309 lines
9.8 KiB
C#
309 lines
9.8 KiB
C#
//Copyright (C) 2005 Richard J. Northedge
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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
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// GNU 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 program; if not, write to the Free Software
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// Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
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//This file is based on the GISModel.java source file found in the
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//original java implementation of MaxEnt. That source file contains the following header:
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// Copyright (C) 2001 Jason Baldridge and Gann Bierner
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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
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// GNU 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 program; if not, write to the Free Software
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// Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
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using System;
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using System.Collections.Generic;
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using System.Text;
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namespace BotSharp.MachineLearning
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{
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/// <summary>
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/// A maximum entropy model which has been trained using the Generalized
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/// Iterative Scaling procedure.
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/// </summary>
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/// <author>
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/// Tom Morton and Jason Baldridge
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/// </author>
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/// <author>
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/// Richard J. Northedge
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/// </author>
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/// <version>
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/// based on GISModel.java, $Revision: 1.13 $, $Date: 2004/06/11 20:51:44 $
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/// </version>
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public sealed class GisModel : IMaximumEntropyModel
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{
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private readonly IO.IGisModelReader _reader;
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private readonly string[] _outcomeNames;
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private readonly int _outcomeCount;
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private readonly double _initialProbability;
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private readonly double _correctionConstantInverse;
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private readonly int[] _featureCounts;
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/// <summary>
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/// Constructor for a maximum entropy model trained using the
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/// Generalized Iterative Scaling procedure.
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/// </summary>
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/// <param name="reader">
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/// A reader providing the data for the model.
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/// </param>
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public GisModel(IO.IGisModelReader reader)
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{
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this._reader = reader;
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_outcomeNames = reader.GetOutcomeLabels();
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CorrectionConstant = reader.CorrectionConstant;
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CorrectionParameter = reader.CorrectionParameter;
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_outcomeCount = _outcomeNames.Length;
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_initialProbability = Math.Log(1.0 / _outcomeCount);
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_correctionConstantInverse = 1.0 / CorrectionConstant;
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_featureCounts = new int[_outcomeCount];
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}
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// implementation of IMaxentModel -------
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/// <summary>
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/// Returns the number of outcomes for this model.
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/// </summary>
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/// <returns>
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/// The number of outcomes.
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/// </returns>
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public int OutcomeCount
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{
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get
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{
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return (_outcomeCount);
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}
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}
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/// <summary>
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/// Evaluates a context.
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/// </summary>
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/// <param name="context">
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/// A list of string names of the contextual predicates
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/// which are to be evaluated together.
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/// </param>
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/// <returns>
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/// An array of the probabilities for each of the different
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/// outcomes, all of which sum to 1.
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/// </returns>
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public double[] Evaluate(string[] context)
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{
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return Evaluate(context, new double[_outcomeCount]);
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}
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/// <summary>
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/// Use this model to evaluate a context and return an array of the
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/// likelihood of each outcome given that context.
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/// </summary>
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/// <param name="context">
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/// The names of the predicates which have been observed at
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/// the present decision point.
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/// </param>
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/// <param name="outcomeSums">
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/// This is where the distribution is stored.
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/// </param>
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/// <returns>
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/// The normalized probabilities for the outcomes given the
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/// context. The indexes of the double[] are the outcome
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/// ids, and the actual string representation of the
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/// outcomes can be obtained from the method
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/// GetOutcome(int outcomeIndex).
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/// </returns>
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public double[] Evaluate(string[] context, double[] outcomeSums)
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{
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for (int outcomeIndex = 0; outcomeIndex < _outcomeCount; outcomeIndex++)
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{
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outcomeSums[outcomeIndex] = _initialProbability;
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_featureCounts[outcomeIndex] = 0;
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}
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foreach (string con in context)
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{
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_reader.GetPredicateData(con, _featureCounts, outcomeSums);
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}
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double normal = 0.0;
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for (int outcomeIndex = 0;outcomeIndex < _outcomeCount; outcomeIndex++)
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{
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outcomeSums[outcomeIndex] = Math.Exp((outcomeSums[outcomeIndex] * _correctionConstantInverse) + ((1.0 - (_featureCounts[outcomeIndex] / CorrectionConstant)) * CorrectionParameter));
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normal += outcomeSums[outcomeIndex];
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}
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for (int outcomeIndex = 0; outcomeIndex < _outcomeCount;outcomeIndex++)
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{
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outcomeSums[outcomeIndex] /= normal;
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}
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return outcomeSums;
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}
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/// <summary>
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/// Return the name of the outcome corresponding to the highest likelihood
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/// in the parameter outcomes.
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/// </summary>
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/// <param name="outcomes">
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/// A double[] as returned by the Evaluate(string[] context)
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/// method.
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/// </param>
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/// <returns>
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/// The name of the most likely outcome.
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/// </returns>
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public string GetBestOutcome(double[] outcomes)
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{
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int bestOutcomeIndex = 0;
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for (int currentOutcome = 1; currentOutcome < outcomes.Length; currentOutcome++)
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if (outcomes[currentOutcome] > outcomes[bestOutcomeIndex])
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{
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bestOutcomeIndex = currentOutcome;
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}
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return _outcomeNames[bestOutcomeIndex];
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}
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/// <summary>
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/// Return a string matching all the outcome names with all the
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/// probabilities produced by the <code>Evaluate(string[] context)</code>
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/// method.
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/// </summary>
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/// <param name="outcomes">
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/// A <code>double[]</code> as returned by the
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/// <code>eval(string[] context)</code>
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/// method.
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/// </param>
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/// <returns>
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/// string containing outcome names paired with the normalized
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/// probability (contained in the <code>double[] outcomes</code>)
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/// for each one.
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/// </returns>
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public string GetAllOutcomes(double[] outcomes)
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{
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if (outcomes.Length != _outcomeNames.Length)
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{
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throw new ArgumentException("The double array sent as a parameter to GisModel.GetAllOutcomes() must not have been produced by this model.");
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}
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else
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{
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var outcomeInfo = new StringBuilder(outcomes.Length * 2);
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outcomeInfo.Append(_outcomeNames[0]).Append("[").Append(outcomes[0].ToString("0.0000", System.Globalization.CultureInfo.CurrentCulture)).Append("]");
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for (int currentOutcome = 1; currentOutcome < outcomes.Length; currentOutcome++)
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{
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outcomeInfo.Append(" ").Append(_outcomeNames[currentOutcome]).Append("[").Append(outcomes[currentOutcome].ToString("0.0000", System.Globalization.CultureInfo.CurrentCulture)).Append("]");
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}
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return outcomeInfo.ToString();
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}
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}
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/// <summary>
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/// Return the name of an outcome corresponding to an integer ID value.
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/// </summary>
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/// <param name="outcomeIndex">
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/// An outcome ID.
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/// </param>
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/// <returns>
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/// The name of the outcome associated with that ID.
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/// </returns>
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public string GetOutcomeName(int outcomeIndex)
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{
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return _outcomeNames[outcomeIndex];
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}
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/// <summary>
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/// Gets the index associated with the string name of the given outcome.
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/// </summary>
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/// <param name="outcome">
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/// the string name of the outcome for which the
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/// index is desired
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/// </param>
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/// <returns>
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/// the index if the given outcome label exists for this
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/// model, -1 if it does not.
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/// </returns>
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public int GetOutcomeIndex(string outcome)
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{
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for (int iCurrentOutcomeName = 0; iCurrentOutcomeName < _outcomeNames.Length; iCurrentOutcomeName++)
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{
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if (_outcomeNames[iCurrentOutcomeName] == outcome)
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{
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return iCurrentOutcomeName;
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}
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}
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return - 1;
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}
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/// <summary>
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/// Provides the predicates data structure which is part of the encoding of the maxent model
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/// information. This method will usually only be needed by
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/// GisModelWriters.
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/// </summary>
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/// <returns>
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/// Dictionary containing PatternedPredicate objects.
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/// </returns>
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public Dictionary<string, PatternedPredicate> GetPredicates()
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{
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return _reader.GetPredicates();
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}
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/// <summary>
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/// Provides the list of outcome patterns used by the predicates. This method will usually
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/// only be needed by GisModelWriters.
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/// </summary>
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/// <returns>
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/// Array of outcome patterns.
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/// </returns>
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public int[][] GetOutcomePatterns()
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{
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return _reader.GetOutcomePatterns();
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}
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/// <summary>
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/// Provides the outcome names data structure which is part of the encoding of the maxent model
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/// information. This method will usually only be needed by
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/// GisModelWriters.
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/// </summary>
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/// <returns>
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/// Array containing the outcome names.
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/// </returns>
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public string[] GetOutcomeNames()
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{
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return _outcomeNames;
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}
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/// <summary>
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/// Provides the model's correction constant.
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/// This property will usually only be needed by GisModelWriters.
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/// </summary>
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public int CorrectionConstant { get; private set; }
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/// <summary>
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/// Provides the model's correction parameter.
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/// This property will usually only be needed by GisModelWriters.
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/// </summary>
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public double CorrectionParameter { get; private set; }
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}
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}
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