/*
    * 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.neuralnet.sofm;
  
  import org.junit.Test;
  import org.junit.Assert;
  
  import static org.junit.jupiter.api.Assertions.assertThrows;
  
  /**
   * Tests for {@link LearningFactorFunctionFactory} class.
   */
  public class LearningFactorFunctionFactoryTest {
  
      @Test
      public void testExponentialDecayPrecondition0() {
          assertThrows(IllegalArgumentException.class, () ->
                  LearningFactorFunctionFactory.exponentialDecay(0d, 0d, 2));
      }
  
      @Test
      public void testExponentialDecayPrecondition1() {
          assertThrows(IllegalArgumentException.class, () ->
                  LearningFactorFunctionFactory.exponentialDecay(1 + 1e-10, 0d, 2));
      }
  
      @Test
      public void testExponentialDecayPrecondition2() {
          assertThrows(IllegalArgumentException.class, () ->
                  LearningFactorFunctionFactory.exponentialDecay(1d, 0d, 2));
      }
  
      @Test
      public void testExponentialDecayPrecondition3() {
          assertThrows(IllegalArgumentException.class, () ->
                  LearningFactorFunctionFactory.exponentialDecay(1d, 1d, 100));
      }
  
      @Test
      public void testExponentialDecayPrecondition4() {
          assertThrows(IllegalArgumentException.class, () ->
                  LearningFactorFunctionFactory.exponentialDecay(1d, 0.2, 0));
      }
  
      @Test
      public void testExponentialDecayTrivial() {
          final int n = 65;
          final double init = 0.5;
          final double valueAtN = 0.1;
          final LearningFactorFunction f
              = LearningFactorFunctionFactory.exponentialDecay(init, valueAtN, n);
  
          Assert.assertEquals(init, f.value(0), 0d);
          Assert.assertEquals(valueAtN, f.value(n), 0d);
          Assert.assertEquals(0, f.value(Long.MAX_VALUE), 0d);
      }
  
      @Test
      public void testQuasiSigmoidDecayPrecondition0() {
          assertThrows(IllegalArgumentException.class, () ->
                  LearningFactorFunctionFactory.quasiSigmoidDecay(0d, -1d, 2));
      }
  
      @Test
      public void testQuasiSigmoidDecayPrecondition1() {
          assertThrows(IllegalArgumentException.class, () ->
                  LearningFactorFunctionFactory.quasiSigmoidDecay(1 + 1e-10, -1d, 2));
      }
  
      @Test
      public void testQuasiSigmoidDecayPrecondition3() {
          assertThrows(IllegalArgumentException.class, () ->
                  LearningFactorFunctionFactory.quasiSigmoidDecay(1d, 0d, 100));
      }
  
      @Test
      public void testQuasiSigmoidDecayPrecondition4() {
          assertThrows(IllegalArgumentException.class, () ->
                  LearningFactorFunctionFactory.quasiSigmoidDecay(1d, -1d, 0));
      }
  
      @Test
      public void testQuasiSigmoidDecayTrivial() {
          final int n = 65;
         final double init = 0.5;
         final double slope = -1e-1;
         final LearningFactorFunction f
             = LearningFactorFunctionFactory.quasiSigmoidDecay(init, slope, n);
 
         Assert.assertEquals(init, f.value(0), 0d);
         // Very approximate derivative.
         Assert.assertEquals(slope, f.value(n) - f.value(n - 1), 1e-2);
         Assert.assertEquals(0, f.value(Long.MAX_VALUE), 0d);
     }
 
 }