BachelorarbeitCozmo/Assets/UnitySDK/Assets/ML-Agents/Editor/Tests/MultinomialTest.cs

using System;
using NUnit.Framework;
using UnityEngine;
using MLAgents.InferenceBrain;
using MLAgents.InferenceBrain.Utils;

namespace MLAgents.Tests
{
    public class MultinomialTest
    {
        [Test]
        public void TestEvalP()
        {
            Multinomial m = new Multinomial(2018);

            Tensor src = new Tensor
            {
                Data = new float[1, 3] {{0.1f, 0.2f, 0.7f}},
                ValueType = Tensor.TensorType.FloatingPoint
            };

            Tensor dst = new Tensor
            {
                Data = new float[1, 3],
                ValueType = Tensor.TensorType.FloatingPoint
            };

            m.Eval(src, dst);

            float[] reference = {2, 2, 1};
            int i = 0;
            foreach (var f in dst.Data)
            {
                Assert.AreEqual(reference[i], f);
                ++i;
            }
        }

        [Test]
        public void TestEvalLogits()
        {
            Multinomial m = new Multinomial(2018);

            Tensor src = new Tensor
            {
                Data = new float[1, 3] {{Mathf.Log(0.1f) - 50, Mathf.Log(0.2f) - 50, Mathf.Log(0.7f) - 50}},
                ValueType = Tensor.TensorType.FloatingPoint
            };

            Tensor dst = new Tensor
            {
                Data = new float[1, 3],
                ValueType = Tensor.TensorType.FloatingPoint
            };

            m.Eval(src, dst);

            float[] reference = {2, 2, 2};
            int i = 0;
            foreach (var f in dst.Data)
            {
                Assert.AreEqual(reference[i], f);
                ++i;
            }
        }

        [Test]
        public void TestEvalBatching()
        {
            Multinomial m = new Multinomial(2018);

            Tensor src = new Tensor
            {
                Data = new float[2, 3]
                {
                    {Mathf.Log(0.1f) - 50, Mathf.Log(0.2f) - 50, Mathf.Log(0.7f) - 50},
                    {Mathf.Log(0.3f) - 25, Mathf.Log(0.4f) - 25, Mathf.Log(0.3f) - 25},
                    
                },
                ValueType = Tensor.TensorType.FloatingPoint
            };

            Tensor dst = new Tensor
            {
                Data = new float[2, 3],
                ValueType = Tensor.TensorType.FloatingPoint
            };

            m.Eval(src, dst);

            float[] reference = {2, 2, 2, 0, 1, 0};
            int i = 0;
            foreach (var f in dst.Data)
            {
                Assert.AreEqual(reference[i], f);
                ++i;
            }
        }
        
        [Test]
        public void TestSrcInt()
        {
            Multinomial m = new Multinomial(2018);

            Tensor src = new Tensor
            {
                ValueType = Tensor.TensorType.Integer
            };

            Assert.Throws<NotImplementedException>(() => m.Eval(src, null));
        }
        
        [Test]
        public void TestDstInt()
        {
            Multinomial m = new Multinomial(2018);

            Tensor src = new Tensor
            {
                ValueType = Tensor.TensorType.FloatingPoint
            };
            Tensor dst = new Tensor
            {
                ValueType = Tensor.TensorType.Integer
            };

            Assert.Throws<ArgumentException>(() => m.Eval(src, dst));
        }
        
        [Test]
        public void TestSrcDataNull()
        {
            Multinomial m = new Multinomial(2018);
            
            Tensor src = new Tensor
            {
                ValueType = Tensor.TensorType.FloatingPoint
            };
            Tensor dst = new Tensor
            {
                ValueType = Tensor.TensorType.FloatingPoint
            };

            Assert.Throws<ArgumentNullException>(() => m.Eval(src, dst));
        }

        [Test]
        public void TestDstDataNull()
        {
            Multinomial m = new Multinomial(2018);
            
            Tensor src = new Tensor
            {
                ValueType = Tensor.TensorType.FloatingPoint,
                Data = new float[1]
            };
            Tensor dst = new Tensor
            {
                ValueType = Tensor.TensorType.FloatingPoint
            };

            Assert.Throws<ArgumentNullException>(() => m.Eval(src, dst));
        }
        
        [Test]
        public void TestSrcWrongShape()
        {
            Multinomial m = new Multinomial(2018);
            
            Tensor src = new Tensor
            {
                ValueType = Tensor.TensorType.FloatingPoint,
                Data = new float[1]
            };
            Tensor dst = new Tensor
            {
                ValueType = Tensor.TensorType.FloatingPoint,
                Data = new float[1]
            };

            Assert.Throws<ArgumentException>(() => m.Eval(src, dst));
        }
        
        [Test]
        public void TestDstWrongShape()
        {
            Multinomial m = new Multinomial(2018);
            
            Tensor src = new Tensor
            {
                ValueType = Tensor.TensorType.FloatingPoint,
                Data = new float[1, 1]
            };
            Tensor dst = new Tensor
            {
                ValueType = Tensor.TensorType.FloatingPoint,
                Data = new float[1]
            };

            Assert.Throws<ArgumentException>(() => m.Eval(src, dst));
        }

        [Test]
        public void TestUnequalBatchSize()
        {
            Multinomial m = new Multinomial(2018);
            
            Tensor src = new Tensor
            {
                ValueType = Tensor.TensorType.FloatingPoint,
                Data = new float[1, 1]
            };
            Tensor dst = new Tensor
            {
                ValueType = Tensor.TensorType.FloatingPoint,
                Data = new float[2, 1]
            };

            Assert.Throws<ArgumentException>(() => m.Eval(src, dst));
        }
        
        
    }
}