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LikelihoodTaggerBase.cs
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LikelihoodTaggerBase.cs
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// Accord Statistics Library
// The Accord.NET Framework
// http://accord-framework.net
//
// Copyright © César Souza, 2009-2017
// cesarsouza at gmail.com
//
// This library is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 2.1 of the License, or (at your option) any later version.
//
// This library is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
// Lesser General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License along with this library; if not, write to the Free Software
// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
//
namespace Accord.MachineLearning
{
using Accord.Math;
using Accord.Statistics;
using System;
using Accord.Compat;
/// <summary>
/// Base implementation for generative observation sequence taggers. A sequence
/// tagger can predict the class label of each individual observation in a
/// input sequence vector.
/// </summary>
///
/// <typeparam name="TInput">The data type for the input data. Default is double[].</typeparam>
///
[Serializable]
public abstract class LikelihoodTaggerBase<TInput> :
ScoreTaggerBase<TInput>,
ILikelihoodTagger<TInput>
{
/// <summary>
/// Predicts a the probability that the sequence vector
/// has been generated by this log-likelihood tagger.
/// </summary>
///
public abstract double[] LogLikelihood(TInput[][] sequences, double[] result);
/// <summary>
/// Predicts a the probability that the sequence vector
/// has been generated by this log-likelihood tagger.
/// </summary>
///
public abstract double[] LogLikelihood(TInput[][] sequences, ref int[][] decision, double[] result);
/// <summary>
/// Predicts a the log-likelihood for each of the observations in
/// the sequence vector assuming each of the possible states in the
/// tagger model.
/// </summary>
///
public abstract double[][] LogLikelihoods(TInput[] sequence, double[][] result);
/// <summary>
/// Predicts a the log-likelihood for each of the observations in
/// the sequence vector assuming each of the possible states in the
/// tagger model.
/// </summary>
///
public abstract double[][] LogLikelihoods(TInput[] sequence, ref int[] decision, double[][] result);
/// <summary>
/// Predicts a the probability that the sequence vector
/// has been generated by this log-likelihood tagger.
/// </summary>
///
public double Probability(TInput[] sequence)
{
return Probability(new[] { sequence })[0];
}
/// <summary>
/// Predicts a the probability that the sequence vector
/// has been generated by this log-likelihood tagger.
/// </summary>
///
public double Probability(TInput[] sequence, ref int[] decision)
{
var d = new[] { decision };
double p = Probability(new[] { sequence }, ref d)[0];
decision = d[0];
return p;
}
/// <summary>
/// Predicts a the probability that the sequence vector
/// has been generated by this log-likelihood tagger.
/// </summary>
///
public double[] Probability(TInput[][] sequences)
{
return Probability(sequences, new double[sequences.Length]);
}
/// <summary>
/// Predicts a the probability that the sequence vector
/// has been generated by this log-likelihood tagger.
/// </summary>
///
public double[] Probability(TInput[][] sequences, ref int[][] decision)
{
return Probability(sequences, ref decision, new double[sequences.Length]);
}
/// <summary>
/// Predicts a the probability that the sequence vector
/// has been generated by this log-likelihood tagger.
/// </summary>
///
public virtual double[] Probability(TInput[][] sequences, double[] result)
{
LogLikelihood(sequences, result);
return Elementwise.Exp(result, result: result);
}
/// <summary>
/// Predicts a the probability that the sequence vector
/// has been generated by this log-likelihood tagger.
/// </summary>
///
public virtual double[] Probability(TInput[][] sequences, ref int[][] decision, double[] result)
{
LogLikelihood(sequences, ref decision, result);
return Elementwise.Exp(result, result: result);
}
/// <summary>
/// Predicts a the probability that the sequence vector
/// has been generated by this log-likelihood tagger.
/// </summary>
///
public double LogLikelihood(TInput[] sequence)
{
return LogLikelihood(new[] { sequence })[0];
}
/// <summary>
/// Predicts a the probability that the sequence vector
/// has been generated by this log-likelihood tagger.
/// </summary>
///
public double LogLikelihood(TInput[] sequence, ref int[] decision)
{
var d = new[] { decision };
double l = LogLikelihood(new[] { sequence }, ref d)[0];
decision = d[0];
return l;
}
/// <summary>
/// Predicts a the probability that the sequence vector
/// has been generated by this log-likelihood tagger.
/// </summary>
///
public double[] LogLikelihood(TInput[][] sequences)
{
return LogLikelihood(sequences, new double[sequences.Length]);
}
/// <summary>
/// Predicts a the probability that the sequence vector
/// has been generated by this log-likelihood tagger.
/// </summary>
///
public double[] LogLikelihood(TInput[][] sequences, ref int[][] decision)
{
return LogLikelihood(sequences, ref decision, new double[sequences.Length]);
}
/// <summary>
/// Predicts a the probabilities for each of the observations in
/// the sequence vector assuming each of the possible states in the
/// tagger model.
/// </summary>
///
public double[][] Probabilities(TInput[] sequence)
{
return Probabilities(sequence, create(sequence));
}
/// <summary>
/// Predicts a the probabilities for each of the observations in
/// the sequence vector assuming each of the possible states in the
/// tagger model.
/// </summary>
///
public virtual double[][] Probabilities(TInput[] sequence, double[][] result)
{
Probabilities(sequence, result);
return Elementwise.Exp(result, result: result);
}
/// <summary>
/// Predicts a the log-likelihood for each of the observations in
/// the sequence vector assuming each of the possible states in the
/// tagger model.
/// </summary>
///
public double[][] Probabilities(TInput[] sequence, ref int[] decision)
{
return Probabilities(sequence, ref decision, create(sequence));
}
/// <summary>
/// Predicts a the log-likelihood for each of the observations in
/// the sequence vector assuming each of the possible states in the
/// tagger model.
/// </summary>
///
public virtual double[][] Probabilities(TInput[] sequence, ref int[] decision, double[][] result)
{
Probabilities(sequence, ref decision, result);
return Elementwise.Exp(result, result: result);
}
/// <summary>
/// Predicts a the log-likelihood for each of the observations in
/// the sequence vector assuming each of the possible states in the
/// tagger model.
/// </summary>
///
public double[][] LogLikelihoods(TInput[] sequence)
{
return LogLikelihoods(sequence, create(sequence));
}
/// <summary>
/// Predicts a the log-likelihood for each of the observations in
/// the sequence vector assuming each of the possible states in the
/// tagger model.
/// </summary>
///
public double[][] LogLikelihoods(TInput[] sequence, ref int[] decision)
{
return LogLikelihoods(sequence, ref decision, create(sequence));
}
/// <summary>
/// Predicts a the log-likelihood for each of the observations in
/// the sequence vector assuming each of the possible states in the
/// tagger model.
/// </summary>
///
public double[][][] Probabilities(TInput[][] sequences)
{
return Probabilities(sequences, create(sequences));
}
/// <summary>
/// Predicts a the log-likelihood for each of the observations in
/// the sequence vector assuming each of the possible states in the
/// tagger model.
/// </summary>
///
public virtual double[][][] Probabilities(TInput[][] sequences, double[][][] result)
{
LogLikelihoods(sequences, result);
for (int i = 0; i < result.Length; i++)
Elementwise.Exp(result[i], result: result[i]);
return result;
}
/// <summary>
/// Predicts a the probabilities for each of the observations in
/// the sequence vector assuming each of the possible states in the
/// tagger model.
/// </summary>
///
public double[][][] Probabilities(TInput[][] sequences, ref int[][] decision)
{
return Probabilities(sequences, ref decision, create(sequences));
}
/// <summary>
/// Predicts a the probabilities for each of the observations in
/// the sequence vector assuming each of the possible states in the
/// tagger model.
/// </summary>
///
public virtual double[][][] Probabilities(TInput[][] sequences, ref int[][] decision, double[][][] result)
{
LogLikelihoods(sequences, ref decision, result);
for (int i = 0; i < result.Length; i++)
Elementwise.Exp(result[i], result: result[i]);
return result;
}
/// <summary>
/// Predicts a the log-likelihood for each of the observations in
/// the sequence vector assuming each of the possible states in the
/// tagger model.
/// </summary>
///
public double[][][] LogLikelihoods(TInput[][] sequences)
{
return LogLikelihoods(sequences, create(sequences));
}
/// <summary>
/// Predicts a the log-likelihood for each of the observations in
/// the sequence vector assuming each of the possible states in the
/// tagger model.
/// </summary>
///
public virtual double[][][] LogLikelihoods(TInput[][] sequences, double[][][] result)
{
for (int i = 0; i < sequences.Length; i++)
LogLikelihoods(sequences[i], result[i]);
return result;
}
/// <summary>
/// Predicts a the log-likelihood for each of the observations in
/// the sequence vector assuming each of the possible states in the
/// tagger model.
/// </summary>
///
public virtual double[][][] LogLikelihoods(TInput[][] sequences, ref int[][] decision, double[][][] result)
{
for (int i = 0; i < sequences.Length; i++)
LogLikelihoods(sequences[i], ref decision[i], result[i]);
return result;
}
/// <summary>
/// Predicts a the log-likelihood for each of the observations in
/// the sequence vector assuming each of the possible states in the
/// tagger model.
/// </summary>
///
public double[][][] LogLikelihoods(TInput[][] sequences, ref int[][] decision)
{
return LogLikelihoods(sequences, ref decision, create(sequences));
}
/// <summary>
/// Computes numerical scores measuring the association between
/// each of the given <paramref name="sequences" /> vectors and each
/// possible class.
/// </summary>
///
public override double[][][] Scores(TInput[][] sequences, double[][][] result)
{
return LogLikelihoods(sequences, result);
}
/// <summary>
/// Computes numerical scores measuring the association between
/// each of the given <paramref name="sequences" /> vectors and each
/// possible class.
/// </summary>
///
public override double[][][] Scores(TInput[][] sequences, ref int[][] decision, double[][][] result)
{
return LogLikelihoods(sequences, ref decision, result);
}
double ICovariantTransform<TInput[], double>.Transform(TInput[] input)
{
return LogLikelihood(input);
}
double[] ICovariantTransform<TInput[], double>.Transform(TInput[][] input)
{
return LogLikelihood(input);
}
/// <summary>
/// Applies the transformation to an input, producing an associated output.
/// </summary>
/// <param name="input">The input data to which the transformation should be applied.</param>
/// <param name="result"></param>
/// <returns>
/// The output generated by applying this transformation to the given input.
/// </returns>
public double[] Transform(TInput[][] input, double[] result)
{
return LogLikelihood(input, result);
}
}
}