mirror of
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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.
250 lines
8.5 KiB
C#
250 lines
8.5 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.Markov.Hybrid
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{
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using System;
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using Accord.Math;
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/// <summary>
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/// General Markov function for arbitrary state-emission density definitions.
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/// </summary>
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///
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/// <param name="previous">The previous state index.</param>
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/// <param name="observation">The observation at the current state.</param>
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/// <returns>An array containing the values for the observations in each next possible state.</returns>
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///
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public delegate double[] GeneralMarkovFunction(int previous, double[] observation);
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/// <summary>
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/// Hybrid Markov model for arbitrary state-observation functions.
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/// </summary>
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///
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/// <remarks>
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/// This class can be used to implement HMM hybrids such as ANN-HMM
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/// or SVM-HMMs through the specification of a custom <see cref="GeneralMarkovFunction"/>.
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/// </remarks>
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///
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public class HybridMarkovModel
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{
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/// <summary>
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/// Gets the Markov function, which takes the previous state, the
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/// next state and a observation and produces a probability value.
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/// </summary>
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///
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public GeneralMarkovFunction Function { get; private set; }
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/// <summary>
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/// Gets the number of states in the model.
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/// </summary>
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///
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public int States { get; private set; }
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/// <summary>
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/// Gets the number of dimensions of the
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/// observations handled by this model.
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/// </summary>
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///
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public int Dimension { get; private set; }
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/// <summary>
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/// Gets or sets an user-defined object associated with this model.
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/// </summary>
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///
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public object Tag { get; set; }
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/// <summary>
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/// Initializes a new instance of the <see cref="HybridMarkovModel"/> class.
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/// </summary>
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///
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/// <param name="function">A function specifying a probability for a transition-emission pair.</param>
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/// <param name="states">The number of states in the model.</param>
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/// <param name="dimension">The number of dimensions in the model.</param>
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///
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public HybridMarkovModel(GeneralMarkovFunction function, int states, int dimension)
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{
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this.Function = function;
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this.States = states;
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this.Dimension = dimension;
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}
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/// <summary>
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/// Calculates the most likely sequence of hidden states
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/// that produced the given observation sequence.
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/// </summary>
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/// <remarks>
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/// Decoding problem. Given the HMM M = (A, B, pi) and the observation sequence
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/// O = {o1,o2, ..., oK}, calculate the most likely sequence of hidden states Si
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/// that produced this observation sequence O. This can be computed efficiently
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/// using the Viterbi algorithm.
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/// </remarks>
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/// <param name="observations">
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/// A sequence of observations.</param>
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/// <param name="logLikelihood">
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/// The state optimized probability.</param>
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/// <returns>
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/// The sequence of states that most likely produced the sequence.
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/// </returns>
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///
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public int[] Decode(double[][] observations, out double logLikelihood)
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{
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if (observations == null)
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throw new ArgumentNullException("observations");
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if (observations.Length == 0)
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{
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logLikelihood = Double.NegativeInfinity;
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return new int[0];
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}
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// Viterbi-forward algorithm.
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int T = observations.Length;
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int states = States;
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int maxState;
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double maxWeight;
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double weight;
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int[,] s = new int[states, T];
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double[,] lnFwd = new double[states, T];
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double[][] output = new double[states][];
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// Base
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output[0] = Function(-1, observations[0]);
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for (int i = 0; i < states; i++)
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lnFwd[i, 0] = output[0][i];
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// Induction
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for (int t = 1; t < T; t++)
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{
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double[] observation = observations[t];
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for (int i = 0; i < States; i++)
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output[i] = Function(i, observation);
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for (int j = 0; j < states; j++)
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{
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maxState = 0;
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maxWeight = lnFwd[0, t - 1] + output[0][j];
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for (int i = 1; i < states; i++)
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{
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weight = lnFwd[i, t - 1] + output[i][j];
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if (weight > maxWeight)
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{
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maxState = i;
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maxWeight = weight;
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}
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}
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lnFwd[j, t] = maxWeight;
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s[j, t] = maxState;
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}
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}
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// Find maximum value for time T-1
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maxState = 0;
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maxWeight = lnFwd[0, T - 1];
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for (int i = 1; i < states; i++)
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{
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if (lnFwd[i, T - 1] > maxWeight)
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{
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maxState = i;
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maxWeight = lnFwd[i, T - 1];
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}
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}
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// Trackback
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int[] path = new int[T];
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path[T - 1] = maxState;
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for (int t = T - 2; t >= 0; t--)
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path[t] = s[path[t + 1], t + 1];
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// Returns the sequence probability as an out parameter
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logLikelihood = maxWeight;
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// Returns the most likely (Viterbi path) for the given sequence
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return path;
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}
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/// <summary>
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/// Calculates the probability that this model has generated the given sequence.
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/// </summary>
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/// <remarks>
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/// Evaluation problem. Given the HMM M = (A, B, pi) and the observation
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/// sequence O = {o1, o2, ..., oK}, calculate the probability that model
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/// M has generated sequence O. This can be computed efficiently using the
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/// Forward algorithm. </remarks>
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/// <param name="observations">
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/// A sequence of observations. </param>
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/// <returns>
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/// The probability that the given sequence has been generated by this model.
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/// </returns>
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///
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public double Evaluate(double[][] observations)
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{
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int T = observations.Length;
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double[,] lnFwd = new double[States, T];
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double[][] output = new double[States][];
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// 1. Initialization
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output[0] = Function(-1, observations[0]);
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for (int i = 0; i < States; i++)
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lnFwd[0, i] = Math.Log(output[0][i]);
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// 2. Induction
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for (int t = 1; t < T; t++)
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{
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double[] x = observations[t];
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for (int i = 0; i < States; i++)
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output[i] = Function(i, x);
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for (int i = 0; i < States; i++)
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{
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double sum = Double.NegativeInfinity;
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for (int j = 0; j < States; j++)
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sum = Special.LogSum(sum, lnFwd[t - 1, j] + output[j][i]);
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lnFwd[t, i] = sum;
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}
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}
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double logLikelihood = Double.NegativeInfinity;
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for (int i = 0; i < States; i++)
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logLikelihood = Special.LogSum(logLikelihood, lnFwd[T - 1, i]);
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return logLikelihood;
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}
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}
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}
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