MultiVariateFitter.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 variate function [y1, y2, ...] = f([x1, x2, ...])
* by using provided data (x, y).
*/
public abstract class MultiVariateFitter 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 multi variate function f(x1, x2, ...) at
* provided x points.
* Each row contains the function evaluation for a given point x, and
* each column contains values for each output variable f1, f2, ...
*/
protected Matrix 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 MultiVariateFitter() {
}
/**
* Constructor.
*
* @param x input points x where a multi variate function f(x1, x2, ...) is
* evaluated.
* @param y result of evaluation of multi variate 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 MultiVariateFitter(final Matrix x, final Matrix y, final double[] sig) {
setInputData(x, y, sig);
}
/**
* Constructor.
*
* @param x input points x where a multi variate function f(x1, x2, ...) is
* evaluated.
* @param y result of evaluation of multi variate 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 MultiVariateFitter(final Matrix x, final Matrix y, final double sig) {
setInputData(x, y, sig);
}
/**
* Returns input points x where a multi variate 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 multi variate function f(x) at provided
* x points. This is provided as input data along with x array.
*
* @return result of evaluation.
*/
public Matrix 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 multi variate 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 multi variate 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 Matrix y, final double[] sig) {
if (x.getRows() != y.getRows() || sig.length != y.getRows()) {
throw new IllegalArgumentException();
}
this.x = x;
this.y = y;
this.sig = sig;
ndat = sig.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 multi variate 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 multi variate 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 Matrix y, final double sig) {
if (x.getRows() != y.getRows()) {
throw new IllegalArgumentException();
}
this.x = x;
this.y = y;
ndat = y.getRows();
this.sig = new double[ndat];
Arrays.fill(this.sig, sig);
}
/**
* 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;
}
}