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Sequence Classifiers in C# - Part I: Hidden Markov Models

, 3 Dec 2014 CPOL
Let's understand hidden Markov models before taking a step into hidden conditional random fields.
Resources
Accord.dll
Accord.MachineLearning.dll
Accord.Math.dll
Accord.Statistics.dll
AForge.dll
AForge.Math.dll
Gestures.exe
Gestures.vshost.exe
Gestures.vshost.exe.manifest
Externals
Accord.Controls.dll
Accord.dll
Accord.MachineLearning.dll
Accord.Math.dll
Accord.Statistics.dll
AForge.dll
AForge.Math.dll
Native
Properties
Gestures.csproj.user
Gestures.pidb
Gestures.suo
// Accord.NET Sample Applications
// http://accord.googlecode.com
//
// Copyright © César Souza, 2009-2013
// 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
//

using System;
using System.ComponentModel;
using System.Drawing;
using System.IO;
using System.Linq;
using System.Windows.Forms;

using Accord.Statistics.Distributions.Fitting;
using Accord.Statistics.Distributions.Multivariate;

using Accord.Statistics.Models.Fields;
using Accord.Statistics.Models.Fields.Functions;
using Accord.Statistics.Models.Fields.Learning;

using Accord.Statistics.Models.Markov;
using Accord.Statistics.Models.Markov.Learning;
using Accord.Statistics.Models.Markov.Topology;
using Gestures.Native;

namespace Gestures
{
    public partial class MainForm : Form
    {

        private Database database;
        private HiddenMarkovClassifier<MultivariateNormalDistribution> hmm;
        private HiddenConditionalRandomField<double[]> hcrf;


        public MainForm()
        {
            InitializeComponent();

            database = new Database();
            gridSamples.AutoGenerateColumns = false;
            cbClasses.DataSource = database.Classes;
            gridSamples.DataSource = database.Samples;
            openDataDialog.InitialDirectory = Path.Combine(Application.StartupPath, "Resources");
        }



        private void btnLearnHMM_Click(object sender, EventArgs e)
        {
            if (gridSamples.Rows.Count == 0)
            {
                MessageBox.Show("Please load or insert some data first.");
                return;
            }

            BindingList<Sequence> samples = database.Samples;
            BindingList<String> classes = database.Classes;

            double[][][] inputs = new double[samples.Count][][];
            int[] outputs = new int[samples.Count];

            for (int i = 0; i < inputs.Length; i++)
            {
                inputs[i] = samples[i].Input;
                outputs[i] = samples[i].Output;
            }

            int states = 5;
            int iterations = 0;
            double tolerance = 0.01;
            bool rejection = false;


            hmm = new HiddenMarkovClassifier<MultivariateNormalDistribution>(classes.Count,
                new Forward(states), new MultivariateNormalDistribution(2), classes.ToArray());


            // Create the learning algorithm for the ensemble classifier
            var teacher = new HiddenMarkovClassifierLearning<MultivariateNormalDistribution>(hmm,

                // Train each model using the selected convergence criteria
                i => new BaumWelchLearning<MultivariateNormalDistribution>(hmm.Models[i])
                {
                    Tolerance = tolerance,
                    Iterations = iterations,

                    FittingOptions = new NormalOptions()
                    {
                        Regularization = 1e-5
                    }
                }
            );

            teacher.Empirical = true;
            teacher.Rejection = rejection;


            // Run the learning algorithm
            double error = teacher.Run(inputs, outputs);


            // Classify all training instances
            foreach (var sample in database.Samples)
            {
                sample.RecognizedAs = hmm.Compute(sample.Input);
            }

            foreach (DataGridViewRow row in gridSamples.Rows)
            {
                var sample = row.DataBoundItem as Sequence;
                row.DefaultCellStyle.BackColor = (sample.RecognizedAs == sample.Output) ?
                    Color.LightGreen : Color.White;
            }

            btnLearnHCRF.Enabled = true;
        }

        private void btnLearnHCRF_Click(object sender, EventArgs e)
        {
            if (gridSamples.Rows.Count == 0)
            {
                MessageBox.Show("Please load or insert some data first.");
                return;
            }

            var samples = database.Samples;
            var classes = database.Classes;

            double[][][] inputs = new double[samples.Count][][];
            int[] outputs = new int[samples.Count];

            for (int i = 0; i < inputs.Length; i++)
            {
                inputs[i] = samples[i].Input;
                outputs[i] = samples[i].Output;
            }

            int iterations = 100;
            double tolerance = 0.01;


            hcrf = new HiddenConditionalRandomField<double[]>(
                new MarkovMultivariateFunction(hmm));


            // Create the learning algorithm for the ensemble classifier
            var teacher = new HiddenResilientGradientLearning<double[]>(hcrf)
            {
                Iterations = iterations,
                Tolerance = tolerance
            };


            // Run the learning algorithm
            double error = teacher.Run(inputs, outputs);


            foreach (var sample in database.Samples)
            {
                sample.RecognizedAs = hcrf.Compute(sample.Input);
            }

            foreach (DataGridViewRow row in gridSamples.Rows)
            {
                var sample = row.DataBoundItem as Sequence;
                row.DefaultCellStyle.BackColor = (sample.RecognizedAs == sample.Output) ?
                    Color.LightGreen : Color.White;
            }
        }



        // Load and save database methods
        private void openDataStripMenuItem_Click(object sender, EventArgs e)
        {
            openDataDialog.ShowDialog();
        }

        private void saveDataStripMenuItem_Click(object sender, EventArgs e)
        {
            saveDataDialog.ShowDialog();
        }

        private void openDataDialog_FileOk(object sender, System.ComponentModel.CancelEventArgs e)
        {
            hmm = null;
            hcrf = null;

            using (var stream = openDataDialog.OpenFile())
                database.Load(stream);

            btnLearnHMM.Enabled = true;
            btnLearnHCRF.Enabled = false;

            panelClassification.Visible = false;
            panelUserLabeling.Visible = false;
        }

        private void saveDataDialog_FileOk(object sender, CancelEventArgs e)
        {
            using (var stream = saveDataDialog.OpenFile())
                database.Save(stream);
        }

        private void btnFile_MouseDown(object sender, MouseEventArgs e)
        {
            menuFile.Show(button4, button4.PointToClient(Cursor.Position));
        }



        // Top user interaction panel box events
        private void btnYes_Click(object sender, EventArgs e)
        {
            addGesture();
        }

        private void btnNo_Click(object sender, EventArgs e)
        {
            panelClassification.Visible = false;
            panelUserLabeling.Visible = true;
        }


        // Bottom user interaction panel box events
        private void btnClear_Click(object sender, EventArgs e)
        {
            canvas.Clear();
            panelUserLabeling.Visible = false;
        }

        private void btnInsert_Click(object sender, EventArgs e)
        {
            addGesture();
        }

        private void addGesture()
        {
            string selectedItem = cbClasses.SelectedItem as String;
            string classLabel = String.IsNullOrEmpty(selectedItem) ?
                cbClasses.Text : selectedItem;

            if (database.Add(canvas.GetSequence(), classLabel) != null)
            {
                canvas.Clear();

                if (database.Classes.Count >= 2 &&
                    database.SamplesPerClass() >= 3)
                    btnLearnHMM.Enabled = true;

                panelUserLabeling.Visible = false;
            }
        }


        // Canvas events
        private void inputCanvas_MouseUp(object sender, MouseEventArgs e)
        {
            double[][] input = Sequence.Preprocess(canvas.GetSequence());

            if (input.Length < 5)
            {
                panelUserLabeling.Visible = false;
                panelClassification.Visible = false;
                return;
            }

            if (hmm == null && hcrf == null)
            {
                panelUserLabeling.Visible = true;
                panelClassification.Visible = false;
            }

            else
            {
                int index = (hcrf != null) ?
                    hcrf.Compute(input) : hmm.Compute(input);

                string label = database.Classes[index];
                lbHaveYouDrawn.Text = String.Format("Have you drawn a {0}?", label);
                panelClassification.Visible = true;
                panelUserLabeling.Visible = false;
            }
        }

        private void canvas_MouseDown(object sender, MouseEventArgs e)
        {
            lbIdle.Visible = false;
        }




        // Aero Glass settings
        //
        private void MainForm_Load(object sender, EventArgs e)
        {
            // Perform special processing to enable aero
            if (SafeNativeMethods.IsAeroEnabled)
            {
                ThemeMargins margins = new ThemeMargins();
                margins.TopHeight = canvas.Top;
                margins.LeftWidth = canvas.Left;
                margins.RightWidth = ClientRectangle.Right - gridSamples.Right;
                margins.BottomHeight = ClientRectangle.Bottom - canvas.Bottom;

                // Extend the Frame into client area
                SafeNativeMethods.ExtendAeroGlassIntoClientArea(this, margins);
            }
        }

        /// <summary>
        ///   Paints the background of the control.
        /// </summary>
        protected override void OnPaintBackground(PaintEventArgs e)
        {
            base.OnPaintBackground(e);

            if (SafeNativeMethods.IsAeroEnabled)
            {
                // paint background black to enable include glass regions
                e.Graphics.Clear(Color.FromArgb(0, this.BackColor));
            }
        }
    }
}

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This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)

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About the Author

César de Souza
Engineer Xerox Research Center Europe
Brazil Brazil
Computer and technology enthusiast, interested in artificial intelligence and image processing. Has a Master's degree on Computer Science specialized on Image and Signal Processing, with expertise on Machine Learning, Computer Vision, Pattern Recognition and Data Mining systems. Author of the Accord.NET Framework for developing scientific computing applications.

If you would like to hire good developers to build your dream application, please check out DaitanGroup, one of the top outsourcing companies in Brazil. This company, located in Brazil's Sillicon Valley but with US-based offices, has huge experience developing telecommunications software for large and small companies worldwide.

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