| /* |
| * Licensed to the Apache Software Foundation (ASF) under one or more |
| * contributor license agreements. See the NOTICE file distributed with |
| * this work for additional information regarding copyright ownership. |
| * The ASF licenses this file to You 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 org.apache.commons.math4.legacy.analysis.interpolation; |
| |
| import org.apache.commons.math4.legacy.TestUtils; |
| import org.apache.commons.math4.legacy.analysis.UnivariateFunction; |
| import org.apache.commons.math4.legacy.analysis.polynomials.PolynomialFunction; |
| import org.apache.commons.math4.legacy.analysis.polynomials.PolynomialSplineFunction; |
| import org.apache.commons.math4.legacy.exception.DimensionMismatchException; |
| import org.apache.commons.math4.legacy.exception.NonMonotonicSequenceException; |
| import org.apache.commons.math4.legacy.exception.NumberIsTooSmallException; |
| import org.apache.commons.math4.core.jdkmath.JdkMath; |
| import org.junit.Assert; |
| import org.junit.Test; |
| |
| /** |
| * Test the SplineInterpolator. |
| * |
| */ |
| public class SplineInterpolatorTest { |
| |
| /** error tolerance for spline interpolator value at knot points */ |
| protected double knotTolerance = 1E-14; |
| |
| /** error tolerance for interpolating polynomial coefficients */ |
| protected double coefficientTolerance = 1E-14; |
| |
| /** error tolerance for interpolated values -- high value is from sin test */ |
| protected double interpolationTolerance = 1E-14; |
| |
| @Test |
| public void testInterpolateLinearDegenerateTwoSegment() { |
| double tolerance = 1e-15; |
| double x[] = { 0.0, 0.5, 1.0 }; |
| double y[] = { 0.0, 0.5, 1.0 }; |
| UnivariateInterpolator i = new SplineInterpolator(); |
| UnivariateFunction f = i.interpolate(x, y); |
| verifyInterpolation(f, x, y); |
| verifyConsistency((PolynomialSplineFunction) f, x); |
| |
| // Verify coefficients using analytical values |
| PolynomialFunction polynomials[] = ((PolynomialSplineFunction) f).getPolynomials(); |
| double target[] = {y[0], 1d}; |
| TestUtils.assertEquals(polynomials[0].getCoefficients(), target, coefficientTolerance); |
| target = new double[]{y[1], 1d}; |
| TestUtils.assertEquals(polynomials[1].getCoefficients(), target, coefficientTolerance); |
| |
| // Check interpolation |
| Assert.assertEquals(0.0,f.value(0.0), tolerance); |
| Assert.assertEquals(0.4,f.value(0.4), tolerance); |
| Assert.assertEquals(1.0,f.value(1.0), tolerance); |
| } |
| |
| @Test |
| public void testInterpolateLinearDegenerateThreeSegment() { |
| double tolerance = 1e-15; |
| double x[] = { 0.0, 0.5, 1.0, 1.5 }; |
| double y[] = { 0.0, 0.5, 1.0, 1.5 }; |
| UnivariateInterpolator i = new SplineInterpolator(); |
| UnivariateFunction f = i.interpolate(x, y); |
| verifyInterpolation(f, x, y); |
| |
| // Verify coefficients using analytical values |
| PolynomialFunction polynomials[] = ((PolynomialSplineFunction) f).getPolynomials(); |
| double target[] = {y[0], 1d}; |
| TestUtils.assertEquals(polynomials[0].getCoefficients(), target, coefficientTolerance); |
| target = new double[]{y[1], 1d}; |
| TestUtils.assertEquals(polynomials[1].getCoefficients(), target, coefficientTolerance); |
| target = new double[]{y[2], 1d}; |
| TestUtils.assertEquals(polynomials[2].getCoefficients(), target, coefficientTolerance); |
| |
| // Check interpolation |
| Assert.assertEquals(0,f.value(0), tolerance); |
| Assert.assertEquals(1.4,f.value(1.4), tolerance); |
| Assert.assertEquals(1.5,f.value(1.5), tolerance); |
| } |
| |
| @Test |
| public void testInterpolateLinear() { |
| double x[] = { 0.0, 0.5, 1.0 }; |
| double y[] = { 0.0, 0.5, 0.0 }; |
| UnivariateInterpolator i = new SplineInterpolator(); |
| UnivariateFunction f = i.interpolate(x, y); |
| verifyInterpolation(f, x, y); |
| verifyConsistency((PolynomialSplineFunction) f, x); |
| |
| // Verify coefficients using analytical values |
| PolynomialFunction polynomials[] = ((PolynomialSplineFunction) f).getPolynomials(); |
| double target[] = {y[0], 1.5d, 0d, -2d}; |
| TestUtils.assertEquals(polynomials[0].getCoefficients(), target, coefficientTolerance); |
| target = new double[]{y[1], 0d, -3d, 2d}; |
| TestUtils.assertEquals(polynomials[1].getCoefficients(), target, coefficientTolerance); |
| } |
| |
| @Test |
| public void testInterpolateSin() { |
| double sineCoefficientTolerance = 1e-6; |
| double sineInterpolationTolerance = 0.0043; |
| double x[] = |
| { |
| 0.0, |
| JdkMath.PI / 6d, |
| JdkMath.PI / 2d, |
| 5d * JdkMath.PI / 6d, |
| JdkMath.PI, |
| 7d * JdkMath.PI / 6d, |
| 3d * JdkMath.PI / 2d, |
| 11d * JdkMath.PI / 6d, |
| 2.d * JdkMath.PI }; |
| double y[] = { 0d, 0.5d, 1d, 0.5d, 0d, -0.5d, -1d, -0.5d, 0d }; |
| UnivariateInterpolator i = new SplineInterpolator(); |
| UnivariateFunction f = i.interpolate(x, y); |
| verifyInterpolation(f, x, y); |
| verifyConsistency((PolynomialSplineFunction) f, x); |
| |
| /* Check coefficients against values computed using R (version 1.8.1, Red Hat Linux 9) |
| * |
| * To replicate in R: |
| * x[1] <- 0 |
| * x[2] <- pi / 6, etc, same for y[] (could use y <- scan() for y values) |
| * g <- splinefun(x, y, "natural") |
| * splinecoef <- eval(expression(z), envir = environment(g)) |
| * print(splinecoef) |
| */ |
| PolynomialFunction polynomials[] = ((PolynomialSplineFunction) f).getPolynomials(); |
| double target[] = {y[0], 1.002676d, 0d, -0.17415829d}; |
| TestUtils.assertEquals(polynomials[0].getCoefficients(), target, sineCoefficientTolerance); |
| target = new double[]{y[1], 8.594367e-01, -2.735672e-01, -0.08707914}; |
| TestUtils.assertEquals(polynomials[1].getCoefficients(), target, sineCoefficientTolerance); |
| target = new double[]{y[2], 1.471804e-17,-5.471344e-01, 0.08707914}; |
| TestUtils.assertEquals(polynomials[2].getCoefficients(), target, sineCoefficientTolerance); |
| target = new double[]{y[3], -8.594367e-01, -2.735672e-01, 0.17415829}; |
| TestUtils.assertEquals(polynomials[3].getCoefficients(), target, sineCoefficientTolerance); |
| target = new double[]{y[4], -1.002676, 6.548562e-17, 0.17415829}; |
| TestUtils.assertEquals(polynomials[4].getCoefficients(), target, sineCoefficientTolerance); |
| target = new double[]{y[5], -8.594367e-01, 2.735672e-01, 0.08707914}; |
| TestUtils.assertEquals(polynomials[5].getCoefficients(), target, sineCoefficientTolerance); |
| target = new double[]{y[6], 3.466465e-16, 5.471344e-01, -0.08707914}; |
| TestUtils.assertEquals(polynomials[6].getCoefficients(), target, sineCoefficientTolerance); |
| target = new double[]{y[7], 8.594367e-01, 2.735672e-01, -0.17415829}; |
| TestUtils.assertEquals(polynomials[7].getCoefficients(), target, sineCoefficientTolerance); |
| |
| //Check interpolation |
| Assert.assertEquals(JdkMath.sqrt(2d) / 2d,f.value(JdkMath.PI/4d),sineInterpolationTolerance); |
| Assert.assertEquals(JdkMath.sqrt(2d) / 2d,f.value(3d*JdkMath.PI/4d),sineInterpolationTolerance); |
| } |
| |
| @Test |
| public void testIllegalArguments() { |
| // Data set arrays of different size. |
| UnivariateInterpolator i = new SplineInterpolator(); |
| try { |
| double xval[] = { 0.0, 1.0 }; |
| double yval[] = { 0.0, 1.0, 2.0 }; |
| i.interpolate(xval, yval); |
| Assert.fail("Failed to detect data set array with different sizes."); |
| } catch (DimensionMismatchException iae) { |
| // Expected. |
| } |
| // X values not sorted. |
| try { |
| double xval[] = { 0.0, 1.0, 0.5 }; |
| double yval[] = { 0.0, 1.0, 2.0 }; |
| i.interpolate(xval, yval); |
| Assert.fail("Failed to detect unsorted arguments."); |
| } catch (NonMonotonicSequenceException iae) { |
| // Expected. |
| } |
| // Not enough data to interpolate. |
| try { |
| double xval[] = { 0.0, 1.0 }; |
| double yval[] = { 0.0, 1.0 }; |
| i.interpolate(xval, yval); |
| Assert.fail("Failed to detect unsorted arguments."); |
| } catch (NumberIsTooSmallException iae) { |
| // Expected. |
| } |
| } |
| |
| /** |
| * verifies that f(x[i]) = y[i] for i = 0..n-1 where n is common length. |
| */ |
| protected void verifyInterpolation(UnivariateFunction f, double x[], double y[]) { |
| for (int i = 0; i < x.length; i++) { |
| Assert.assertEquals(f.value(x[i]), y[i], knotTolerance); |
| } |
| } |
| |
| /** |
| * Verifies that interpolating polynomials satisfy consistency requirement: |
| * adjacent polynomials must agree through two derivatives at knot points |
| */ |
| protected void verifyConsistency(PolynomialSplineFunction f, double x[]) { |
| PolynomialFunction polynomials[] = f.getPolynomials(); |
| for (int i = 1; i < x.length - 2; i++) { |
| // evaluate polynomials and derivatives at x[i + 1] |
| Assert.assertEquals(polynomials[i].value(x[i +1] - x[i]), polynomials[i + 1].value(0), 0.1); |
| Assert.assertEquals(polynomials[i].polynomialDerivative().value(x[i +1] - x[i]), |
| polynomials[i + 1].polynomialDerivative().value(0), 0.5); |
| Assert.assertEquals(polynomials[i].polynomialDerivative().polynomialDerivative().value(x[i +1] - x[i]), |
| polynomials[i + 1].polynomialDerivative().polynomialDerivative().value(0), 0.5); |
| } |
| } |
| } |