001/* 002 * Licensed to the Apache Software Foundation (ASF) under one or more 003 * contributor license agreements. See the NOTICE file distributed with 004 * this work for additional information regarding copyright ownership. 005 * The ASF licenses this file to You under the Apache License, Version 2.0 006 * (the "License"); you may not use this file except in compliance with 007 * the License. You may obtain a copy of the License at 008 * 009 * http://www.apache.org/licenses/LICENSE-2.0 010 * 011 * Unless required by applicable law or agreed to in writing, software 012 * distributed under the License is distributed on an "AS IS" BASIS, 013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 014 * See the License for the specific language governing permissions and 015 * limitations under the License. 016 */ 017package org.apache.commons.math4.legacy.analysis.interpolation; 018 019import org.apache.commons.math4.legacy.analysis.polynomials.PolynomialFunction; 020import org.apache.commons.math4.legacy.analysis.polynomials.PolynomialSplineFunction; 021import org.apache.commons.math4.legacy.exception.DimensionMismatchException; 022import org.apache.commons.math4.legacy.exception.NumberIsTooSmallException; 023import org.apache.commons.math4.legacy.exception.util.LocalizedFormats; 024import org.apache.commons.math4.legacy.core.MathArrays; 025 026/** 027 * Computes a natural (also known as "free", "unclamped") cubic spline interpolation for the data set. 028 * <p> 029 * The {@link #interpolate(double[], double[])} method returns a {@link PolynomialSplineFunction} 030 * consisting of n cubic polynomials, defined over the subintervals determined by the x values, 031 * {@code x[0] < x[i] ... < x[n].} The x values are referred to as "knot points."</p> 032 * <p> 033 * The value of the PolynomialSplineFunction at a point x that is greater than or equal to the smallest 034 * knot point and strictly less than the largest knot point is computed by finding the subinterval to which 035 * x belongs and computing the value of the corresponding polynomial at <code>x - x[i] </code> where 036 * <code>i</code> is the index of the subinterval. See {@link PolynomialSplineFunction} for more details. 037 * </p> 038 * <p> 039 * The interpolating polynomials satisfy: <ol> 040 * <li>The value of the PolynomialSplineFunction at each of the input x values equals the 041 * corresponding y value.</li> 042 * <li>Adjacent polynomials are equal through two derivatives at the knot points (i.e., adjacent polynomials 043 * "match up" at the knot points, as do their first and second derivatives).</li> 044 * </ol> 045 * <p> 046 * The cubic spline interpolation algorithm implemented is as described in R.L. Burden, J.D. Faires, 047 * <u>Numerical Analysis</u>, 4th Ed., 1989, PWS-Kent, ISBN 0-53491-585-X, pp 126-131. 048 * </p> 049 * 050 */ 051public class SplineInterpolator implements UnivariateInterpolator { 052 /** 053 * Computes an interpolating function for the data set. 054 * @param x the arguments for the interpolation points 055 * @param y the values for the interpolation points 056 * @return a function which interpolates the data set 057 * @throws DimensionMismatchException if {@code x} and {@code y} 058 * have different sizes. 059 * @throws NumberIsTooSmallException if the size of {@code x < 3}. 060 * @throws org.apache.commons.math4.legacy.exception.NonMonotonicSequenceException 061 * if {@code x} is not sorted in strict increasing order. 062 */ 063 @Override 064 public PolynomialSplineFunction interpolate(double[] x, double[] y) { 065 if (x.length != y.length) { 066 throw new DimensionMismatchException(x.length, y.length); 067 } 068 069 if (x.length < 3) { 070 throw new NumberIsTooSmallException(LocalizedFormats.NUMBER_OF_POINTS, 071 x.length, 3, true); 072 } 073 074 // Number of intervals. The number of data points is n + 1. 075 final int n = x.length - 1; 076 077 MathArrays.checkOrder(x); 078 079 // Differences between knot points 080 final double[] h = new double[n]; 081 for (int i = 0; i < n; i++) { 082 h[i] = x[i + 1] - x[i]; 083 } 084 085 final double[] mu = new double[n]; 086 final double[] z = new double[n + 1]; 087 double g = 0; 088 int indexM1 = 0; 089 int index = 1; 090 int indexP1 = 2; 091 while (index < n) { 092 final double xIp1 = x[indexP1]; 093 final double xIm1 = x[indexM1]; 094 final double hIm1 = h[indexM1]; 095 final double hI = h[index]; 096 g = 2d * (xIp1 - xIm1) - hIm1 * mu[indexM1]; 097 mu[index] = hI / g; 098 z[index] = (3d * (y[indexP1] * hIm1 - y[index] * (xIp1 - xIm1)+ y[indexM1] * hI) / 099 (hIm1 * hI) - hIm1 * z[indexM1]) / g; 100 101 indexM1 = index; 102 index = indexP1; 103 indexP1 = indexP1 + 1; 104 } 105 106 // cubic spline coefficients -- b is linear, c quadratic, d is cubic (original y's are constants) 107 final double[] b = new double[n]; 108 final double[] c = new double[n + 1]; 109 final double[] d = new double[n]; 110 111 for (int j = n - 1; j >= 0; j--) { 112 final double cJp1 = c[j + 1]; 113 final double cJ = z[j] - mu[j] * cJp1; 114 final double hJ = h[j]; 115 b[j] = (y[j + 1] - y[j]) / hJ - hJ * (cJp1 + 2d * cJ) / 3d; 116 c[j] = cJ; 117 d[j] = (cJp1 - cJ) / (3d * hJ); 118 } 119 120 final PolynomialFunction[] polynomials = new PolynomialFunction[n]; 121 final double[] coefficients = new double[4]; 122 for (int i = 0; i < n; i++) { 123 coefficients[0] = y[i]; 124 coefficients[1] = b[i]; 125 coefficients[2] = c[i]; 126 coefficients[3] = d[i]; 127 polynomials[i] = new PolynomialFunction(coefficients); 128 } 129 130 return new PolynomialSplineFunction(x, polynomials); 131 } 132}