| /* |
| * 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.analysis.BivariateFunction; |
| import org.apache.commons.statistics.distribution.ContinuousDistribution; |
| import org.apache.commons.statistics.distribution.UniformContinuousDistribution; |
| import org.apache.commons.math4.legacy.exception.DimensionMismatchException; |
| import org.apache.commons.math4.legacy.exception.InsufficientDataException; |
| import org.apache.commons.math4.legacy.exception.NonMonotonicSequenceException; |
| import org.apache.commons.math4.legacy.exception.NullArgumentException; |
| import org.apache.commons.rng.UniformRandomProvider; |
| import org.apache.commons.rng.simple.RandomSource; |
| import org.junit.Assert; |
| import org.junit.Test; |
| |
| /** |
| * Test case for the piecewise bicubic interpolator. |
| */ |
| public final class PiecewiseBicubicSplineInterpolatorTest { |
| /** |
| * Test preconditions. |
| */ |
| @Test |
| public void testPreconditions() { |
| double[] xval = new double[] { 3, 4, 5, 6.5, 7.5 }; |
| double[] yval = new double[] { -4, -3, -1, 2.5, 3.5 }; |
| double[][] zval = new double[xval.length][yval.length]; |
| |
| BivariateGridInterpolator interpolator = new PiecewiseBicubicSplineInterpolator(); |
| |
| try { |
| interpolator.interpolate( null, yval, zval ); |
| Assert.fail( "Failed to detect x null pointer" ); |
| } catch ( NullArgumentException iae ) { |
| // Expected. |
| } |
| |
| try { |
| interpolator.interpolate( xval, null, zval ); |
| Assert.fail( "Failed to detect y null pointer" ); |
| } catch ( NullArgumentException iae ) { |
| // Expected. |
| } |
| |
| try { |
| interpolator.interpolate( xval, yval, null ); |
| Assert.fail( "Failed to detect z null pointer" ); |
| } catch ( NullArgumentException iae ) { |
| // Expected. |
| } |
| |
| try { |
| double xval1[] = { 0.0, 1.0, 2.0, 3.0 }; |
| interpolator.interpolate( xval1, yval, zval ); |
| Assert.fail( "Failed to detect insufficient x data" ); |
| } catch ( InsufficientDataException iae ) { |
| // Expected. |
| } |
| |
| try { |
| double yval1[] = { 0.0, 1.0, 2.0, 3.0 }; |
| interpolator.interpolate( xval, yval1, zval ); |
| Assert.fail( "Failed to detect insufficient y data" ); |
| } catch ( InsufficientDataException iae ) { |
| // Expected. |
| } |
| |
| try { |
| double zval1[][] = new double[4][4]; |
| interpolator.interpolate( xval, yval, zval1 ); |
| Assert.fail( "Failed to detect insufficient z data" ); |
| } catch ( InsufficientDataException iae ) { |
| // Expected. |
| } |
| |
| try { |
| double xval1[] = { 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 }; |
| interpolator.interpolate( xval1, yval, zval ); |
| Assert.fail( "Failed to detect data set array with different sizes." ); |
| } catch ( DimensionMismatchException iae ) { |
| // Expected. |
| } |
| |
| try { |
| double yval1[] = { 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 }; |
| interpolator.interpolate( xval, yval1, zval ); |
| Assert.fail( "Failed to detect data set array with different sizes." ); |
| } catch ( DimensionMismatchException iae ) { |
| // Expected. |
| } |
| |
| // X values not sorted. |
| try { |
| double xval1[] = { 0.0, 1.0, 0.5, 7.0, 3.5 }; |
| interpolator.interpolate( xval1, yval, zval ); |
| Assert.fail( "Failed to detect unsorted x arguments." ); |
| } catch ( NonMonotonicSequenceException iae ) { |
| // Expected. |
| } |
| |
| // Y values not sorted. |
| try { |
| double yval1[] = { 0.0, 1.0, 1.5, 0.0, 3.0 }; |
| interpolator.interpolate( xval, yval1, zval ); |
| Assert.fail( "Failed to detect unsorted y arguments." ); |
| } catch ( NonMonotonicSequenceException iae ) { |
| // Expected. |
| } |
| } |
| |
| /** |
| * Interpolating a plane. |
| * <p> |
| * z = 2 x - 3 y + 5 |
| */ |
| @Test |
| public void testInterpolation1() { |
| final int sz = 21; |
| double[] xval = new double[sz]; |
| double[] yval = new double[sz]; |
| // Coordinate values |
| final double delta = 1d / (sz - 1); |
| for ( int i = 0; i < sz; i++ ){ |
| xval[i] = -1 + 15 * i * delta; |
| yval[i] = -20 + 30 * i * delta; |
| } |
| |
| // Function values |
| BivariateFunction f = new BivariateFunction() { |
| @Override |
| public double value( double x, double y ) { |
| return 2 * x - 3 * y + 5; |
| } |
| }; |
| double[][] zval = new double[xval.length][yval.length]; |
| for ( int i = 0; i < xval.length; i++ ) { |
| for ( int j = 0; j < yval.length; j++ ) { |
| zval[i][j] = f.value(xval[i], yval[j]); |
| } |
| } |
| |
| BivariateGridInterpolator interpolator = new PiecewiseBicubicSplineInterpolator(); |
| BivariateFunction p = interpolator.interpolate(xval, yval, zval); |
| double x; |
| double y; |
| |
| final UniformRandomProvider rng = RandomSource.WELL_19937_C.create(1234567L); |
| final ContinuousDistribution.Sampler distX = UniformContinuousDistribution.of(xval[0], xval[xval.length - 1]).createSampler(rng); |
| final ContinuousDistribution.Sampler distY = UniformContinuousDistribution.of(yval[0], yval[yval.length - 1]).createSampler(rng); |
| |
| final int numSamples = 50; |
| final double tol = 2e-14; |
| for ( int i = 0; i < numSamples; i++ ) { |
| x = distX.sample(); |
| for ( int j = 0; j < numSamples; j++ ) { |
| y = distY.sample(); |
| // System.out.println(x + " " + y + " " + f.value(x, y) + " " + p.value(x, y)); |
| Assert.assertEquals(f.value(x, y), p.value(x, y), tol); |
| } |
| // System.out.println(); |
| } |
| } |
| |
| /** |
| * Interpolating a paraboloid. |
| * <p> |
| * z = 2 x<sup>2</sup> - 3 y<sup>2</sup> + 4 x y - 5 |
| */ |
| @Test |
| public void testInterpolation2() { |
| final int sz = 21; |
| double[] xval = new double[sz]; |
| double[] yval = new double[sz]; |
| // Coordinate values |
| final double delta = 1d / (sz - 1); |
| for ( int i = 0; i < sz; i++ ) { |
| xval[i] = -1 + 15 * i * delta; |
| yval[i] = -20 + 30 * i * delta; |
| } |
| |
| // Function values |
| BivariateFunction f = new BivariateFunction() { |
| @Override |
| public double value( double x, double y ) { |
| return 2 * x * x - 3 * y * y + 4 * x * y - 5; |
| } |
| }; |
| double[][] zval = new double[xval.length][yval.length]; |
| for ( int i = 0; i < xval.length; i++ ) { |
| for ( int j = 0; j < yval.length; j++ ) { |
| zval[i][j] = f.value(xval[i], yval[j]); |
| } |
| } |
| |
| BivariateGridInterpolator interpolator = new PiecewiseBicubicSplineInterpolator(); |
| BivariateFunction p = interpolator.interpolate(xval, yval, zval); |
| double x; |
| double y; |
| |
| final UniformRandomProvider rng = RandomSource.WELL_19937_C.create(1234567L); |
| final ContinuousDistribution.Sampler distX = UniformContinuousDistribution.of(xval[0], xval[xval.length - 1]).createSampler(rng); |
| final ContinuousDistribution.Sampler distY = UniformContinuousDistribution.of(yval[0], yval[yval.length - 1]).createSampler(rng); |
| |
| final int numSamples = 50; |
| final double tol = 5e-13; |
| for ( int i = 0; i < numSamples; i++ ) { |
| x = distX.sample(); |
| for ( int j = 0; j < numSamples; j++ ) { |
| y = distY.sample(); |
| // System.out.println(x + " " + y + " " + f.value(x, y) + " " + p.value(x, y)); |
| Assert.assertEquals(f.value(x, y), p.value(x, y), tol); |
| } |
| // System.out.println(); |
| } |
| } |
| } |