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.
1238 lines
50 KiB
C#
1238 lines
50 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;
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using Accord.Statistics.Distributions.Multivariate;
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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 Hidden Markov Models.
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/// </summary>
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///
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[Serializable]
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public sealed class MarkovMultivariateFunction
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: PotentialFunctionBase<double[]>, IPotentialFunction<double[]>
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{
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/// <summary>
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/// Gets the total number of dimensions for
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/// this multivariate potential function.
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/// </summary>
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///
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public int Dimensions { get; private set; }
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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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/// <param name="includePriors">True to include class features (priors), false otherwise.</param>
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///
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[Obsolete("Obsolete due the use of HiddenMarkovClassifier<MultivariateNormalDistribution>. Please use HiddenMarkovClassifier<MultivariateNormalDistribution, double[]> instead.")]
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public MarkovMultivariateFunction(HiddenMarkovClassifier<MultivariateNormalDistribution> classifier, bool includePriors = true)
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{
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// TODO: Mark this function as obsolete
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this.Outputs = classifier.Classes;
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this.Dimensions = classifier.Models[0].Dimension;
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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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if (includePriors)
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{
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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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}
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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.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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for (int d = 0; d < model.Dimension; d++)
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{
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double mean = model.Emissions[i].Mean[d];
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double var = model.Emissions[i].Variance[d];
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double u = -0.5 * (Math.Log(2.0 * Math.PI * var) + (mean * mean) / var);
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double m1 = mean / var;
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double m2 = -1.0 / (2.0 * var);
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// Occupancy
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stateParams.Add(u);
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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(m1);
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stateFeatures.Add(new MultivariateFirstMomentFeature(this, c, i, d));
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// 2nd Moment (x²)
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stateParams.Add(m2);
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stateFeatures.Add(new MultivariateSecondMomentFeature(this, c, i, d));
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}
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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.Models.Length; c++)
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{
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Factors[c] = new MarkovMultivariateNormalFactor(this, classifier.Models[c].States, c, Dimensions,
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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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/// <param name="includePriors">True to include class features (priors), false otherwise.</param>
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///
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public MarkovMultivariateFunction(HiddenMarkovClassifier<MultivariateNormalDistribution, double[]> classifier, bool includePriors = true)
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{
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this.Outputs = classifier.Classes;
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this.Dimensions = classifier.Models[0].NumberOfInputs;
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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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if (includePriors)
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{
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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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}
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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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for (int d = 0; d < model.NumberOfInputs; d++)
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{
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double mean = model.Emissions[i].Mean[d];
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double var = model.Emissions[i].Variance[d];
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double u = -0.5 * (Math.Log(2.0 * Math.PI * var) + (mean * mean) / var);
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double m1 = mean / var;
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double m2 = -1.0 / (2.0 * var);
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// Occupancy
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stateParams.Add(u);
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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(m1);
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stateFeatures.Add(new MultivariateFirstMomentFeature(this, c, i, d));
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// 2nd Moment (x²)
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stateParams.Add(m2);
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stateFeatures.Add(new MultivariateSecondMomentFeature(this, c, i, d));
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}
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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.Models.Length; c++)
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{
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Factors[c] = new MarkovMultivariateNormalFactor(this, classifier.Models[c].States, c, Dimensions,
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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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/// <param name="includePriors">True to include class features (priors), false otherwise.</param>
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///
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[Obsolete("Obsolete due the use of HiddenMarkovClassifier<Independent<NormalDistribution>>. Please use HiddenMarkovClassifier<Independent<NormalDistribution, double>, double[]> instead.")]
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public MarkovMultivariateFunction(HiddenMarkovClassifier<Independent<NormalDistribution>> classifier, bool includePriors = true)
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{
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this.Outputs = classifier.Classes;
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this.Dimensions = classifier.Models[0].Dimension;
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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[] startClassIndex = new int[classifier.Classes];
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int[] startEdgeIndex = new int[classifier.Classes];
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int[] startStateIndex = new int[classifier.Classes];
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int[] endClassIndex = new int[classifier.Classes];
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int[] endEdgeIndex = new int[classifier.Classes];
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int[] endStateIndex = 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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if (includePriors)
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{
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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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}
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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.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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for (int d = 0; d < model.Emissions[i].Mean.Length; d++)
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{
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double mean = model.Emissions[i].Mean[d];
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double var = model.Emissions[i].Variance[d];
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double u = -0.5 * (Math.Log(2.0 * Math.PI * var) + (mean * mean) / var);
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double m1 = mean / var;
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double m2 = -1.0 / (2.0 * var);
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// Occupancy
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stateParams.Add(u);
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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(m1);
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stateFeatures.Add(new MultivariateFirstMomentFeature(this, c, i, d));
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// 2nd Moment (x²)
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stateParams.Add(m2);
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stateFeatures.Add(new MultivariateSecondMomentFeature(this, c, i, d));
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}
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}
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startClassIndex[c] = factorIndex;
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startEdgeIndex[c] = factorIndex + classParams.Count;
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startStateIndex[c] = factorIndex + classParams.Count + edgeParams.Count;
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endClassIndex[c] = classParams.Count;
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endEdgeIndex[c] = edgeParams.Count;
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endStateIndex[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 MarkovMultivariateNormalFactor(this, classifier.Models[c].States, c, Dimensions,
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classIndex: startClassIndex[c], classCount: endClassIndex[c], // 1. classes
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edgeIndex: startEdgeIndex[c], edgeCount: endEdgeIndex[c], // 2. edges
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stateIndex: startStateIndex[c], stateCount: endStateIndex[c]); // 3. states
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|
}
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|
}
|
|
|
|
/// <summary>
|
|
/// Constructs a new potential function modeling Hidden Markov Models.
|
|
/// </summary>
|
|
///
|
|
/// <param name="classifier">A hidden Markov sequence classifier.</param>
|
|
/// <param name="includePriors">True to include class features (priors), false otherwise.</param>
|
|
///
|
|
public MarkovMultivariateFunction(HiddenMarkovClassifier<Independent<NormalDistribution, double>, double[]> classifier, bool includePriors = true)
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{
|
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this.Outputs = classifier.Classes;
|
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this.Dimensions = classifier.Models[0].NumberOfInputs;
|
|
|
|
int factorIndex = 0;
|
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var factorParams = new List<double>();
|
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var factorFeatures = new List<IFeature<double[]>>();
|
|
|
|
this.Factors = new FactorPotential<double[]>[Outputs];
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|
|
|
int[] startClassIndex = new int[classifier.Classes];
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int[] startEdgeIndex = new int[classifier.Classes];
|
|
int[] startStateIndex = new int[classifier.Classes];
|
|
int[] endClassIndex = new int[classifier.Classes];
|
|
int[] endEdgeIndex = new int[classifier.Classes];
|
|
int[] endStateIndex = new int[classifier.Classes];
|
|
|
|
|
|
// Create features for initial class probabilities
|
|
for (int c = 0; c < classifier.Classes; c++)
|
|
{
|
|
var stateParams = new List<double>();
|
|
var stateFeatures = new List<IFeature<double[]>>();
|
|
|
|
var edgeParams = new List<double>();
|
|
var edgeFeatures = new List<IFeature<double[]>>();
|
|
|
|
var classParams = new List<double>();
|
|
var classFeatures = new List<IFeature<double[]>>();
|
|
|
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var model = classifier[c];
|
|
|
|
if (includePriors)
|
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{
|
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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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|
}
|
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|
|
// Create features for initial state probabilities
|
|
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
|
|
for (int i = 0; i < model.States; i++)
|
|
{
|
|
for (int j = 0; j < model.States; j++)
|
|
{
|
|
edgeParams.Add(model.LogTransitions[i][j]);
|
|
edgeFeatures.Add(new TransitionFeature<double[]>(this, c, i, j));
|
|
}
|
|
}
|
|
|
|
// Create features emission probabilities
|
|
for (int i = 0; i < model.States; i++)
|
|
{
|
|
for (int d = 0; d < model.Emissions[i].Mean.Length; d++)
|
|
{
|
|
double mean = model.Emissions[i].Mean[d];
|
|
double var = model.Emissions[i].Variance[d];
|
|
|
|
double u = -0.5 * (Math.Log(2.0 * Math.PI * var) + (mean * mean) / var);
|
|
double m1 = mean / var;
|
|
double m2 = -1.0 / (2.0 * var);
|
|
|
|
// Occupancy
|
|
stateParams.Add(u);
|
|
stateFeatures.Add(new OccupancyFeature<double[]>(this, c, i));
|
|
|
|
// 1st Moment (x)
|
|
stateParams.Add(m1);
|
|
stateFeatures.Add(new MultivariateFirstMomentFeature(this, c, i, d));
|
|
|
|
// 2nd Moment (x²)
|
|
stateParams.Add(m2);
|
|
stateFeatures.Add(new MultivariateSecondMomentFeature(this, c, i, d));
|
|
}
|
|
}
|
|
|
|
startClassIndex[c] = factorIndex;
|
|
startEdgeIndex[c] = factorIndex + classParams.Count;
|
|
startStateIndex[c] = factorIndex + classParams.Count + edgeParams.Count;
|
|
|
|
endClassIndex[c] = classParams.Count;
|
|
endEdgeIndex[c] = edgeParams.Count;
|
|
endStateIndex[c] = stateParams.Count;
|
|
|
|
|
|
// 1. classes
|
|
factorFeatures.AddRange(classFeatures);
|
|
factorParams.AddRange(classParams);
|
|
|
|
// 2. edges
|
|
factorFeatures.AddRange(edgeFeatures);
|
|
factorParams.AddRange(edgeParams);
|
|
|
|
// 3. states
|
|
factorFeatures.AddRange(stateFeatures);
|
|
factorParams.AddRange(stateParams);
|
|
|
|
factorIndex += classParams.Count + stateParams.Count + edgeParams.Count;
|
|
}
|
|
|
|
Accord.Diagnostics.Debug.Assert(factorIndex == factorParams.Count);
|
|
Accord.Diagnostics.Debug.Assert(factorIndex == factorFeatures.Count);
|
|
|
|
this.Weights = factorParams.ToArray();
|
|
this.Features = factorFeatures.ToArray();
|
|
|
|
for (int c = 0; c < classifier.Classes; c++)
|
|
{
|
|
Factors[c] = new MarkovMultivariateNormalFactor(this, classifier.Models[c].States, c, Dimensions,
|
|
classIndex: startClassIndex[c], classCount: endClassIndex[c], // 1. classes
|
|
edgeIndex: startEdgeIndex[c], edgeCount: endEdgeIndex[c], // 2. edges
|
|
stateIndex: startStateIndex[c], stateCount: endStateIndex[c]); // 3. states
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Constructs a new potential function modeling Hidden Markov Models.
|
|
/// </summary>
|
|
///
|
|
/// <param name="classifier">A hidden Markov sequence classifier.</param>
|
|
/// <param name="includePriors">True to include class features (priors), false otherwise.</param>
|
|
///
|
|
public MarkovMultivariateFunction(HiddenMarkovClassifier<Independent<NormalDistribution>, double[]> classifier, bool includePriors = true)
|
|
{
|
|
// TODO: Unify the contents of this method with the method above
|
|
|
|
this.Outputs = classifier.Classes;
|
|
this.Dimensions = classifier.Models[0].NumberOfInputs;
|
|
|
|
int factorIndex = 0;
|
|
var factorParams = new List<double>();
|
|
var factorFeatures = new List<IFeature<double[]>>();
|
|
|
|
this.Factors = new FactorPotential<double[]>[Outputs];
|
|
|
|
int[] startClassIndex = new int[classifier.Classes];
|
|
int[] startEdgeIndex = new int[classifier.Classes];
|
|
int[] startStateIndex = new int[classifier.Classes];
|
|
int[] endClassIndex = new int[classifier.Classes];
|
|
int[] endEdgeIndex = new int[classifier.Classes];
|
|
int[] endStateIndex = new int[classifier.Classes];
|
|
|
|
|
|
// Create features for initial class probabilities
|
|
for (int c = 0; c < classifier.Classes; c++)
|
|
{
|
|
var stateParams = new List<double>();
|
|
var stateFeatures = new List<IFeature<double[]>>();
|
|
|
|
var edgeParams = new List<double>();
|
|
var edgeFeatures = new List<IFeature<double[]>>();
|
|
|
|
var classParams = new List<double>();
|
|
var classFeatures = new List<IFeature<double[]>>();
|
|
|
|
var model = classifier[c];
|
|
|
|
if (includePriors)
|
|
{
|
|
// Create features for class labels
|
|
classParams.Add(Math.Log(classifier.Priors[c]));
|
|
classFeatures.Add(new OutputFeature<double[]>(this, c, c));
|
|
}
|
|
|
|
// Create features for initial state probabilities
|
|
for (int i = 0; i < model.States; i++)
|
|
{
|
|
edgeParams.Add(model.LogInitial[i]);
|
|
edgeFeatures.Add(new InitialFeature<double[]>(this, c, i));
|
|
}
|
|
|
|
// Create features for state transition probabilities
|
|
for (int i = 0; i < model.States; i++)
|
|
{
|
|
for (int j = 0; j < model.States; j++)
|
|
{
|
|
edgeParams.Add(model.LogTransitions[i][j]);
|
|
edgeFeatures.Add(new TransitionFeature<double[]>(this, c, i, j));
|
|
}
|
|
}
|
|
|
|
// Create features emission probabilities
|
|
for (int i = 0; i < model.States; i++)
|
|
{
|
|
for (int d = 0; d < model.Emissions[i].Mean.Length; d++)
|
|
{
|
|
double mean = model.Emissions[i].Mean[d];
|
|
double var = model.Emissions[i].Variance[d];
|
|
|
|
double u = -0.5 * (Math.Log(2.0 * Math.PI * var) + (mean * mean) / var);
|
|
double m1 = mean / var;
|
|
double m2 = -1.0 / (2.0 * var);
|
|
|
|
// Occupancy
|
|
stateParams.Add(u);
|
|
stateFeatures.Add(new OccupancyFeature<double[]>(this, c, i));
|
|
|
|
// 1st Moment (x)
|
|
stateParams.Add(m1);
|
|
stateFeatures.Add(new MultivariateFirstMomentFeature(this, c, i, d));
|
|
|
|
// 2nd Moment (x²)
|
|
stateParams.Add(m2);
|
|
stateFeatures.Add(new MultivariateSecondMomentFeature(this, c, i, d));
|
|
}
|
|
}
|
|
|
|
startClassIndex[c] = factorIndex;
|
|
startEdgeIndex[c] = factorIndex + classParams.Count;
|
|
startStateIndex[c] = factorIndex + classParams.Count + edgeParams.Count;
|
|
|
|
endClassIndex[c] = classParams.Count;
|
|
endEdgeIndex[c] = edgeParams.Count;
|
|
endStateIndex[c] = stateParams.Count;
|
|
|
|
|
|
// 1. classes
|
|
factorFeatures.AddRange(classFeatures);
|
|
factorParams.AddRange(classParams);
|
|
|
|
// 2. edges
|
|
factorFeatures.AddRange(edgeFeatures);
|
|
factorParams.AddRange(edgeParams);
|
|
|
|
// 3. states
|
|
factorFeatures.AddRange(stateFeatures);
|
|
factorParams.AddRange(stateParams);
|
|
|
|
factorIndex += classParams.Count + stateParams.Count + edgeParams.Count;
|
|
}
|
|
|
|
Accord.Diagnostics.Debug.Assert(factorIndex == factorParams.Count);
|
|
Accord.Diagnostics.Debug.Assert(factorIndex == factorFeatures.Count);
|
|
|
|
this.Weights = factorParams.ToArray();
|
|
this.Features = factorFeatures.ToArray();
|
|
|
|
for (int c = 0; c < classifier.Classes; c++)
|
|
{
|
|
Factors[c] = new MarkovMultivariateNormalFactor(this, classifier.Models[c].States, c, Dimensions,
|
|
classIndex: startClassIndex[c], classCount: endClassIndex[c], // 1. classes
|
|
edgeIndex: startEdgeIndex[c], edgeCount: endEdgeIndex[c], // 2. edges
|
|
stateIndex: startStateIndex[c], stateCount: endStateIndex[c]); // 3. states
|
|
}
|
|
}
|
|
|
|
|
|
/// <summary>
|
|
/// Constructs a new potential function modeling Hidden Markov Models.
|
|
/// </summary>
|
|
///
|
|
/// <param name="model">A normal density hidden Markov.</param>
|
|
///
|
|
[Obsolete("Obsolete due the use of HiddenMarkovModel<MultivariateNormalDistribution>. Please use HiddenMarkovModel<MultivariateNormalDistribution, double[]> instead.")]
|
|
public MarkovMultivariateFunction(HiddenMarkovModel<MultivariateNormalDistribution> model)
|
|
{
|
|
this.Dimensions = model.Dimension;
|
|
|
|
var factorParams = new List<double>();
|
|
var factorFeatures = new List<IFeature<double[]>>();
|
|
|
|
|
|
var stateParams = new List<double>();
|
|
var stateFeatures = new List<IFeature<double[]>>();
|
|
|
|
var edgeParams = new List<double>();
|
|
var edgeFeatures = new List<IFeature<double[]>>();
|
|
|
|
|
|
|
|
// Create features for initial state probabilities
|
|
for (int i = 0; i < model.States; i++)
|
|
{
|
|
edgeParams.Add(model.LogInitial[i]);
|
|
edgeFeatures.Add(new InitialFeature<double[]>(this, 0, i));
|
|
}
|
|
|
|
// Create features for state transition probabilities
|
|
for (int i = 0; i < model.States; i++)
|
|
{
|
|
for (int j = 0; j < model.States; j++)
|
|
{
|
|
edgeParams.Add(model.Transitions[i, j]);
|
|
edgeFeatures.Add(new TransitionFeature<double[]>(this, 0, i, j));
|
|
}
|
|
}
|
|
|
|
// Create features emission probabilities
|
|
for (int i = 0; i < model.States; i++)
|
|
{
|
|
for (int d = 0; d < model.Dimension; d++)
|
|
{
|
|
double mean = model.Emissions[i].Mean[d];
|
|
double var = model.Emissions[i].Variance[d];
|
|
|
|
// Occupancy
|
|
stateParams.Add(-0.5 * (Math.Log(2.0 * Math.PI * var) + (mean * mean) / var));
|
|
stateFeatures.Add(new OccupancyFeature<double[]>(this, 0, i));
|
|
|
|
// 1st Moment (x)
|
|
stateParams.Add(mean / var);
|
|
stateFeatures.Add(new MultivariateFirstMomentFeature(this, 0, i, d));
|
|
|
|
// 2nd Moment (x²)
|
|
stateParams.Add(-1.0 / (2.0 * var));
|
|
stateFeatures.Add(new MultivariateSecondMomentFeature(this, 0, i, d));
|
|
}
|
|
}
|
|
|
|
|
|
// 1. edges
|
|
factorFeatures.AddRange(edgeFeatures);
|
|
factorParams.AddRange(edgeParams);
|
|
|
|
// 2. states
|
|
factorFeatures.AddRange(stateFeatures);
|
|
factorParams.AddRange(stateParams);
|
|
|
|
this.Weights = factorParams.ToArray();
|
|
this.Features = factorFeatures.ToArray();
|
|
|
|
|
|
|
|
Factors = new[] // First features and parameters are always belonging to edges
|
|
{
|
|
new MarkovMultivariateNormalFactor(this, model.States, 0, Dimensions,
|
|
edgeIndex: 0, edgeCount: edgeParams.Count, // 1. edges
|
|
stateIndex: edgeParams.Count, stateCount: stateParams.Count) // 2. states
|
|
};
|
|
}
|
|
|
|
/// <summary>
|
|
/// Constructs a new potential function modeling Hidden Markov Models.
|
|
/// </summary>
|
|
///
|
|
/// <param name="model">A normal density hidden Markov.</param>
|
|
///
|
|
public MarkovMultivariateFunction(HiddenMarkovModel<MultivariateNormalDistribution, double[]> model)
|
|
{
|
|
this.Dimensions = model.NumberOfInputs;
|
|
|
|
var factorParams = new List<double>();
|
|
var factorFeatures = new List<IFeature<double[]>>();
|
|
|
|
|
|
var stateParams = new List<double>();
|
|
var stateFeatures = new List<IFeature<double[]>>();
|
|
|
|
var edgeParams = new List<double>();
|
|
var edgeFeatures = new List<IFeature<double[]>>();
|
|
|
|
|
|
|
|
// Create features for initial state probabilities
|
|
for (int i = 0; i < model.States; i++)
|
|
{
|
|
edgeParams.Add(model.LogInitial[i]);
|
|
edgeFeatures.Add(new InitialFeature<double[]>(this, 0, i));
|
|
}
|
|
|
|
// Create features for state transition probabilities
|
|
for (int i = 0; i < model.States; i++)
|
|
{
|
|
for (int j = 0; j < model.States; j++)
|
|
{
|
|
edgeParams.Add(model.LogTransitions[i][j]);
|
|
edgeFeatures.Add(new TransitionFeature<double[]>(this, 0, i, j));
|
|
}
|
|
}
|
|
|
|
// Create features emission probabilities
|
|
for (int i = 0; i < model.States; i++)
|
|
{
|
|
for (int d = 0; d < model.Emissions[i].Mean.Length; d++)
|
|
{
|
|
double mean = model.Emissions[i].Mean[d];
|
|
double var = model.Emissions[i].Variance[d];
|
|
|
|
// Occupancy
|
|
stateParams.Add(-0.5 * (Math.Log(2.0 * Math.PI * var) + (mean * mean) / var));
|
|
stateFeatures.Add(new OccupancyFeature<double[]>(this, 0, i));
|
|
|
|
// 1st Moment (x)
|
|
stateParams.Add(mean / var);
|
|
stateFeatures.Add(new MultivariateFirstMomentFeature(this, 0, i, d));
|
|
|
|
// 2nd Moment (x²)
|
|
stateParams.Add(-1.0 / (2.0 * var));
|
|
stateFeatures.Add(new MultivariateSecondMomentFeature(this, 0, i, d));
|
|
}
|
|
}
|
|
|
|
|
|
// 1. edges
|
|
factorFeatures.AddRange(edgeFeatures);
|
|
factorParams.AddRange(edgeParams);
|
|
|
|
// 2. states
|
|
factorFeatures.AddRange(stateFeatures);
|
|
factorParams.AddRange(stateParams);
|
|
|
|
this.Weights = factorParams.ToArray();
|
|
this.Features = factorFeatures.ToArray();
|
|
|
|
|
|
|
|
Factors = new[] // First features and parameters are always belonging to edges
|
|
{
|
|
new MarkovMultivariateNormalFactor(this, model.States, 0, Dimensions,
|
|
edgeIndex: 0, edgeCount: edgeParams.Count, // 1. edges
|
|
stateIndex: edgeParams.Count, stateCount: stateParams.Count) // 2. states
|
|
};
|
|
}
|
|
|
|
/// <summary>
|
|
/// Constructs a new potential function modeling Hidden Markov Models.
|
|
/// </summary>
|
|
///
|
|
/// <param name="classifier">A hidden Markov sequence classifier.</param>
|
|
/// <param name="includePriors">True to include class features (priors), false otherwise.</param>
|
|
///
|
|
[Obsolete("Obsolete due the use of HiddenMarkovClassifier<Independent>. Please use HiddenMarkovClassifier<Independent, double[]> instead.")]
|
|
public MarkovMultivariateFunction(HiddenMarkovClassifier<Independent> classifier, bool includePriors = true)
|
|
{
|
|
// TODO: Mark this function as obsolete
|
|
|
|
this.Outputs = classifier.Classes;
|
|
|
|
int factorIndex = 0;
|
|
var factorParams = new List<double>();
|
|
var factorFeatures = new List<IFeature<double[]>>();
|
|
|
|
this.Factors = new FactorPotential<double[]>[Outputs];
|
|
|
|
int[] classOffset = new int[classifier.Classes];
|
|
int[] edgeOffset = new int[classifier.Classes];
|
|
int[] stateOffset = new int[classifier.Classes];
|
|
int[] classCount = new int[classifier.Classes];
|
|
int[] edgeCount = new int[classifier.Classes];
|
|
int[] stateCount = new int[classifier.Classes];
|
|
|
|
int[][][] lookupTables = new int[classifier.Classes][][];
|
|
|
|
|
|
// Create features for initial class probabilities
|
|
for (int c = 0; c < classifier.Classes; c++)
|
|
{
|
|
var stateParams = new List<double>();
|
|
var stateFeatures = new List<IFeature<double[]>>();
|
|
|
|
var edgeParams = new List<double>();
|
|
var edgeFeatures = new List<IFeature<double[]>>();
|
|
|
|
var classParams = new List<double>();
|
|
var classFeatures = new List<IFeature<double[]>>();
|
|
|
|
var model = classifier[c];
|
|
|
|
int[][] lookupTable = new int[model.States][];
|
|
for (int i = 0; i < lookupTable.Length; i++)
|
|
lookupTable[i] = new int[model.Dimension];
|
|
|
|
if (includePriors)
|
|
{
|
|
// Create features for class labels
|
|
classParams.Add(Math.Log(classifier.Priors[c]));
|
|
classFeatures.Add(new OutputFeature<double[]>(this, c, c));
|
|
}
|
|
|
|
// Create features for initial state probabilities
|
|
for (int i = 0; i < model.States; i++)
|
|
{
|
|
edgeParams.Add(model.LogInitial[i]);
|
|
edgeFeatures.Add(new InitialFeature<double[]>(this, c, i));
|
|
}
|
|
|
|
// Create features for state transition probabilities
|
|
for (int i = 0; i < model.States; i++)
|
|
{
|
|
for (int j = 0; j < model.States; j++)
|
|
{
|
|
edgeParams.Add(model.Transitions[i, j]);
|
|
edgeFeatures.Add(new TransitionFeature<double[]>(this, c, i, j));
|
|
}
|
|
}
|
|
|
|
int position = 0;
|
|
|
|
// Create features emission probabilities
|
|
for (int i = 0; i < model.States; i++)
|
|
{
|
|
for (int d = 0; d < model.Emissions[i].Components.Length; d++)
|
|
{
|
|
var distribution = model.Emissions[i].Components[d];
|
|
|
|
NormalDistribution normal = distribution as NormalDistribution;
|
|
if (normal != null)
|
|
{
|
|
double var = normal.Variance;
|
|
double mean = normal.Mean;
|
|
|
|
// Occupancy
|
|
stateParams.Add(-0.5 * (Math.Log(2.0 * Math.PI * var) + (mean * mean) / var));
|
|
stateFeatures.Add(new OccupancyFeature<double[]>(this, c, i));
|
|
lookupTable[i][d] = position;
|
|
position++;
|
|
|
|
// 1st Moment (x)
|
|
stateParams.Add(mean / var);
|
|
stateFeatures.Add(new MultivariateFirstMomentFeature(this, c, i, d));
|
|
position++;
|
|
|
|
// 2nd Moment (x²)
|
|
stateParams.Add(-1.0 / (2.0 * var));
|
|
stateFeatures.Add(new MultivariateSecondMomentFeature(this, c, i, d));
|
|
position++;
|
|
|
|
continue;
|
|
}
|
|
|
|
var discrete = distribution as GeneralDiscreteDistribution;
|
|
|
|
if (discrete != null)
|
|
{
|
|
lookupTable[i][d] = position;
|
|
|
|
for (int k = 0; k < discrete.Frequencies.Length; k++)
|
|
{
|
|
stateParams.Add(Math.Log(discrete.Frequencies[k]));
|
|
stateFeatures.Add(new MultivariateEmissionFeature(this, c, i, k, d));
|
|
position++;
|
|
}
|
|
|
|
continue;
|
|
}
|
|
}
|
|
}
|
|
|
|
classOffset[c] = factorIndex;
|
|
edgeOffset[c] = factorIndex + classParams.Count;
|
|
stateOffset[c] = factorIndex + classParams.Count + edgeParams.Count;
|
|
|
|
classCount[c] = classParams.Count;
|
|
edgeCount[c] = edgeParams.Count;
|
|
stateCount[c] = stateParams.Count;
|
|
|
|
lookupTables[c] = lookupTable;
|
|
|
|
|
|
// 1. classes
|
|
factorFeatures.AddRange(classFeatures);
|
|
factorParams.AddRange(classParams);
|
|
|
|
// 2. edges
|
|
factorFeatures.AddRange(edgeFeatures);
|
|
factorParams.AddRange(edgeParams);
|
|
|
|
// 3. states
|
|
factorFeatures.AddRange(stateFeatures);
|
|
factorParams.AddRange(stateParams);
|
|
|
|
factorIndex += classParams.Count + stateParams.Count + edgeParams.Count;
|
|
}
|
|
|
|
Accord.Diagnostics.Debug.Assert(factorIndex == factorParams.Count);
|
|
Accord.Diagnostics.Debug.Assert(factorIndex == factorFeatures.Count);
|
|
|
|
this.Weights = factorParams.ToArray();
|
|
this.Features = factorFeatures.ToArray();
|
|
|
|
|
|
for (int c = 0; c < classifier.Models.Length; c++)
|
|
{
|
|
Factors[c] = new MarkovIndependentFactor(this, classifier.Models[c].States, c, lookupTables[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>
|
|
/// Constructs a new potential function modeling Hidden Markov Models.
|
|
/// </summary>
|
|
///
|
|
/// <param name="classifier">A hidden Markov sequence classifier.</param>
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|
/// <param name="includePriors">True to include class features (priors), false otherwise.</param>
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|
///
|
|
public MarkovMultivariateFunction(HiddenMarkovClassifier<Independent, double[]> classifier, bool includePriors = true)
|
|
{
|
|
this.Outputs = classifier.Classes;
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|
|
|
int factorIndex = 0;
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|
var factorParams = new List<double>();
|
|
var factorFeatures = new List<IFeature<double[]>>();
|
|
|
|
this.Factors = new FactorPotential<double[]>[Outputs];
|
|
|
|
int[] classOffset = new int[classifier.Classes];
|
|
int[] edgeOffset = new int[classifier.Classes];
|
|
int[] stateOffset = new int[classifier.Classes];
|
|
int[] classCount = new int[classifier.Classes];
|
|
int[] edgeCount = new int[classifier.Classes];
|
|
int[] stateCount = new int[classifier.Classes];
|
|
|
|
int[][][] lookupTables = new int[classifier.Classes][][];
|
|
|
|
|
|
// Create features for initial class probabilities
|
|
for (int c = 0; c < classifier.Classes; c++)
|
|
{
|
|
var stateParams = new List<double>();
|
|
var stateFeatures = new List<IFeature<double[]>>();
|
|
|
|
var edgeParams = new List<double>();
|
|
var edgeFeatures = new List<IFeature<double[]>>();
|
|
|
|
var classParams = new List<double>();
|
|
var classFeatures = new List<IFeature<double[]>>();
|
|
|
|
var model = classifier[c];
|
|
|
|
int[][] lookupTable = new int[model.States][];
|
|
for (int i = 0; i < lookupTable.Length; i++)
|
|
lookupTable[i] = new int[model.NumberOfInputs];
|
|
|
|
if (includePriors)
|
|
{
|
|
// Create features for class labels
|
|
classParams.Add(Math.Log(classifier.Priors[c]));
|
|
classFeatures.Add(new OutputFeature<double[]>(this, c, c));
|
|
}
|
|
|
|
// Create features for initial state probabilities
|
|
for (int i = 0; i < model.States; i++)
|
|
{
|
|
edgeParams.Add(model.LogInitial[i]);
|
|
edgeFeatures.Add(new InitialFeature<double[]>(this, c, i));
|
|
}
|
|
|
|
// Create features for state transition probabilities
|
|
for (int i = 0; i < model.States; i++)
|
|
{
|
|
for (int j = 0; j < model.States; j++)
|
|
{
|
|
edgeParams.Add(model.LogTransitions[i][j]);
|
|
edgeFeatures.Add(new TransitionFeature<double[]>(this, c, i, j));
|
|
}
|
|
}
|
|
|
|
int position = 0;
|
|
|
|
// Create features emission probabilities
|
|
for (int i = 0; i < model.States; i++)
|
|
{
|
|
for (int d = 0; d < model.Emissions[i].Components.Length; d++)
|
|
{
|
|
var distribution = model.Emissions[i].Components[d];
|
|
|
|
NormalDistribution normal = distribution as NormalDistribution;
|
|
if (normal != null)
|
|
{
|
|
double var = normal.Variance;
|
|
double mean = normal.Mean;
|
|
|
|
// Occupancy
|
|
stateParams.Add(-0.5 * (Math.Log(2.0 * Math.PI * var) + (mean * mean) / var));
|
|
stateFeatures.Add(new OccupancyFeature<double[]>(this, c, i));
|
|
lookupTable[i][d] = position;
|
|
position++;
|
|
|
|
// 1st Moment (x)
|
|
stateParams.Add(mean / var);
|
|
stateFeatures.Add(new MultivariateFirstMomentFeature(this, c, i, d));
|
|
position++;
|
|
|
|
// 2nd Moment (x²)
|
|
stateParams.Add(-1.0 / (2.0 * var));
|
|
stateFeatures.Add(new MultivariateSecondMomentFeature(this, c, i, d));
|
|
position++;
|
|
|
|
continue;
|
|
}
|
|
|
|
var discrete = distribution as GeneralDiscreteDistribution;
|
|
|
|
if (discrete != null)
|
|
{
|
|
lookupTable[i][d] = position;
|
|
|
|
for (int k = 0; k < discrete.Frequencies.Length; k++)
|
|
{
|
|
stateParams.Add(Math.Log(discrete.Frequencies[k]));
|
|
stateFeatures.Add(new MultivariateEmissionFeature(this, c, i, k, d));
|
|
position++;
|
|
}
|
|
|
|
continue;
|
|
}
|
|
}
|
|
}
|
|
|
|
classOffset[c] = factorIndex;
|
|
edgeOffset[c] = factorIndex + classParams.Count;
|
|
stateOffset[c] = factorIndex + classParams.Count + edgeParams.Count;
|
|
|
|
classCount[c] = classParams.Count;
|
|
edgeCount[c] = edgeParams.Count;
|
|
stateCount[c] = stateParams.Count;
|
|
|
|
lookupTables[c] = lookupTable;
|
|
|
|
|
|
// 1. classes
|
|
factorFeatures.AddRange(classFeatures);
|
|
factorParams.AddRange(classParams);
|
|
|
|
// 2. edges
|
|
factorFeatures.AddRange(edgeFeatures);
|
|
factorParams.AddRange(edgeParams);
|
|
|
|
// 3. states
|
|
factorFeatures.AddRange(stateFeatures);
|
|
factorParams.AddRange(stateParams);
|
|
|
|
factorIndex += classParams.Count + stateParams.Count + edgeParams.Count;
|
|
}
|
|
|
|
Accord.Diagnostics.Debug.Assert(factorIndex == factorParams.Count);
|
|
Accord.Diagnostics.Debug.Assert(factorIndex == factorFeatures.Count);
|
|
|
|
this.Weights = factorParams.ToArray();
|
|
this.Features = factorFeatures.ToArray();
|
|
|
|
|
|
for (int c = 0; c < classifier.Models.Length; c++)
|
|
{
|
|
Factors[c] = new MarkovIndependentFactor(this, classifier.Models[c].States, c, lookupTables[c],
|
|
classIndex: classOffset[c], classCount: classCount[c], // 1. classes
|
|
edgeIndex: edgeOffset[c], edgeCount: edgeCount[c], // 2. edges
|
|
stateIndex: stateOffset[c], stateCount: stateCount[c]); // 3. states
|
|
}
|
|
}
|
|
|
|
|
|
#region ICloneable Members
|
|
|
|
private MarkovMultivariateFunction() { }
|
|
|
|
/// <summary>
|
|
/// Creates a new object that is a copy of the current instance.
|
|
/// </summary>
|
|
///
|
|
/// <returns>
|
|
/// A new object that is a copy of this instance.
|
|
/// </returns>
|
|
///
|
|
public object Clone()
|
|
{
|
|
var clone = new MarkovMultivariateFunction();
|
|
|
|
clone.Factors = new FactorPotential<double[]>[Factors.Length];
|
|
for (int i = 0; i < Factors.Length; i++)
|
|
clone.Factors[i] = Factors[i].Clone(newOwner: clone);
|
|
|
|
clone.Features = new IFeature<double[]>[Features.Length];
|
|
for (int i = 0; i < Features.Length; i++)
|
|
clone.Features[i] = Features[i].Clone(newOwner: clone);
|
|
|
|
clone.Outputs = Outputs;
|
|
clone.Weights = (double[])Weights.Clone();
|
|
|
|
clone.Dimensions = Dimensions;
|
|
|
|
return clone;
|
|
}
|
|
|
|
#endregion
|
|
|
|
}
|
|
}
|