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);
}
/**
* Constructs an object to contain a CNN classification fit.
*
* This class should not be instantiated directly and is only
* required withtin R
*
* @param noutput The number of output neurons (ie the number of predicted variables)
* @param nobs The number of observations
* @param weights A 1-dimensional array containing the weights and biases
* @param fitted A 1-dimensional array containing the fitted values
* @param residual A 1-dimensional array containing the residuals of the fitted values
* @param value The final value of the cost function
* @param hessian A 1-dimensional array containing the Hessian
*/
public CNNClassificationModelFit(
int noutput,
int nobs,
double[] weights,
double[] fitted, double[] residual,
double value,
double[] hessian) {
// dimensions of hessian = nwt x nwt
// dimensions of fitted, residual = nobs x noutput
// also note that matrices come in as columnwise 1D arrays
this.noutput = noutput;
setWeights(weights);
setResiduals(vectorToMatrix(residual, nobs,noutput));
setFitted(vectorToMatrix(fitted, nobs,noutput));
setValue(value);
setHessian(vectorToMatrix(hessian,weights.length,weights.length));
}
/**
* Constructs an object to contain a CNN classification fit.
*
* This class should not be instantiated directly and is only
* required withtin R
*
* @param noutput The number of output neurons (ie the number of predicted variables)
* @param nobs The number of observations
* @param weights A 1-dimensional array containing the weights and biases
* @param fitted A 1-dimensional array containing the fitted values
* @param residual A 1-dimensional array containing the residuals of the fitted values
* @param value The final value of the cost function
*/
public CNNClassificationModelFit(
int noutput,
int nobs,
double[] weights,
double[] fitted, double[] residual,
double value) {
this.noutput = noutput;
setWeights(weights);
setResiduals(vectorToMatrix(residual, nobs,noutput));
setFitted(vectorToMatrix(fitted, nobs,noutput));
setValue(value);
}
/**
* Get the final value of the cost function.
*
* This method should not be called outside this class
*
* @return The final value of the cost function
* @see #setValue
*/
public double getValue() {
return(this.value);
}
/**
* Set the final value of the cost function.
*
* This method should not be called outside this class
*
* @param value The value of the cost function at convergence
* @see #getValue
*/
public void setValue(double value) {
this.value = value;
}
/**
* Get the Hessian for the final network.
*
* This method should not be called outside this class
*
* @return A 2-dimensional array containing the hessian
* @see #setHessian
*/
public double[][] getHessian() { return(this.hessian); }
/**
* Set the Hessian for the final network.
*
* This method should not be called outside this class
*
* @param theHessian A 2-dimensional array containing the hessian
* @see #getHessian
*/
public void setHessian(double[][] theHessian) {
if (theHessian == null) return;
this.hessian = new double[theHessian.length][this.noutput];
for (int i = 0; i < theHessian.length; i++) {
for (int j = 0; j < this.noutput; j++) {
this.hessian[i][j] = theHessian[i][j];
}
}
}
/**
* Get the weights and biases of the final network.
*
* This method should not be called outside this class
*
* @return A 1-dimensional array of weights and biases
* @see #setWeights
*/
public double[] getWeights() { return(this.weights); }
/**
* Set the weights and biases of the final network.
*
* This method should not be called outside this class
*
* @param weights A 1-dimensional array of weights and biases
* @see #getWeights
*/
public void setWeights(double[] weights) {
this.weights = new double[weights.length];
for (int i = 0; i < weights.length; i++) {
this.weights[i] = weights[i];
}
}
/**
* Get the residuals of the fit.
*
* This method should not be called outside this class
*
* @return A 2-dimensional array of residuals. The rows contain the
* observations and the columns contain the predicted variables
* @see #setResiduals
*/
public double[][] getResiduals() { return(this.residuals); }
/**
* Set the residuals of the fit.
*
* This method should not be called outside this class
*
* @param residuals A 2-dimensional array of residuals. The rows contain the
* observations and the columns contain the predicted variables
* @see #getResiduals
*/
public void setResiduals(double[][] residuals) {
this.residuals = new double[residuals.length][this.noutput];
for (int i = 0; i < residuals.length; i++) {
for (int j = 0; j < this.noutput; j++) {
this.residuals[i][j] = residuals[i][j];
}
}
}
/**
* Get the fitted values.
*
* This method should not be called outside this class
*
* @return A 2-dimensional array of residuals. The rows contain the
* observations and the columns contain the predicted variables
* @see #setFitted
*/
public double[][] getFitted() { return(this.fitted); }
/**
* Set the fitted values.
*
* This method should not be called outside this class
*
* @param fitted A 2-dimensional array of residuals. The rows contain the
* observations and the columns contain the predicted variables
* @see #getFitted
*/
public void setFitted(double[][] fitted) {
this.fitted = new double[fitted.length][this.noutput];
for (int i = 0; i < fitted.length; i++) {
for (int j = 0; j < this.noutput; j++) {
this.fitted[i][j] = fitted[i][j];
}
}
}
}