/*
    * 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.util;
  
  import org.junit.Test;
  import org.junit.Assert;
  
  import static org.junit.jupiter.api.Assertions.assertThrows;
  
  /**
   * Tests for {@link QuasiSigmoidDecayFunction} class
   */
  public class QuasiSigmoidDecayFunctionTest {
  
      @Test
      public void testPrecondition1() {
          assertThrows(IllegalArgumentException.class, () ->
                  new QuasiSigmoidDecayFunction(0d, -1d, 2));
      }
  
      @Test
      public void testPrecondition3() {
          assertThrows(IllegalArgumentException.class, () ->
                  new QuasiSigmoidDecayFunction(1d, 0d, 100));
      }
  
      @Test
      public void testPrecondition4() {
          assertThrows(IllegalArgumentException.class, () ->
                  new QuasiSigmoidDecayFunction(1d, -1d, 0));
      }
  
      @Test
      public void testTrivial() {
          final int n = 65;
          final double init = 4;
          final double slope = -1e-1;
          final QuasiSigmoidDecayFunction f = new QuasiSigmoidDecayFunction(init, slope, n);
  
          Assert.assertEquals(init, f.applyAsDouble(0), 0d);
          // Very approximate derivative.
          Assert.assertEquals(slope, f.applyAsDouble(n + 1) - f.applyAsDouble(n), 1e-4);
          Assert.assertEquals(0, f.applyAsDouble(Long.MAX_VALUE), 0d);
      }
  
  }