MultiDimensionFitter.java
/*
* Copyright (C) 2015 Alberto Irurueta Carro (alberto@irurueta.com)
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.irurueta.numerical.fitting;
import com.irurueta.algebra.Matrix;
import java.util.Arrays;
/**
* Base class to fit a multi dimension function y = f(x1, x2, ...) by using
* provided data (x, y)
*/
public abstract class MultiDimensionFitter extends Fitter {
/**
* Input points x where a multidimensional function f(x1, x2, ...) is
* evaluated where each column of the matrix represents each dimension of
* the point and each row is related to each sample corresponding to
* provided y pairs of values
*/
protected Matrix x;
/**
* Result of evaluation of multidimensional function f(x1, x2, ...)
* at provided x points. This is provided as input data along x array
*/
protected double[] y;
/**
* Standard deviations of each pair of points (x, y).
*/
protected double[] sig;
/**
* Number of samples (x, y) in provided input data
*/
protected int ndat;
/**
* Estimated parameters of linear single dimensional function
*/
protected double[] a;
/**
* Covariance of estimated parameters of linear single dimensional function
*/
protected Matrix covar;
/**
* Estimated chi square value of input data
*/
protected double chisq;
/**
* Constructor
*/
protected MultiDimensionFitter() {
}
/**
* Constructor
*
* @param x input points x where a multidimensional function f(x1, x2, ...)
* is evaluated
* @param y result of evaluation of multidimensional function
* f(x1, x2, ...) at provided x points
* @param sig standard deviations of each pair of points (x, y)
* @throws IllegalArgumentException if provided matrix rows and arrays
* don't have the same length
*/
protected MultiDimensionFitter(final Matrix x, final double[] y, final double[] sig) {
setInputData(x, y, sig);
}
/**
* Constructor
*
* @param x input points x where a multidimensional function f(x1, x2, ...)
* is evaluated
* @param y result of evaluation of linear multidimensional function
* f(x1, x2, ...) at provided x points
* @param sig standard deviation of all pair of points assuming that
* standard deviations are constant
* @throws IllegalArgumentException if provided matrix rows and arrays
* don't have the same length
*/
protected MultiDimensionFitter(final Matrix x, final double[] y, final double sig) {
setInputData(x, y, sig);
}
/**
* Returns input points x where a multidimensional function f(x1, x2, ...)
* is evaluated and where each column of the matrix represents
* each dimension of the point and each row is related to each sample
* corresponding to provided y pairs of values
*
* @return input point x
*/
public Matrix getX() {
return x;
}
/**
* Returns result of evaluation of multidimensional function f(x)
* at provided x points. This is provided as input data along with x array
*
* @return result of evaluation
*/
public double[] getY() {
return y;
}
/**
* Returns standard deviations of each pair of points (x,y).
*
* @return standard deviations of each pair of points (x,y)
*/
public double[] getSig() {
return sig;
}
/**
* Sets required input data to start function fitting
*
* @param x input points x where a multidimensional function f(x1, x2, ...)
* is evaluated and where each column of the matrix represents each
* dimension of the point and each row is related to each sample
* corresponding to provided y pairs of values
* @param y result of evaluation of multidimensional function
* f(x1, x2, ...) at provided x points. This is provided as input data along
* with x array
* @param sig standard deviations of each pair of points (x,y)
* @throws IllegalArgumentException if provided arrays don't have the same
* size
*/
public final void setInputData(final Matrix x, final double[] y, final double[] sig) {
if (x.getRows() != y.length || sig.length != y.length) {
throw new IllegalArgumentException();
}
this.x = x;
this.y = y;
this.sig = sig;
ndat = y.length;
}
/**
* Sets required input data to start function fitting and assuming constant
* standard deviation errors in input data
*
* @param x input points x where a multidimensional function f(x1, x2, ...)
* is evaluated and where each column of the matrix represents each
* dimension of the point and each row is related to each sample
* corresponding to provided y pairs of values
* @param y result of evaluation of multidimensional function
* f(x1, x2, ...) at provided x points. This is provided as input data along
* with x array
* @param sig standard deviation of all pair of points assuming that
* standard deviations are constant
* @throws IllegalArgumentException if provided arrays don't have the same
* size
*/
public final void setInputData(final Matrix x, final double[] y, final double sig) {
if (x.getRows() != y.length) {
throw new IllegalArgumentException();
}
this.x = x;
this.y = y;
this.sig = new double[y.length];
Arrays.fill(this.sig, sig);
ndat = y.length;
}
/**
* Returns estimated parameters of linear single dimensional function
*
* @return estimated parameters
*/
public double[] getA() {
return a;
}
/**
* Returns covariance of estimated parameters of linear single dimensional
* function
*
* @return covariance of estimated parameters
*/
public Matrix getCovar() {
return covar;
}
/**
* Returns estimated chi square value of input data
*
* @return estimated chi square value of input data
*/
public double getChisq() {
return chisq;
}
}