/*
    * 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.differentiation;
  
  import org.apache.commons.math4.legacy.TestUtils;
  import org.apache.commons.math4.legacy.exception.DimensionMismatchException;
  import org.apache.commons.math4.legacy.exception.MathIllegalArgumentException;
  import org.apache.commons.math4.core.jdkmath.JdkMath;
  import org.junit.Test;
  
  
  /**
   * Test for class {@link GradientFunction}.
   */
  public class GradientFunctionTest {
  
      @Test
      public void test2DDistance() {
          EuclideanDistance f = new EuclideanDistance();
          GradientFunction g = new GradientFunction(f);
          for (double x = -10; x < 10; x += 0.5) {
              for (double y = -10; y < 10; y += 0.5) {
                  double[] point = new double[] { x, y };
                  TestUtils.assertEquals(f.gradient(point), g.value(point), 1.0e-15);
              }
          }
      }
  
      @Test
      public void test3DDistance() {
          EuclideanDistance f = new EuclideanDistance();
          GradientFunction g = new GradientFunction(f);
          for (double x = -10; x < 10; x += 0.5) {
              for (double y = -10; y < 10; y += 0.5) {
                  for (double z = -10; z < 10; z += 0.5) {
                      double[] point = new double[] { x, y, z };
                      TestUtils.assertEquals(f.gradient(point), g.value(point), 1.0e-15);
                  }
              }
          }
      }
  
      private static final class EuclideanDistance implements MultivariateDifferentiableFunction {
  
          @Override
          public double value(double[] point) {
              double d2 = 0;
              for (double x : point) {
                  d2 += x * x;
              }
              return JdkMath.sqrt(d2);
          }
  
          @Override
          public DerivativeStructure value(DerivativeStructure[] point)
              throws DimensionMismatchException, MathIllegalArgumentException {
              DerivativeStructure d2 = point[0].getField().getZero();
              for (DerivativeStructure x : point) {
                  d2 = d2.add(x.multiply(x));
              }
              return d2.sqrt();
          }
  
          public double[] gradient(double[] point) {
              double[] gradient = new double[point.length];
              double d = value(point);
              for (int i = 0; i < point.length; ++i) {
                  gradient[i] = point[i] / d;
              }
              return gradient;
          }
      }
  }