/**
* Copyright (C) 2013 - present by OpenGamma Inc. and the OpenGamma group of companies
*
* Please see distribution for license.
*/
package com.opengamma.analytics.math.interpolation;
import org.apache.commons.lang.Validate;
import com.opengamma.analytics.math.function.PiecewisePolynomialWithSensitivityFunction1D;
import com.opengamma.analytics.math.interpolation.data.ArrayInterpolator1DDataBundle;
import com.opengamma.analytics.math.interpolation.data.Interpolator1DDataBundle;
import com.opengamma.analytics.math.interpolation.data.Interpolator1DLogPiecewisePoynomialDataBundle;
import com.opengamma.analytics.math.matrix.DoubleMatrix1D;
import com.opengamma.util.ArgumentChecker;
/**
* Find a interpolant F(x) = exp( f(x) ) where f(x) is a Natural cubic spline with Monotonicity cubic fileter.
*
* The natural cubic spline is determined by {@link LogNaturalSplineHelper}, where the tridiagonal algorithm is used to solve a linear system.
* Since {@link PiecewisePolynomialResultsWithSensitivity} in {@link Interpolator1DLogPiecewisePoynomialDataBundle} contains information on f(x) (NOT F(x)),
* computation done by {@link PiecewisePolynomialWithSensitivityFunction1D} MUST be exponentiated.
*/
public class LogNaturalCubicMonotonicityPreservingInterpolator1D extends PiecewisePolynomialInterpolator1D {
/** Serialization version */
private static final long serialVersionUID = 1L;
private static final PiecewisePolynomialWithSensitivityFunction1D FUNC = new PiecewisePolynomialWithSensitivityFunction1D();
/**
*
*/
public LogNaturalCubicMonotonicityPreservingInterpolator1D() {
super(new MonotonicityPreservingCubicSplineInterpolator(new LogNaturalSplineHelper()));
}
@Override
public Double interpolate(final Interpolator1DDataBundle data, final Double value) {
Validate.notNull(value, "value");
Validate.notNull(data, "data bundle");
Validate.isTrue(data instanceof Interpolator1DLogPiecewisePoynomialDataBundle);
final Interpolator1DLogPiecewisePoynomialDataBundle polyData = (Interpolator1DLogPiecewisePoynomialDataBundle) data;
final DoubleMatrix1D res = FUNC.evaluate(polyData.getPiecewisePolynomialResultsWithSensitivity(), value);
return Math.exp(res.getEntry(0));
}
@Override
public double firstDerivative(final Interpolator1DDataBundle data, final Double value) {
Validate.notNull(value, "value");
Validate.notNull(data, "data bundle");
Validate.isTrue(data instanceof Interpolator1DLogPiecewisePoynomialDataBundle);
final Interpolator1DLogPiecewisePoynomialDataBundle polyData = (Interpolator1DLogPiecewisePoynomialDataBundle) data;
final DoubleMatrix1D resValue = FUNC.evaluate(polyData.getPiecewisePolynomialResultsWithSensitivity(), value);
final DoubleMatrix1D resDerivative = FUNC.differentiate(polyData.getPiecewisePolynomialResultsWithSensitivity(), value);
return Math.exp(resValue.getEntry(0)) * resDerivative.getEntry(0);
}
@Override
public double[] getNodeSensitivitiesForValue(final Interpolator1DDataBundle data, final Double value) {
Validate.notNull(value, "value");
Validate.notNull(data, "data bundle");
Validate.isTrue(data instanceof Interpolator1DLogPiecewisePoynomialDataBundle);
final Interpolator1DLogPiecewisePoynomialDataBundle polyData = (Interpolator1DLogPiecewisePoynomialDataBundle) data;
final double[] resSense = FUNC.nodeSensitivity(polyData.getPiecewisePolynomialResultsWithSensitivity(), value).getData();
final double resValue = Math.exp(FUNC.evaluate(polyData.getPiecewisePolynomialResultsWithSensitivity(), value).getEntry(0));
final double[] knotValues = data.getValues();
final int nKnots = knotValues.length;
final double[] res = new double[nKnots];
for (int i = 0; i < nKnots; ++i) {
res[i] = resSense[i] * resValue / knotValues[i];
}
return res;
}
@Override
public Interpolator1DDataBundle getDataBundle(final double[] x, final double[] y) {
Validate.notNull(y, "y");
final int nData = y.length;
final double[] logY = new double[nData];
for (int i = 0; i < nData; ++i) {
ArgumentChecker.isTrue(y[i] > 0., "y should be positive");
logY[i] = Math.log(y[i]);
}
return new Interpolator1DLogPiecewisePoynomialDataBundle(new ArrayInterpolator1DDataBundle(x, logY, false), new MonotonicityPreservingCubicSplineInterpolator(new LogNaturalSplineHelper()));
}
@Override
public Interpolator1DDataBundle getDataBundleFromSortedArrays(final double[] x, final double[] y) {
Validate.notNull(y, "y");
final int nData = y.length;
final double[] logY = new double[nData];
for (int i = 0; i < nData; ++i) {
ArgumentChecker.isTrue(y[i] > 0., "y should be positive");
logY[i] = Math.log(y[i]);
}
return new Interpolator1DLogPiecewisePoynomialDataBundle(new ArrayInterpolator1DDataBundle(x, logY, true), new MonotonicityPreservingCubicSplineInterpolator(new LogNaturalSplineHelper()));
}
}