/*
    * 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.fitting.leastsquares;
  
  import org.apache.commons.math4.legacy.TestUtils;
  import org.apache.commons.math4.legacy.analysis.MultivariateMatrixFunction;
  import org.apache.commons.math4.legacy.analysis.MultivariateVectorFunction;
  import org.apache.commons.math4.legacy.exception.MathIllegalStateException;
  import org.apache.commons.math4.legacy.fitting.leastsquares.LeastSquaresProblem.Evaluation;
  import org.apache.commons.math4.legacy.linear.ArrayRealVector;
  import org.apache.commons.math4.legacy.linear.DiagonalMatrix;
  import org.apache.commons.math4.legacy.linear.MatrixUtils;
  import org.apache.commons.math4.legacy.linear.RealMatrix;
  import org.apache.commons.math4.legacy.linear.RealVector;
  import org.apache.commons.math4.legacy.linear.SingularMatrixException;
  import org.apache.commons.math4.core.jdkmath.JdkMath;
  import org.apache.commons.math4.legacy.core.Pair;
  import org.apache.commons.numbers.core.Precision;
  import org.junit.Assert;
  import org.junit.Test;
  
  import java.io.IOException;
  import java.util.Arrays;
  
  /**
   * The only features tested here are utility methods defined
   * in {@link LeastSquaresProblem.Evaluation} that compute the
   * chi-square and parameters standard-deviations.
   */
  public class EvaluationTest {
  
      /**
       * Create a {@link LeastSquaresBuilder} from a {@link StatisticalReferenceDataset}.
       *
       * @param dataset the source data
       * @return a builder for further customization.
       */
      public LeastSquaresBuilder builder(StatisticalReferenceDataset dataset) {
          StatisticalReferenceDataset.LeastSquaresProblem problem
                  = dataset.getLeastSquaresProblem();
          final double[] start = dataset.getParameters();
          final double[] observed = dataset.getData()[1];
          final double[] weights = new double[observed.length];
          Arrays.fill(weights, 1d);
  
          return new LeastSquaresBuilder()
                  .model(problem.getModelFunction(), problem.getModelFunctionJacobian())
                  .target(observed)
                  .weight(new DiagonalMatrix(weights))
                  .start(start);
      }
  
      @Test
      public void testComputeResiduals() {
          //setup
          RealVector point = new ArrayRealVector(2);
          Evaluation evaluation = new LeastSquaresBuilder()
                  .target(new ArrayRealVector(new double[]{3,-1}))
                  .model(new MultivariateJacobianFunction() {
                      @Override
                      public Pair<RealVector, RealMatrix> value(RealVector point) {
                          return new Pair<>(
                                  new ArrayRealVector(new double[]{1, 2}),
                                  MatrixUtils.createRealIdentityMatrix(2)
                          );
                      }
                  })
                  .weight(MatrixUtils.createRealIdentityMatrix(2))
                  .build()
                  .evaluate(point);
  
          //action + verify
          Assert.assertArrayEquals(
                  evaluation.getResiduals().toArray(),
                  new double[]{2, -3},
                  Precision.EPSILON);
      }
  
      @Test
      public void testComputeCovariance() throws IOException {
          //setup
          RealVector point = new ArrayRealVector(2);
          Evaluation evaluation = new LeastSquaresBuilder()
                  .model(new MultivariateJacobianFunction() {
                      @Override
                     public Pair<RealVector, RealMatrix> value(RealVector point) {
                         return new Pair<>(
                                 new ArrayRealVector(2),
                                 MatrixUtils.createRealDiagonalMatrix(new double[]{1, 1e-2})
                         );
                     }
                 })
                 .weight(MatrixUtils.createRealDiagonalMatrix(new double[]{1, 1}))
                 .target(new ArrayRealVector(2))
                 .build()
                 .evaluate(point);
 
         //action
         TestUtils.assertEquals(
                 "covariance",
                 evaluation.getCovariances(JdkMath.nextAfter(1e-4, 0.0)),
                 MatrixUtils.createRealMatrix(new double[][]{{1, 0}, {0, 1e4}}),
                 Precision.EPSILON
         );
 
         //singularity fail
         try {
             evaluation.getCovariances(JdkMath.nextAfter(1e-4, 1.0));
             Assert.fail("Expected Exception");
         } catch (SingularMatrixException e) {
             //expected
         }
     }
 
     @Test
     public void testComputeValueAndJacobian() {
         //setup
         final RealVector point = new ArrayRealVector(new double[]{1, 2});
         Evaluation evaluation = new LeastSquaresBuilder()
                 .weight(new DiagonalMatrix(new double[]{16, 4}))
                 .model(new MultivariateJacobianFunction() {
                     @Override
                     public Pair<RealVector, RealMatrix> value(RealVector actualPoint) {
                         //verify correct values passed in
                         Assert.assertArrayEquals(
                                 point.toArray(), actualPoint.toArray(), Precision.EPSILON);
                         //return values
                         return new Pair<>(
                                 new ArrayRealVector(new double[]{3, 4}),
                                 MatrixUtils.createRealMatrix(new double[][]{{5, 6}, {7, 8}})
                         );
                     }
                 })
                 .target(new double[2])
                 .build()
                 .evaluate(point);
 
         //action
         RealVector residuals = evaluation.getResiduals();
         RealMatrix jacobian = evaluation.getJacobian();
 
         //verify
         Assert.assertArrayEquals(evaluation.getPoint().toArray(), point.toArray(), 0);
         Assert.assertArrayEquals(new double[]{-12, -8}, residuals.toArray(), Precision.EPSILON);
         TestUtils.assertEquals(
                 "jacobian",
                 jacobian,
                 MatrixUtils.createRealMatrix(new double[][]{{20, 24},{14, 16}}),
                 Precision.EPSILON);
     }
 
     @Test
     public void testComputeCost() throws IOException {
         final StatisticalReferenceDataset dataset
             = StatisticalReferenceDatasetFactory.createKirby2();
 
         final LeastSquaresProblem lsp = builder(dataset).build();
 
         final double expected = dataset.getResidualSumOfSquares();
         final double cost = lsp.evaluate(lsp.getStart()).getCost();
         final double actual = cost * cost;
         Assert.assertEquals(dataset.getName(), expected, actual, 1e-11 * expected);
     }
 
     @Test
     public void testComputeRMS() throws IOException {
         final StatisticalReferenceDataset dataset
             = StatisticalReferenceDatasetFactory.createKirby2();
 
         final LeastSquaresProblem lsp = builder(dataset).build();
 
         final double expected = JdkMath.sqrt(dataset.getResidualSumOfSquares() /
                                               dataset.getNumObservations());
         final double actual = lsp.evaluate(lsp.getStart()).getRMS();
         Assert.assertEquals(dataset.getName(), expected, actual, 1e-11 * expected);
     }
 
     @Test
     public void testComputeSigma() throws IOException {
         final StatisticalReferenceDataset dataset
             = StatisticalReferenceDatasetFactory.createKirby2();
 
         final LeastSquaresProblem lsp = builder(dataset).build();
 
         final double[] expected = dataset.getParametersStandardDeviations();
 
         final Evaluation evaluation = lsp.evaluate(lsp.getStart());
         final double cost = evaluation.getCost();
         final RealVector sig = evaluation.getSigma(1e-14);
         final int dof = lsp.getObservationSize() - lsp.getParameterSize();
         for (int i = 0; i < sig.getDimension(); i++) {
             final double actual = JdkMath.sqrt(cost * cost / dof) * sig.getEntry(i);
             Assert.assertEquals(dataset.getName() + ", parameter #" + i,
                                 expected[i], actual, 1e-6 * expected[i]);
         }
     }
 
     @Test
     public void testEvaluateCopiesPoint() throws IOException {
         //setup
         StatisticalReferenceDataset dataset
                 = StatisticalReferenceDatasetFactory.createKirby2();
         LeastSquaresProblem lsp = builder(dataset).build();
         RealVector point = new ArrayRealVector(lsp.getParameterSize());
 
         //action
         Evaluation evaluation = lsp.evaluate(point);
 
         //verify
         Assert.assertNotSame(point, evaluation.getPoint());
         point.setEntry(0, 1);
         Assert.assertEquals(evaluation.getPoint().getEntry(0), 0, 0);
     }
 
     @Test
     public void testLazyEvaluation() {
         final RealVector dummy = new ArrayRealVector(new double[] { 0 });
 
         final LeastSquaresProblem p
             = LeastSquaresFactory.create(LeastSquaresFactory.model(dummyModel(), dummyJacobian()),
                                          dummy, dummy, null, null, 0, 0, true, null);
 
         // Should not throw because actual evaluation is deferred.
         final Evaluation eval = p.evaluate(dummy);
 
         try {
             eval.getResiduals();
             Assert.fail("Exception expected");
         } catch (RuntimeException e) {
             // Expecting exception.
             Assert.assertEquals("dummyModel", e.getMessage());
         }
 
         try {
             eval.getJacobian();
             Assert.fail("Exception expected");
         } catch (RuntimeException e) {
             // Expecting exception.
             Assert.assertEquals("dummyJacobian", e.getMessage());
         }
     }
 
     // MATH-1151
     @Test
     public void testLazyEvaluationPrecondition() {
         final RealVector dummy = new ArrayRealVector(new double[] { 0 });
 
         // "ValueAndJacobianFunction" is required but we implement only
         // "MultivariateJacobianFunction".
         final MultivariateJacobianFunction m1 = new MultivariateJacobianFunction() {
                 @Override
                 public Pair<RealVector, RealMatrix> value(RealVector notUsed) {
                     return new Pair<>(null, null);
                 }
             };
 
         try {
             // Should throw.
             LeastSquaresFactory.create(m1, dummy, dummy, null, null, 0, 0, true, null);
             Assert.fail("Expecting MathIllegalStateException");
         } catch (MathIllegalStateException e) {
             // Expected.
         }
 
         final MultivariateJacobianFunction m2 = new ValueAndJacobianFunction() {
                 @Override
                 public Pair<RealVector, RealMatrix> value(RealVector notUsed) {
                     return new Pair<>(null, null);
                 }
                 @Override
                 public RealVector computeValue(final double[] params) {
                     return null;
                 }
                 @Override
                 public RealMatrix computeJacobian(final double[] params) {
                     return null;
                 }
             };
 
         // Should pass.
         LeastSquaresFactory.create(m2, dummy, dummy, null, null, 0, 0, true, null);
     }
 
     @Test
     public void testDirectEvaluation() {
         final RealVector dummy = new ArrayRealVector(new double[] { 0 });
 
         final LeastSquaresProblem p
             = LeastSquaresFactory.create(LeastSquaresFactory.model(dummyModel(), dummyJacobian()),
                                          dummy, dummy, null, null, 0, 0, false, null);
 
         try {
             // Should throw.
             p.evaluate(dummy);
             Assert.fail("Exception expected");
         } catch (RuntimeException e) {
             // Expecting exception.
             // Whether it is model or Jacobian that caused it is not significant.
             final String msg = e.getMessage();
             Assert.assertTrue(msg.equals("dummyModel") ||
                               msg.equals("dummyJacobian"));
         }
     }
 
     /** Used for testing direct vs lazy evaluation. */
     private MultivariateVectorFunction dummyModel() {
         return new MultivariateVectorFunction() {
             @Override
             public double[] value(double[] p) {
                 throw new RuntimeException("dummyModel");
             }
         };
     }
 
     /** Used for testing direct vs lazy evaluation. */
     private MultivariateMatrixFunction dummyJacobian() {
         return new MultivariateMatrixFunction() {
             @Override
             public double[][] value(double[] p) {
                 throw new RuntimeException("dummyJacobian");
             }
         };
     }
 }