/**
* Copyright (C) 2014 - present by OpenGamma Inc. and the OpenGamma group of companies
*
* Please see distribution for license.
*/
package com.opengamma.analytics.math.function;
import com.opengamma.analytics.math.differentiation.ScalarFieldFirstOrderDifferentiator;
import com.opengamma.analytics.math.matrix.DoubleMatrix1D;
import com.opengamma.util.tuple.DoublesPair;
/**
* A parameterised surface that gives the both the surface (the function z=f(xy) where xy is a 2D point and z is a scalar)
* and the surface sensitivity (dz/dp where p is one of the parameters) for given parameters.
*/
public abstract class ParameterizedSurface extends ParameterizedFunction<DoublesPair, DoubleMatrix1D, Double> {
private static final ScalarFieldFirstOrderDifferentiator FIRST_ORDER_DIFF = new ScalarFieldFirstOrderDifferentiator();
/**
* For a function of two variables (surface) that can be written as $z=f(x, y;\boldsymbol{\theta})$ where x, y & z are scalars and
* $\boldsymbol{\theta})$ is a vector of parameters (i.e. $x,y,z \in \mathbb{R}$ and $\boldsymbol{\theta} \in \mathbb{R}^n$)
* this returns the function $g : \mathbb{R} \to \mathbb{R}^n; x,y \mapsto g(x,y)$, which is the function's (curves') sensitivity
* to its parameters, i.e. $g(x,y) = \frac{\partial f(x,y;\boldsymbol{\theta})}{\partial \boldsymbol{\theta}}$<p>
* The default calculation is performed using finite difference (via {@link ScalarFieldFirstOrderDifferentiator}) but
* it is expected that this will be overridden by concrete subclasses.
* @param params The value of the parameters ($\boldsymbol{\theta}$) at which the sensitivity is calculated
* @return The sensitivity as a function with a DoublesPair (x,y) as its single argument and a vector as its return value
*/
public Function1D<DoublesPair, DoubleMatrix1D> getZParameterSensitivity(final DoubleMatrix1D params) {
return new Function1D<DoublesPair, DoubleMatrix1D>() {
@Override
public DoubleMatrix1D evaluate(final DoublesPair xy) {
final Function1D<DoubleMatrix1D, Double> f = asFunctionOfParameters(xy);
final Function1D<DoubleMatrix1D, DoubleMatrix1D> g = FIRST_ORDER_DIFF.differentiate(f);
return g.evaluate(params);
}
};
}
}