mirror of
https://github.com/hanzoai/jube-fork.git
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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.
315 lines
12 KiB
C#
315 lines
12 KiB
C#
// Accord Statistics Library
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// The Accord.NET Framework
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// http://accord-framework.net
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//
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// Copyright © César Souza, 2009-2017
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// cesarsouza at gmail.com
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//
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// This library is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 2.1 of the License, or (at your option) any later version.
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//
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// This library is distributed in the hope that it will be useful,
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// but WITHOUT ANY WARRANTY; without even the implied warranty of
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// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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// Lesser General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License along with this library; if not, write to the Free Software
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// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
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//
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namespace Accord.Statistics.Models.Fields.Functions
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{
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#pragma warning disable 612, 618
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using System;
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using System.Collections.Generic;
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using Accord.Statistics.Distributions.Univariate;
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using Accord.Statistics.Models.Fields.Features;
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using Accord.Statistics.Models.Fields.Functions.Specialized;
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using Accord.Statistics.Models.Markov;
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using Accord.Compat;
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/// <summary>
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/// Potential function modeling <see cref="HiddenMarkovClassifier">Hidden Markov Classifiers</see>.
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/// </summary>
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///
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[Serializable]
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public sealed class MarkovContinuousFunction : PotentialFunctionBase<double>, IPotentialFunction<double>
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{
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/// <summary>
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/// Constructs a new potential function modeling Hidden Markov Models.
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/// </summary>
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///
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/// <param name="classifier">A hidden Markov sequence classifier.</param>
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///
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[Obsolete("Obsolete due the use of HiddenMarkovClassifier<NormalDistribution>. Please use HiddenMarkovClassifier<NormalDistribution, double> instead.")]
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public MarkovContinuousFunction(HiddenMarkovClassifier<NormalDistribution> classifier)
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{
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this.Outputs = classifier.Classes;
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int factorIndex = 0;
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var factorParams = new List<double>();
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var factorFeatures = new List<IFeature<double>>();
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this.Factors = new FactorPotential<double>[Outputs];
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int[] classOffset = new int[classifier.Classes];
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int[] edgeOffset = new int[classifier.Classes];
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int[] stateOffset = new int[classifier.Classes];
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int[] classCount = new int[classifier.Classes];
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int[] edgeCount = new int[classifier.Classes];
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int[] stateCount = new int[classifier.Classes];
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// Create features for initial class probabilities
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for (int c = 0; c < classifier.Classes; c++)
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{
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var stateParams = new List<double>();
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var stateFeatures = new List<IFeature<double>>();
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var edgeParams = new List<double>();
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var edgeFeatures = new List<IFeature<double>>();
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var classParams = new List<double>();
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var classFeatures = new List<IFeature<double>>();
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var model = classifier[c];
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// Create features for class labels
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classParams.Add(Math.Log(classifier.Priors[c]));
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classFeatures.Add(new OutputFeature<double>(this, c, c));
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// Create features for initial state probabilities
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for (int i = 0; i < model.States; i++)
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{
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edgeParams.Add(model.Probabilities[i]);
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edgeFeatures.Add(new InitialFeature<double>(this, c, i));
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}
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// Create features for state transition probabilities
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for (int i = 0; i < model.States; i++)
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{
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for (int j = 0; j < model.States; j++)
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{
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edgeParams.Add(model.Transitions[i, j]);
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edgeFeatures.Add(new TransitionFeature<double>(this, c, i, j));
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}
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}
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// Create features emission probabilities
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for (int i = 0; i < model.States; i++)
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{
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double mean = model.Emissions[i].Mean;
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double var = model.Emissions[i].Variance;
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// Occupancy
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stateParams.Add(-0.5 * (Math.Log(2.0 * Math.PI * var) + (mean * mean) / var));
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stateFeatures.Add(new OccupancyFeature<double>(this, c, i));
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// 1st Moment (x)
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stateParams.Add(mean / var);
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stateFeatures.Add(new FirstMomentFeature(this, c, i));
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// 2nd Moment (x²)
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stateParams.Add(-1.0 / (2.0 * var));
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stateFeatures.Add(new SecondMomentFeature(this, c, i));
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}
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classOffset[c] = factorIndex;
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edgeOffset[c] = factorIndex + classParams.Count;
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stateOffset[c] = factorIndex + classParams.Count + edgeParams.Count;
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classCount[c] = classParams.Count;
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edgeCount[c] = edgeParams.Count;
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stateCount[c] = stateParams.Count;
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// 1. classes
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factorFeatures.AddRange(classFeatures);
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factorParams.AddRange(classParams);
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// 2. edges
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factorFeatures.AddRange(edgeFeatures);
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factorParams.AddRange(edgeParams);
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// 3. states
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factorFeatures.AddRange(stateFeatures);
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factorParams.AddRange(stateParams);
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factorIndex += classParams.Count + stateParams.Count + edgeParams.Count;
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}
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Accord.Diagnostics.Debug.Assert(factorIndex == factorParams.Count);
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Accord.Diagnostics.Debug.Assert(factorIndex == factorFeatures.Count);
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this.Weights = factorParams.ToArray();
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this.Features = factorFeatures.ToArray();
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for (int c = 0; c < classifier.Classes; c++)
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{
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Factors[c] = new MarkovNormalFactor(this, classifier.Models[c].States, c,
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classIndex: classOffset[c], classCount: classCount[c], // 1. classes
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edgeIndex: edgeOffset[c], edgeCount: edgeCount[c], // 2. edges
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stateIndex: stateOffset[c], stateCount: stateCount[c]); // 3. states
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}
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}
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/// <summary>
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/// Constructs a new potential function modeling Hidden Markov Models.
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/// </summary>
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///
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/// <param name="classifier">A hidden Markov sequence classifier.</param>
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///
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public MarkovContinuousFunction(HiddenMarkovClassifier<NormalDistribution, double> classifier)
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{
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this.Outputs = classifier.Classes;
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int factorIndex = 0;
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var factorParams = new List<double>();
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var factorFeatures = new List<IFeature<double>>();
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this.Factors = new FactorPotential<double>[Outputs];
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int[] classOffset = new int[classifier.Classes];
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int[] edgeOffset = new int[classifier.Classes];
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int[] stateOffset = new int[classifier.Classes];
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int[] classCount = new int[classifier.Classes];
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int[] edgeCount = new int[classifier.Classes];
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int[] stateCount = new int[classifier.Classes];
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// Create features for initial class probabilities
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for (int c = 0; c < classifier.Classes; c++)
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{
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var stateParams = new List<double>();
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var stateFeatures = new List<IFeature<double>>();
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var edgeParams = new List<double>();
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var edgeFeatures = new List<IFeature<double>>();
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var classParams = new List<double>();
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var classFeatures = new List<IFeature<double>>();
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var model = classifier[c];
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// Create features for class labels
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classParams.Add(Math.Log(classifier.Priors[c]));
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classFeatures.Add(new OutputFeature<double>(this, c, c));
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// Create features for initial state probabilities
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for (int i = 0; i < model.States; i++)
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{
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edgeParams.Add(model.LogInitial[i]);
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edgeFeatures.Add(new InitialFeature<double>(this, c, i));
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}
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// Create features for state transition probabilities
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for (int i = 0; i < model.States; i++)
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{
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for (int j = 0; j < model.States; j++)
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{
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edgeParams.Add(model.LogTransitions[i][j]);
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edgeFeatures.Add(new TransitionFeature<double>(this, c, i, j));
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}
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}
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// Create features emission probabilities
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for (int i = 0; i < model.States; i++)
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{
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double mean = model.Emissions[i].Mean;
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double var = model.Emissions[i].Variance;
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// Occupancy
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stateParams.Add(-0.5 * (Math.Log(2.0 * Math.PI * var) + (mean * mean) / var));
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stateFeatures.Add(new OccupancyFeature<double>(this, c, i));
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// 1st Moment (x)
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stateParams.Add(mean / var);
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stateFeatures.Add(new FirstMomentFeature(this, c, i));
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// 2nd Moment (x²)
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stateParams.Add(-1.0 / (2.0 * var));
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stateFeatures.Add(new SecondMomentFeature(this, c, i));
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}
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classOffset[c] = factorIndex;
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edgeOffset[c] = factorIndex + classParams.Count;
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stateOffset[c] = factorIndex + classParams.Count + edgeParams.Count;
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classCount[c] = classParams.Count;
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edgeCount[c] = edgeParams.Count;
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stateCount[c] = stateParams.Count;
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// 1. classes
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factorFeatures.AddRange(classFeatures);
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factorParams.AddRange(classParams);
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// 2. edges
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factorFeatures.AddRange(edgeFeatures);
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factorParams.AddRange(edgeParams);
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// 3. states
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factorFeatures.AddRange(stateFeatures);
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factorParams.AddRange(stateParams);
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factorIndex += classParams.Count + stateParams.Count + edgeParams.Count;
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}
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Accord.Diagnostics.Debug.Assert(factorIndex == factorParams.Count);
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Accord.Diagnostics.Debug.Assert(factorIndex == factorFeatures.Count);
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this.Weights = factorParams.ToArray();
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this.Features = factorFeatures.ToArray();
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for (int c = 0; c < classifier.Classes; c++)
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{
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Factors[c] = new MarkovNormalFactor(this, classifier.Models[c].States, c,
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classIndex: classOffset[c], classCount: classCount[c], // 1. classes
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edgeIndex: edgeOffset[c], edgeCount: edgeCount[c], // 2. edges
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stateIndex: stateOffset[c], stateCount: stateCount[c]); // 3. states
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}
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}
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#region ICloneable Members
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private MarkovContinuousFunction() { }
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/// <summary>
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/// Creates a new object that is a copy of the current instance.
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/// </summary>
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///
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/// <returns>
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/// A new object that is a copy of this instance.
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/// </returns>
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///
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public object Clone()
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{
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var clone = new MarkovContinuousFunction();
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clone.Factors = new FactorPotential<double>[Factors.Length];
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for (int i = 0; i < Factors.Length; i++)
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clone.Factors[i] = Factors[i].Clone(newOwner: clone);
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clone.Features = new IFeature<double>[Features.Length];
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for (int i = 0; i < Features.Length; i++)
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clone.Features[i] = Features[i].Clone(newOwner: clone);
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clone.Outputs = Outputs;
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clone.Weights = (double[])Weights.Clone();
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return clone;
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}
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#endregion
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}
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}
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