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CK 170510
 - New infrastructure for genetic algorithms including; selection, mutation and crossover with default implementations. -- Added random & tounament selection methods -- Added point crossover method -- Added random, flip & gaussian mutation methods - Added new Filters framework along with Roulette Wheel sampling, for sampling objects according to a probability distribution - Added extension methods to ease common functions - Added new collection (NSortedList) to overcome SortedList limitations for maintaining sorted collections in ascending and descending order. Adds: #35 (MVP), Tests Passing.
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‎Src/numl.Tests/CollectionTests/CollectionsTests.cs

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using System;
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using System.Collections.Generic;
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using System.Text;
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using System.Linq;
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using numl.AI.Collections;
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using Xunit;
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using MATH = System.Math;
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using System.Diagnostics;
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namespace numl.Tests.CollectionTests
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{
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[Trait("CollectionTests", "CollectionTests")]
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public class CollectionsTests
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{
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[Fact]
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public void NSortedList_Sort_Test()
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{
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for (int run = 0; run < 2; run++)
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{
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bool reverse = (run != 0);
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var sorted = new NSortedList<int>(-1, reverse);
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int length = Math.Probability.Sampling.GetUniform(10, 1024);
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for (int i = 0; i < length; i++)
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{
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int val = Math.Probability.Sampling.GetUniform(0, length - 1);
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sorted.Add(val);
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}
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int remove = (int) MATH.Ceiling(length / 2.0);
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for (int i = 0; i < remove; i++)
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{
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int index = Math.Probability.Sampling.GetUniform(0, sorted.Count - 1);
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sorted.Remove(index);
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}
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bool result = false;
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for (int i = 1; i < (length - remove); i++)
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{
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if (reverse)
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{
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result = sorted[i - 1] >= sorted[i];
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Assert.True(result, $"Item at {i - 1} was larger than expected");
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}
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else
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{
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result = sorted[i - 1] <= sorted[i];
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Assert.True(result, $"Item at {i - 1} was smaller than expected");
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}
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}
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}
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}
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[Fact]
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public void NSortedList_Limit_Reverse_Test()
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{
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for (int run = 0; run < 2; run++)
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{
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int length = Math.Probability.Sampling.GetUniform(10, 1024);
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bool reverse = (run != 0);
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var sorted = new NSortedList<int>(length, reverse);
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for (int i = 0; i < length; i++)
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{
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int val = Math.Probability.Sampling.GetUniform(0, length - 1);
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sorted.Add(val);
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}
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length = (length / 2);
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sorted.Limit = length;
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Assert.True(sorted.Count == length, "Length exceeds specified limit");
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for (int i = 0; i < length / 2; i++)
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{
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int val = Math.Probability.Sampling.GetUniform(0, length - 1);
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sorted.Add(val);
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}
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Assert.True(sorted.Count == length, "Length exceeds specified limit after additions");
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bool result = false;
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for (int i = 1; i < sorted.Count; i++)
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{
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if (reverse)
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{
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result = sorted[i - 1] >= sorted[i];
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Assert.True(result, $"Item at {i - 1} was larger than expected");
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}
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else
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{
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result = sorted[i - 1] <= sorted[i];
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Assert.True(result, $"Item at {i - 1} was smaller than expected");
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}
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}
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}
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}
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[Fact]
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public void NSortedList_Size_Test()
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{
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var sorted = new NSortedList<int>();
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int length = Math.Probability.Sampling.GetUniform(10, 1024);
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var dict = new Dictionary<int, int>();
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for (int i = 0; i < length; i++)
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{
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int val = Math.Probability.Sampling.GetUniform(0, length - 1);
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sorted.Add(val);
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dict[val] = (dict.ContainsKey(val) ? dict[val] + 1 : 1);
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}
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int remove = (int) MATH.Ceiling(length / 2.0);
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for (int i = 0; i < remove; i++)
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{
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int val = Math.Probability.Sampling.GetUniform(0, sorted.Count - 1);
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if (dict.ContainsKey(val))
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{
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sorted.Remove(val);
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dict[val] = 0;
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Assert.False(sorted.Contains(val));
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}
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}
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var contains = (dict.Where(w => w.Value == 0 && sorted.Contains(w.Key)));
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bool result = (contains.Count() == 0);
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Assert.True(result, "Length was not equal to the elements in the collection");
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bool lresult = (sorted.Select(s => s).Count() == sorted.Count);
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Assert.True(lresult, "Enumerator extends beyond number of accessible items in collection");
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}
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[Fact]
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public void NSortedList_Speed_Test()
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{
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int epochs = 10000;
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Stopwatch timer = new Stopwatch();
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timer.Start();
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var nsorted = new NSortedList<Foo>();
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for (int i = 0; i < epochs; i++)
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{
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var foo = new Foo();
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nsorted.Add(foo);
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}
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timer.Stop();
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long ts1 = timer.ElapsedMilliseconds;
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timer.Stop();
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Foo._Id = 0;
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timer.Start();
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var sorted = new SortedList<int, Foo>();
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for (int i = 0; i < epochs; i++)
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{
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var foo = new Foo();
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sorted.Add(foo.Weight, foo);
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}
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timer.Stop();
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long ts2 = timer.ElapsedMilliseconds;
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Assert.True(ts1 < ts2);
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}
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public class Foo : IComparable
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{
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internal static int _Id = 0;
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public int Weight { get; set; }
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public Foo()
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{
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this.Weight = (++_Id);
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}
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public int CompareTo(object obj)
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{
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var other = (Foo) obj;
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return this.Weight.CompareTo(other.Weight);
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}
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}
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}
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}

‎Src/numl.Tests/GeneticTests/BaseGenetic.cs

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using System;
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using System.Collections.Generic;
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using System.Text;
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using Xunit;
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using numl.Math.Probability;
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using numl.Math.LinearAlgebra;
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using numl.Genetic;
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using numl.Genetic.Functions.Crossover;
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using numl.Genetic.Functions.Mutation;
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namespace numl.Tests.GeneticTests
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{
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public abstract class BaseGenetic
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{
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public void Test_Mutation<T>(Func<T> factory, bool binary, int length, Func<IChromosome, IChromosome, bool> fnTest,
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Func<int, double> fnSequenceInitializer = null)
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where T : IMutationFunction
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{
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var s = (fnSequenceInitializer == null ?
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Vector.Create(length, i => (binary ? (i % 2 == 0 ? 1 : 0) : (i + 1)))
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: Vector.Create(length, fnSequenceInitializer));
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var parent = new Chromosome()
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{
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Sequence = s.Copy()
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};
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var mutator = factory();
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var clone = mutator.Mutate(parent);
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bool result = fnTest(parent, clone);
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Assert.True(result);
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}
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public void Test_Crossover<T>(Func<T> factory, int length, Func<IChromosome, IChromosome, IChromosome, bool> fnTest)
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where T : ICrossoverFunction
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{
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ICrossoverFunction crossover = factory();
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var s1 = Vector.Create(length, i => (i + 1) * 100);
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var parent1 = new Chromosome()
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{
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Sequence = s1.Copy()
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};
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var s2 = Vector.Create(length, i => (i + 1));
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var parent2 = new Chromosome()
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{
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Sequence = s2.Copy()
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};
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var child = crossover.Crossover(parent1, parent2);
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bool result = fnTest(parent1, parent2, child);
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Assert.True(result);
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}
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}
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}

‎Src/numl.Tests/GeneticTests/CrossoverTests.cs

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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Text;
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using Xunit;
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using numl.Genetic;
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using numl.Genetic.Functions.Crossover;
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using MATH = System.Math;
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namespace numl.Tests.GeneticTests
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{
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[Trait("Category", "Genetic")]
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public class CrossoverTests : BaseGenetic
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{
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[Fact]
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public void Test_Point_Crossover()
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{
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double prob = 0.25;
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int points = 5;
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int length = 50;
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while (prob <= 1.0)
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{
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var crossover = new PointCrossover(points) { Probability = prob };
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this.Test_Crossover(() => crossover, length,
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(p1, p2, child) =>
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{
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bool result = false;
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var sequence = child.Sequence;
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double count1 = p1.Sequence.Intersect(sequence).Count();
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double count2 = p2.Sequence.Intersect(sequence).Count();
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double p = (MATH.Pow(prob, points)) / 2;
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double c1 = 0, c2 = 0;
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int i; double diff; double t1 = 0, t2 = 0;
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for (i = 0; i < crossover.Points.Length - 1; i++)
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{
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diff = (crossover.Points[i + 1] - crossover.Points[i]);
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if (i % 2 == 0)
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{
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c1 += diff;
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t1 += (diff / length) * (1.0 - p);
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}
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else
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{
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c2 += diff;
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t2 += (diff / length) * p;
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}
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}
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diff = length - crossover.Points[i];
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if (i % 2 == 0)
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{
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c1 += diff;
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t1 += (diff / length) * (1.0 - p);
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}
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else
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{
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c2 += diff;
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t2 += (diff / length) * p;
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}
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t1 = t1 / (t1 + t2);
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t2 = t2 / (t1 + t2);
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result = (t1 >= (1.0 - p * 2.0) && t2 <= p * 2.0);
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return result;
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});
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prob += 0.25;
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}
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}
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}
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}

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