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richard-churchman
cc0a2bee21
Included open-source Accord.net library code natively to the solution given the deprecated state of that project. Accord.net will be supported inside Jube for own needs (such as upgrading to .Net 7+). Accord.net libraries modified to compile to .Net 6. Modified Accord.net libraries to better support Newtonsoft Json.net serialisation. Included annotations in the Accord.net libraries to house important dependency statistics. Serialised json can be used independently of Jube to recall a Neural Network. Removal of Binary Serializer totally as a precursor to the projects support of .Net 7+. Extensive bug fixing in crucial functions of Neural Network training and recall. While this part of the system remains in Alpha, it is substantially more fit for purpose.
99 lines
3.5 KiB
C#
99 lines
3.5 KiB
C#
// AForge Genetic Library
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// AForge.NET framework
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// http://www.aforgenet.com/framework/
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//
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// Copyright © AForge.NET, 2006-2011
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// [email protected]
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//
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namespace Accord.Genetic
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{
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using System;
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using System.Collections;
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using System.Collections.Generic;
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using AForge;
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/// <summary>
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/// Rank selection method.
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/// </summary>
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///
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/// <remarks><para>The algorithm selects chromosomes to the new generation depending on
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/// their fitness values - the better fitness value chromosome has, the more chances
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/// it has to become member of the new generation. Each chromosome can be selected
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/// several times to the new generation.</para>
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///
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/// <para>This algorithm is similar to <see cref="RouletteWheelSelection">Roulette Wheel
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/// Selection</see> algorithm, but the difference is in "wheel" and its sectors' size
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/// calculation method. The size of the wheel equals to <b>size * ( size + 1 ) / 2</b>,
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/// where <b>size</b> is the current size of population. The worst chromosome has its sector's
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/// size equal to 1, the next chromosome has its sector's size equal to 2, etc.</para>
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/// </remarks>
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///
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public class RankSelection : ISelectionMethod
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{
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/// <summary>
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/// Initializes a new instance of the <see cref="RankSelection"/> class.
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/// </summary>
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public RankSelection() { }
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/// <summary>
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/// Apply selection to the specified population.
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/// </summary>
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///
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/// <param name="chromosomes">Population, which should be filtered.</param>
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/// <param name="size">The amount of chromosomes to keep.</param>
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///
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/// <remarks>Filters specified population keeping only those chromosomes, which
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/// won "roulette" game.</remarks>
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///
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public void ApplySelection(List<IChromosome> chromosomes, int size)
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{
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var rand = Accord.Math.Random.Generator.Random;
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// new population, initially empty
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List<IChromosome> newPopulation = new List<IChromosome>();
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// size of current population
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int currentSize = chromosomes.Count;
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// sort current population
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chromosomes.Sort();
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// calculate amount of ranges in the wheel
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double ranges = currentSize * (currentSize + 1) / 2;
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// create wheel ranges
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double[] rangeMax = new double[currentSize];
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double s = 0;
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for (int i = 0, n = currentSize; i < currentSize; i++, n--)
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{
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s += ((double)n / ranges);
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rangeMax[i] = s;
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}
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// select chromosomes from old population to the new population
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for (int j = 0; j < size; j++)
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{
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// get wheel value
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double wheelValue = rand.NextDouble();
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// find the chromosome for the wheel value
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for (int i = 0; i < currentSize; i++)
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{
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if (wheelValue <= rangeMax[i])
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{
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// add the chromosome to the new population
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newPopulation.Add(((IChromosome)chromosomes[i]).Clone());
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break;
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}
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}
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}
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// empty current population
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chromosomes.Clear();
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// move elements from new to current population
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chromosomes.AddRange(newPopulation);
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}
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}
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}
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