GH-167: Posterior method has wrong signature in continuous HMMs.

Sources/Accord.Statistics/Models/Markov/Base/IHiddenMarkovModel.cs

@@ -1,96 +1,151 @@
-// Accord Statistics Library
-// The Accord.NET Framework
-// http://accord-framework.net
-//
-// Copyright © César Souza, 2009-2016
-// 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.Statistics.Models.Markov
-{
-    using System;
-
-    /// <summary>
-    ///   Common interface for Hidden Markov Models.
-    /// </summary>
-    /// 
-    public interface IHiddenMarkovModel
-    {
-
-        /// <summary>
-        ///   Calculates the most likely sequence of hidden states
-        ///   that produced the given observation sequence.
-        /// </summary>
-        /// <remarks>
-        ///   Decoding problem. Given the HMM M = (A, B, pi) and  the observation sequence 
-        ///   O = {o1,o2, ..., oK}, calculate the most likely sequence of hidden states Si
-        ///   that produced this observation sequence O. This can be computed efficiently
-        ///   using the Viterbi algorithm.
-        /// </remarks>
-        /// <param name="observations">
-        ///   A sequence of observations.</param>
-        /// <param name="logLikelihood">
-        ///   The state optimized probability.</param>
-        /// <returns>
-        ///   The sequence of states that most likely produced the sequence.
-        /// </returns>
-        /// 
-        int[] Decode(Array observations, out double logLikelihood);
-
-        /// <summary>
-        ///   Calculates the probability that this model has generated the given sequence.
-        /// </summary>
-        /// <remarks>
-        ///   Evaluation problem. Given the HMM  M = (A, B, pi) and  the observation
-        ///   sequence O = {o1, o2, ..., oK}, calculate the probability that model
-        ///   M has generated sequence O. This can be computed efficiently using the
-        ///   Forward algorithm. </remarks>
-        /// <param name="observations">
-        ///   A sequence of observations. </param>
-        /// <returns>
-        ///   The probability that the given sequence has been generated by this model.
-        /// </returns>
-        /// 
-        double Evaluate(Array observations);
-
-        /// <summary>
-        ///   Gets the number of states of this model.
-        /// </summary>
-        /// 
-        int States { get; }
-
-        /// <summary>
-        ///   Gets the initial probabilities for this model.
-        /// </summary>
-        /// 
-        double[] Probabilities { get; }
-
-        /// <summary>
-        ///   Gets the Transition matrix (A) for this model.
-        /// </summary>
-        /// 
-        double[,] Transitions { get; }
-
-        /// <summary>
-        ///   Gets or sets a user-defined tag.
-        /// </summary>
-        /// 
-        object Tag { get; set; }
-
-    }
-
-}
+// Accord Statistics Library
+// The Accord.NET Framework
+// http://accord-framework.net
+//
+// Copyright © César Souza, 2009-2016
+// 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.Statistics.Models.Markov
+{
+    using System;
+
+    /// <summary>
+    ///   Common interface for Hidden Markov Models.
+    /// </summary>
+    /// 
+    public interface IHiddenMarkovModel
+    {
+
+        /// <summary>
+        ///   Calculates the most likely sequence of hidden states
+        ///   that produced the given observation sequence.
+        /// </summary>
+        /// <remarks>
+        ///   Decoding problem. Given the HMM M = (A, B, pi) and  the observation sequence 
+        ///   O = {o1,o2, ..., oK}, calculate the most likely sequence of hidden states Si
+        ///   that produced this observation sequence O. This can be computed efficiently
+        ///   using the Viterbi algorithm.
+        /// </remarks>
+        /// <param name="observations">
+        ///   A sequence of observations.</param>
+        /// <param name="logLikelihood">
+        ///   The state optimized probability.</param>
+        /// <returns>
+        ///   The sequence of states that most likely produced the sequence.
+        /// </returns>
+        /// 
+        int[] Decode(Array observations, out double logLikelihood);
+
+        /// <summary>
+        ///   Calculates the probability that this model has generated the given sequence.
+        /// </summary>
+        /// <remarks>
+        ///   Evaluation problem. Given the HMM  M = (A, B, pi) and  the observation
+        ///   sequence O = {o1, o2, ..., oK}, calculate the probability that model
+        ///   M has generated sequence O. This can be computed efficiently using the
+        ///   Forward algorithm. </remarks>
+        /// <param name="observations">
+        ///   A sequence of observations. </param>
+        /// <returns>
+        ///   The probability that the given sequence has been generated by this model.
+        /// </returns>
+        /// 
+        double Evaluate(Array observations);
+
+        /// <summary>
+        ///   Gets the number of states of this model.
+        /// </summary>
+        /// 
+        int States { get; }
+
+        /// <summary>
+        ///   Gets the initial probabilities for this model.
+        /// </summary>
+        /// 
+        double[] Probabilities { get; }
+
+        /// <summary>
+        ///   Gets the Transition matrix (A) for this model.
+        /// </summary>
+        /// 
+        double[,] Transitions { get; }
+
+        /// <summary>
+        ///   Gets or sets a user-defined tag.
+        /// </summary>
+        /// 
+        object Tag { get; set; }
+
+
+
+        /// <summary>
+        ///   Calculates the probability of each hidden state for each
+        ///   observation in the observation vector.
+        /// </summary>
+        /// 
+        /// <remarks>
+        ///   If there are 3 states in the model, and the <paramref name="observations"/>
+        ///   array contains 5 elements, the resulting vector will contain 5 vectors of
+        ///   size 3 each. Each vector of size 3 will contain probability values that sum
+        ///   up to one. By following those probabilities in order, we may decode those
+        ///   probabilities into a sequence of most likely states. However, the sequence
+        ///   of obtained states may not be valid in the model.
+        /// </remarks>
+        /// 
+        /// <param name="observations">A sequence of observations.</param>
+        /// 
+        /// <returns>A vector of the same size as the observation vectors, containing
+        ///  the probabilities for each state in the model for the current observation.
+        ///  If there are 3 states in the model, and the <paramref name="observations"/>
+        ///  array contains 5 elements, the resulting vector will contain 5 vectors of
+        ///  size 3 each. Each vector of size 3 will contain probability values that sum
+        ///  up to one.</returns>
+        /// 
+        double[][] Posterior(Array observations);
+
+        /// <summary>
+        ///   Calculates the probability of each hidden state for each observation 
+        ///   in the observation vector, and uses those probabilities to decode the
+        ///   most likely sequence of states for each observation in the sequence 
+        ///   using the posterior decoding method. See remarks for details.
+        /// </summary>
+        /// 
+        /// <remarks>
+        ///   If there are 3 states in the model, and the <paramref name="observations"/>
+        ///   array contains 5 elements, the resulting vector will contain 5 vectors of
+        ///   size 3 each. Each vector of size 3 will contain probability values that sum
+        ///   up to one. By following those probabilities in order, we may decode those
+        ///   probabilities into a sequence of most likely states. However, the sequence
+        ///   of obtained states may not be valid in the model.
+        /// </remarks>
+        /// 
+        /// <param name="observations">A sequence of observations.</param>
+        /// <param name="path">The sequence of states most likely associated with each
+        ///   observation, estimated using the posterior decoding method.</param>
+        /// 
+        /// <returns>A vector of the same size as the observation vectors, containing
+        ///  the probabilities for each state in the model for the current observation.
+        ///  If there are 3 states in the model, and the <paramref name="observations"/>
+        ///  array contains 5 elements, the resulting vector will contain 5 vectors of
+        ///  size 3 each. Each vector of size 3 will contain probability values that sum
+        ///  up to one.</returns>
+        /// 
+        double[][] Posterior(Array observations, out int[] path);
+    }
+
+}

Sources/Accord.Statistics/Models/Markov/HiddenMarkovModel`1.cs

@@ -525,7 +525,7 @@ public double[][] Posterior(Array observations)
         ///  size 3 each. Each vector of size 3 will contain probability values that sum
         ///  up to one.</returns>
         /// 
-        public double[][] Posterior(int[] observations, out int[] path)
+        public double[][] Posterior(Array observations, out int[] path)
         {
             double[][] probabilities = Posterior(observations);