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Neural Networks on C#

, 19 Nov 2006 GPL3
The articles describes a C# library for neural network computations, and their application for several problem solving.
neuro_demo.zip
neuro_demo
Back Propagation
Approximation
AForge.Controls.dll
AForge.dll
AForge.Neuro.dll
Approximation.exe
Data Samples
sample1.csv
sample2.csv
TimeSeries
AForge.Controls.dll
AForge.dll
AForge.Neuro.dll
Data Samples
exponent.csv
growing sinusoid.csv
parabola.csv
sigmoid.csv
sinusoid.csv
TimeSeries.exe
XORProblem
AForge.Controls.dll
AForge.dll
AForge.Neuro.dll
XORProblem.exe
Simple
Delta Rule Learning
AForge.Controls.dll
AForge.dll
AForge.Neuro.dll
Classifier.exe
Data Samples
and.csv
cube.csv
or.csv
sample1.csv
sample2.csv
One-Layer Perceptron Classifier
AForge.Controls.dll
AForge.dll
AForge.Neuro.dll
Classifier.exe
Data Samples
sample1.csv
sample2.csv
Perceptron Classifier
AForge.Controls.dll
AForge.dll
AForge.Neuro.dll
Classifier.exe
Data Samples
and.csv
cube.csv
or.csv
SOM
2DOrganizing
2DOrganizing.exe
AForge.dll
AForge.Neuro.dll
Color
AForge.dll
AForge.Neuro.dll
Color.exe
TSP
AForge.Controls.dll
AForge.dll
AForge.Neuro.dll
TSP.exe
neuro_src.zip
neuro_src
Docs
AForge.Core.chm
AForge.Neuro.chm
Release
AForge.Controls.dll
AForge.dll
AForge.Neuro.dll
Samples
Neuro
Back Propagation
Approximation
App.ico
Data Samples
sample1.csv
sample2.csv
TimeSeries
App.ico
Data Samples
exponent.csv
growing sinusoid.csv
parabola.csv
sigmoid.csv
sinusoid.csv
XORProblem
App.ico
Simple
Delta Rule Learning
App.ico
Data Samples
and.csv
cube.csv
or.csv
sample1.csv
sample2.csv
One-Layer Perceptron Classifier
App.ico
Data Samples
sample1.csv
sample2.csv
Perceptron Classifier
App.ico
Data Samples
and.csv
cube.csv
or.csv
SOM
2DOrganizing
App.ico
Color
App.ico
TSP
App.ico
Sources
Controls
Core
Neuro
Activation Functions
Images
sigmoid.bmp
sigmoid_bipolar.bmp
threshold.bmp
Layers
Learning
Networks
Neurons
// AForge Framework
// Perceptron Classifier
//
// Copyright � Andrew Kirillov, 2006
// andrew.kirillov@gmail.com
//

using System;
using System.Drawing;
using System.Collections;
using System.ComponentModel;
using System.Windows.Forms;
using System.Data;
using System.IO;
using System.Threading;

using AForge;
using AForge.Neuro;
using AForge.Neuro.Learning;
using AForge.Controls;

namespace Classifier
{
	/// <summary>
	/// Summary description for Form1.
	/// </summary>
	public class MainForm : System.Windows.Forms.Form
	{
		private System.Windows.Forms.GroupBox groupBox1;
		private System.Windows.Forms.ListView dataList;
		private System.Windows.Forms.Button loadButton;
		private System.Windows.Forms.OpenFileDialog openFileDialog;
		private AForge.Controls.Chart chart;
		private System.Windows.Forms.GroupBox groupBox2;
		private System.Windows.Forms.Label label1;
		private System.Windows.Forms.TextBox learningRateBox;
		private System.Windows.Forms.Button startButton;
		private System.Windows.Forms.Label noVisualizationLabel;
		private System.Windows.Forms.Label label2;
		private System.Windows.Forms.Label label3;
		private System.Windows.Forms.ListView weightsList;
		private System.Windows.Forms.ColumnHeader columnHeader1;
		private System.Windows.Forms.ColumnHeader columnHeader2;
		private System.Windows.Forms.Label label4;
		private System.Windows.Forms.TextBox iterationsBox;
		private System.Windows.Forms.Button stopButton;
		private System.Windows.Forms.Label label5;
		private AForge.Controls.Chart errorChart;
		private System.Windows.Forms.CheckBox saveFilesCheck;
		/// <summary>
		/// Required designer variable.
		/// </summary>
		private System.ComponentModel.Container components = null;

		private int			samples = 0;
		private int			variables = 0;
		private double[,]	data = null;
		private int[]		classes = null;

		private double		learningRate = 0.1;
		private bool		saveStatisticsToFiles = false;

		private Thread	workerThread = null;
		private bool	needToStop = false;

		// Constructor
		public MainForm( )
		{
			//
			// Required for Windows Form Designer support
			//
			InitializeComponent( );

			// initialize charts
			chart.AddDataSeries( "class1", Color.Red, Chart.SeriesType.Dots, 5 );
			chart.AddDataSeries( "class2", Color.Blue, Chart.SeriesType.Dots, 5 );
			chart.AddDataSeries( "classifier", Color.Gray, Chart.SeriesType.Line, 1, false );

			errorChart.AddDataSeries( "error", Color.Red, Chart.SeriesType.ConnectedDots, 3, false );

			// update some controls
			saveFilesCheck.Checked = saveStatisticsToFiles;
			UpdateSettings( );
		}

		/// <summary>
		/// Clean up any resources being used.
		/// </summary>
		protected override void Dispose( bool disposing )
		{
			if( disposing )
			{
				if (components != null) 
				{
					components.Dispose();
				}
			}
			base.Dispose( disposing );
		}

		#region Windows Form Designer generated code
		/// <summary>
		/// Required method for Designer support - do not modify
		/// the contents of this method with the code editor.
		/// </summary>
		private void InitializeComponent()
		{
			this.groupBox1 = new System.Windows.Forms.GroupBox();
			this.chart = new AForge.Controls.Chart();
			this.loadButton = new System.Windows.Forms.Button();
			this.dataList = new System.Windows.Forms.ListView();
			this.noVisualizationLabel = new System.Windows.Forms.Label();
			this.openFileDialog = new System.Windows.Forms.OpenFileDialog();
			this.groupBox2 = new System.Windows.Forms.GroupBox();
			this.errorChart = new AForge.Controls.Chart();
			this.label5 = new System.Windows.Forms.Label();
			this.stopButton = new System.Windows.Forms.Button();
			this.iterationsBox = new System.Windows.Forms.TextBox();
			this.label4 = new System.Windows.Forms.Label();
			this.weightsList = new System.Windows.Forms.ListView();
			this.columnHeader1 = new System.Windows.Forms.ColumnHeader();
			this.columnHeader2 = new System.Windows.Forms.ColumnHeader();
			this.label3 = new System.Windows.Forms.Label();
			this.label2 = new System.Windows.Forms.Label();
			this.startButton = new System.Windows.Forms.Button();
			this.learningRateBox = new System.Windows.Forms.TextBox();
			this.label1 = new System.Windows.Forms.Label();
			this.saveFilesCheck = new System.Windows.Forms.CheckBox();
			this.groupBox1.SuspendLayout();
			this.groupBox2.SuspendLayout();
			this.SuspendLayout();
			// 
			// groupBox1
			// 
			this.groupBox1.Controls.AddRange(new System.Windows.Forms.Control[] {
																					this.chart,
																					this.loadButton,
																					this.dataList,
																					this.noVisualizationLabel});
			this.groupBox1.Location = new System.Drawing.Point(10, 10);
			this.groupBox1.Name = "groupBox1";
			this.groupBox1.Size = new System.Drawing.Size(190, 420);
			this.groupBox1.TabIndex = 0;
			this.groupBox1.TabStop = false;
			this.groupBox1.Text = "Data";
			// 
			// chart
			// 
			this.chart.Location = new System.Drawing.Point(10, 215);
			this.chart.Name = "chart";
			this.chart.Size = new System.Drawing.Size(170, 170);
			this.chart.TabIndex = 2;
			this.chart.Text = "chart1";
			// 
			// loadButton
			// 
			this.loadButton.Location = new System.Drawing.Point(10, 390);
			this.loadButton.Name = "loadButton";
			this.loadButton.TabIndex = 1;
			this.loadButton.Text = "&Load";
			this.loadButton.Click += new System.EventHandler(this.loadButton_Click);
			// 
			// dataList
			// 
			this.dataList.FullRowSelect = true;
			this.dataList.GridLines = true;
			this.dataList.HeaderStyle = System.Windows.Forms.ColumnHeaderStyle.Nonclickable;
			this.dataList.Location = new System.Drawing.Point(10, 20);
			this.dataList.Name = "dataList";
			this.dataList.Size = new System.Drawing.Size(170, 190);
			this.dataList.TabIndex = 0;
			this.dataList.View = System.Windows.Forms.View.Details;
			// 
			// noVisualizationLabel
			// 
			this.noVisualizationLabel.Location = new System.Drawing.Point(10, 215);
			this.noVisualizationLabel.Name = "noVisualizationLabel";
			this.noVisualizationLabel.Size = new System.Drawing.Size(170, 170);
			this.noVisualizationLabel.TabIndex = 2;
			this.noVisualizationLabel.Text = "Visualization is not available.";
			this.noVisualizationLabel.TextAlign = System.Drawing.ContentAlignment.MiddleCenter;
			this.noVisualizationLabel.Visible = false;
			// 
			// openFileDialog
			// 
			this.openFileDialog.Filter = "CSV (Comma delimited) (*.csv)|*.csv";
			this.openFileDialog.Title = "Select data file";
			// 
			// groupBox2
			// 
			this.groupBox2.Controls.AddRange(new System.Windows.Forms.Control[] {
																					this.saveFilesCheck,
																					this.errorChart,
																					this.label5,
																					this.stopButton,
																					this.iterationsBox,
																					this.label4,
																					this.weightsList,
																					this.label3,
																					this.label2,
																					this.startButton,
																					this.learningRateBox,
																					this.label1});
			this.groupBox2.Location = new System.Drawing.Point(210, 10);
			this.groupBox2.Name = "groupBox2";
			this.groupBox2.Size = new System.Drawing.Size(240, 420);
			this.groupBox2.TabIndex = 1;
			this.groupBox2.TabStop = false;
			this.groupBox2.Text = "Training";
			// 
			// errorChart
			// 
			this.errorChart.Location = new System.Drawing.Point(10, 270);
			this.errorChart.Name = "errorChart";
			this.errorChart.Size = new System.Drawing.Size(220, 140);
			this.errorChart.TabIndex = 10;
			// 
			// label5
			// 
			this.label5.Location = new System.Drawing.Point(10, 250);
			this.label5.Name = "label5";
			this.label5.Size = new System.Drawing.Size(101, 15);
			this.label5.TabIndex = 9;
			this.label5.Text = "Error\'s dynamics:";
			// 
			// stopButton
			// 
			this.stopButton.Enabled = false;
			this.stopButton.Location = new System.Drawing.Point(155, 49);
			this.stopButton.Name = "stopButton";
			this.stopButton.TabIndex = 8;
			this.stopButton.Text = "S&top";
			this.stopButton.Click += new System.EventHandler(this.stopButton_Click);
			// 
			// iterationsBox
			// 
			this.iterationsBox.Location = new System.Drawing.Point(90, 50);
			this.iterationsBox.Name = "iterationsBox";
			this.iterationsBox.ReadOnly = true;
			this.iterationsBox.Size = new System.Drawing.Size(50, 20);
			this.iterationsBox.TabIndex = 7;
			this.iterationsBox.Text = "";
			// 
			// label4
			// 
			this.label4.Location = new System.Drawing.Point(10, 52);
			this.label4.Name = "label4";
			this.label4.Size = new System.Drawing.Size(65, 16);
			this.label4.TabIndex = 6;
			this.label4.Text = "Iterations:";
			// 
			// weightsList
			// 
			this.weightsList.Columns.AddRange(new System.Windows.Forms.ColumnHeader[] {
																						  this.columnHeader1,
																						  this.columnHeader2});
			this.weightsList.FullRowSelect = true;
			this.weightsList.GridLines = true;
			this.weightsList.HeaderStyle = System.Windows.Forms.ColumnHeaderStyle.Nonclickable;
			this.weightsList.Location = new System.Drawing.Point(10, 130);
			this.weightsList.Name = "weightsList";
			this.weightsList.Size = new System.Drawing.Size(220, 110);
			this.weightsList.TabIndex = 5;
			this.weightsList.View = System.Windows.Forms.View.Details;
			// 
			// columnHeader1
			// 
			this.columnHeader1.Text = "Weight";
			this.columnHeader1.Width = 70;
			// 
			// columnHeader2
			// 
			this.columnHeader2.Text = "Value";
			this.columnHeader2.Width = 100;
			// 
			// label3
			// 
			this.label3.Location = new System.Drawing.Point(10, 110);
			this.label3.Name = "label3";
			this.label3.Size = new System.Drawing.Size(112, 16);
			this.label3.TabIndex = 4;
			this.label3.Text = "Perceptron weights:";
			// 
			// label2
			// 
			this.label2.BorderStyle = System.Windows.Forms.BorderStyle.FixedSingle;
			this.label2.Location = new System.Drawing.Point(10, 100);
			this.label2.Name = "label2";
			this.label2.Size = new System.Drawing.Size(220, 2);
			this.label2.TabIndex = 3;
			// 
			// startButton
			// 
			this.startButton.Enabled = false;
			this.startButton.Location = new System.Drawing.Point(155, 19);
			this.startButton.Name = "startButton";
			this.startButton.TabIndex = 2;
			this.startButton.Text = "&Start";
			this.startButton.Click += new System.EventHandler(this.startButton_Click);
			// 
			// learningRateBox
			// 
			this.learningRateBox.Location = new System.Drawing.Point(90, 20);
			this.learningRateBox.Name = "learningRateBox";
			this.learningRateBox.Size = new System.Drawing.Size(50, 20);
			this.learningRateBox.TabIndex = 1;
			this.learningRateBox.Text = "";
			// 
			// label1
			// 
			this.label1.Location = new System.Drawing.Point(10, 22);
			this.label1.Name = "label1";
			this.label1.Size = new System.Drawing.Size(75, 16);
			this.label1.TabIndex = 0;
			this.label1.Text = "Learning rate:";
			// 
			// saveFilesCheck
			// 
			this.saveFilesCheck.Location = new System.Drawing.Point(10, 80);
			this.saveFilesCheck.Name = "saveFilesCheck";
			this.saveFilesCheck.Size = new System.Drawing.Size(182, 16);
			this.saveFilesCheck.TabIndex = 11;
			this.saveFilesCheck.Text = "Save weights and errors to files";
			// 
			// MainForm
			// 
			this.AutoScaleBaseSize = new System.Drawing.Size(5, 13);
			this.ClientSize = new System.Drawing.Size(459, 440);
			this.Controls.AddRange(new System.Windows.Forms.Control[] {
																		  this.groupBox2,
																		  this.groupBox1});
			this.FormBorderStyle = System.Windows.Forms.FormBorderStyle.FixedDialog;
			this.MaximizeBox = false;
			this.Name = "MainForm";
			this.Text = "Perceptron Classifier";
			this.Closing += new System.ComponentModel.CancelEventHandler(this.MainForm_Closing);
			this.groupBox1.ResumeLayout(false);
			this.groupBox2.ResumeLayout(false);
			this.ResumeLayout(false);

		}
		#endregion

		/// <summary>
		/// The main entry point for the application.
		/// </summary>
		[STAThread]
		static void Main( ) 
		{
			Application.Run( new MainForm( ) );
		}

		// On main form closing
		private void MainForm_Closing(object sender, System.ComponentModel.CancelEventArgs e)
		{
			// check if worker thread is running
			if ( ( workerThread != null ) && ( workerThread.IsAlive ) )
			{
				needToStop = true;
				workerThread.Join( );
			}
		}

		// On "Load" button click - load data
		private void loadButton_Click( object sender, System.EventArgs e )
		{
			// data file format:
			// X1, X2, ... Xn, class (0|1)

			// show file selection dialog
			if ( openFileDialog.ShowDialog( ) == DialogResult.OK )
			{
				StreamReader reader = null;

				// temp buffers (for 50 samples only)
				double[,]	tempData = null;
				int[]		tempClasses = new int[50];

				// min and max X values
				double minX = double.MaxValue;
				double maxX = double.MinValue;

				// samples count
				samples = 0;

				try
				{
					string	str = null;

					// open selected file
					reader = File.OpenText( openFileDialog.FileName );

					// read the data
					while ( ( samples < 50 ) && ( ( str = reader.ReadLine( ) ) != null ) )
					{
						// split the string
						string[] strs = str.Split( ';' );
						if ( strs.Length == 1 )
							strs = str.Split( ',' );

						// allocate data array
						if ( samples == 0 )
						{
							variables = strs.Length - 1;
							tempData = new double[50, variables];
						}

						// parse data
						for ( int j = 0; j < variables; j++ )
						{
							tempData[samples, j] = double.Parse( strs[j] );
						}
						tempClasses[samples] = int.Parse( strs[variables] );

						// search for min value
						if ( tempData[samples, 0] < minX )
							minX = tempData[samples, 0];
						// search for max value
						if ( tempData[samples, 0] > maxX )
							maxX = tempData[samples, 0];

						samples++;
					}

					// allocate and set data
					data = new double[samples, variables];
					Array.Copy( tempData, 0, data, 0, samples * variables );
					classes = new int[samples];
					Array.Copy( tempClasses, 0, classes, 0, samples );

					// clear current result
					ClearCurrentSolution( );
				}
				catch ( Exception )
				{
					MessageBox.Show( "Failed reading the file", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error );
					return;
				}
				finally
				{
					// close file
					if ( reader != null )
						reader.Close( );
				}

				// update list and chart
				UpdateDataListView( );

				// show chart or not
				bool showChart = ( variables == 2 );

				if ( showChart )
				{
					chart.RangeX = new DoubleRange( minX, maxX );
					ShowTrainingData( );
				}

				chart.Visible = showChart;
				noVisualizationLabel.Visible = !showChart;

				// enable start button
				startButton.Enabled = true;
			}
		}

		// Update settings controls
		private void UpdateSettings( )
		{
			learningRateBox.Text = learningRate.ToString( );
		}

		// Update data in list view
		private void UpdateDataListView( )
		{
			// remove all curent data and columns
			dataList.Items.Clear( );
			dataList.Columns.Clear( );

			// add columns
			for ( int i = 0, n = variables; i < n; i++ )
			{
				dataList.Columns.Add( string.Format( "X{0}", i + 1 ),
					50, HorizontalAlignment.Left );
			}
			dataList.Columns.Add( "Class", 50, HorizontalAlignment.Left );

			// add items
			for ( int i = 0; i < samples; i++ )
			{
				dataList.Items.Add( data[i, 0].ToString( ) );

				for ( int j = 1; j < variables; j++ )
				{
					dataList.Items[i].SubItems.Add( data[i, j].ToString( ) );
				}
				dataList.Items[i].SubItems.Add( classes[i].ToString( ) );
			}
		}

		// Show training data on chart
		private void ShowTrainingData( )
		{
			int class1Size = 0;
			int class2Size = 0;

			// calculate number of samples in each class
			for ( int i = 0, n = samples; i < n; i++ )
			{
				if ( classes[i] == 0 )
					class1Size++;
				else
					class2Size++;
			}

			// allocate classes arrays
			double[,] class1 = new double[class1Size, 2];
			double[,] class2 = new double[class2Size, 2];

			// fill classes arrays
			for ( int i = 0, c1 = 0, c2 = 0; i < samples; i++ )
			{
				if ( classes[i] == 0 )
				{
					// class 1
					class1[c1, 0] = data[i, 0];
					class1[c1, 1] = data[i, 1];
					c1++;
				}
				else
				{
					// class 2
					class2[c2, 0] = data[i, 0];
					class2[c2, 1] = data[i, 1];
					c2++;
				}
			}

			// updata chart control
			chart.UpdateDataSeries( "class1", class1 );
			chart.UpdateDataSeries( "class2", class2 );
		}

		// Enable/disale controls
		private void EnableControls( bool enable )
		{
			learningRateBox.Enabled	= enable;
			loadButton.Enabled		= enable;
			startButton.Enabled		= enable;
			saveFilesCheck.Enabled	= enable;
			stopButton.Enabled		= !enable;
		}

		// Clear current solution
		private void ClearCurrentSolution( )
		{
			chart.UpdateDataSeries( "classifier", null );
			errorChart.UpdateDataSeries( "error", null );
			weightsList.Items.Clear( );
		}

		// On button "Start" - start learning procedure
		private void startButton_Click(object sender, System.EventArgs e)
		{
			// get learning rate
			try
			{
				learningRate = Math.Max( 0.00001, Math.Min( 1, double.Parse( learningRateBox.Text ) ) );
			}
			catch
			{
				learningRate = 0.1;
			}
			saveStatisticsToFiles = saveFilesCheck.Checked;

			// update settings controls
			UpdateSettings( );

			// disable all settings controls
			EnableControls( false );

			// run worker thread
			needToStop = false;
			workerThread = new Thread( new ThreadStart( SearchSolution ) );
			workerThread.Start( );
		}

		// On button "Stop" - stop learning procedure
		private void stopButton_Click(object sender, System.EventArgs e)
		{
			// stop worker thread
			needToStop = true;
			workerThread.Join( );
			workerThread = null;
		}

		// Worker thread
		void SearchSolution( )
		{
			// prepare learning data
			double[][] input = new double[samples][];
			double[][] output = new double[samples][];

			for ( int i = 0; i < samples; i++ )
			{
				input[i] = new double[variables];
				output[i] = new double[1];

				// copy input
				for ( int j = 0; j < variables; j++ )
					input[i][j] = data[i, j];
				// copy output
				output[i][0] = classes[i];
			}

			// create perceptron
			ActivationNetwork	network = new ActivationNetwork( new ThresholdFunction( ), variables, 1 );
			ActivationNeuron	neuron = network[0][0];
			// create teacher
			PerceptronLearning teacher = new PerceptronLearning( network );
			// set learning rate
			teacher.LearningRate = learningRate;

			// iterations
			int iteration = 1;

			// statistic files
			StreamWriter errorsFile = null;
			StreamWriter weightsFile = null;

			try
			{
				// check if we need to save statistics to files
				if ( saveStatisticsToFiles )
				{
					// open files
					errorsFile	= File.CreateText( "errors.csv" );
					weightsFile	= File.CreateText( "weights.csv" );
				}

				// erros list
				ArrayList errorsList = new ArrayList( );

				// loop
				while ( !needToStop )
				{
					// save current weights
					if ( weightsFile != null )
					{
						for ( int i = 0; i < variables; i++ )
						{
							weightsFile.Write( neuron[i] + ";" );
						}
						weightsFile.WriteLine( neuron.Threshold );
					}

					// run epoch of learning procedure
					double error = teacher.RunEpoch( input, output );
					errorsList.Add( error );

					// show current iteration
					iterationsBox.Text = iteration.ToString( );

					// save current error
					if ( errorsFile != null )
					{
						errorsFile.WriteLine( error );
					}				

					// show classifier in the case of 2 dimensional data
					if ( ( neuron.InputsCount == 2 ) && ( neuron[1] != 0 ) )
					{
						double k = - neuron[0] / neuron[1];
						double b = - neuron.Threshold / neuron[1];

						double[,] classifier = new double[2, 2] {
							{ chart.RangeX.Min, chart.RangeX.Min * k + b },
							{ chart.RangeX.Max, chart.RangeX.Max * k + b }
																};
						// update chart
						chart.UpdateDataSeries( "classifier", classifier );
					}

					// stop if no error
					if ( error == 0 )
						break;

					iteration++;
				}

				// show perceptron's weights
				weightsList.Items.Clear( );
				for ( int i = 0; i < variables; i++ )
				{
					weightsList.Items.Add( string.Format( "Weight {0}", i + 1  ) );
					weightsList.Items[i].SubItems.Add( neuron[i].ToString( "F6" ) );
				}
				weightsList.Items.Add( "Threshold" );
				weightsList.Items[variables].SubItems.Add( neuron.Threshold.ToString( "F6" ) );

				// show error's dynamics
				double[,] errors = new double[errorsList.Count, 2];

				for ( int i = 0, n = errorsList.Count; i < n; i++ )
				{
					errors[i, 0] = i;
					errors[i, 1] = (double) errorsList[i];
				}

				errorChart.RangeX = new DoubleRange( 0, errorsList.Count - 1 );
				errorChart.RangeY = new DoubleRange( 0, samples );
				errorChart.UpdateDataSeries( "error", errors );
			}
			catch ( IOException )
			{
				MessageBox.Show( "Failed writing file", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error );
			}
			finally
			{
				// close files
				if ( errorsFile != null )
					errorsFile.Close( );
				if ( weightsFile != null )
					weightsFile.Close( );
			}

			// enable settings controls
			EnableControls( true );
		}
	}
}

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About the Author

Andrew Kirillov
Software Developer (Senior) Cisco Systems
United Kingdom United Kingdom
Started software development at about 15 years old and it seems like now it lasts most part of my life. Fortunately did not spend too much time with Z80 and BK0010 and switched to 8086 and further. Similar with programming languages – luckily managed to get away from BASIC and Pascal to things like Assembler, C, C++ and then C#. Apart from daily programming for food, do it also for hobby, where mostly enjoy areas like Computer Vision, Robotics and AI. This led to some open source stuff like AForge.NET.
 
Going out of computers I am just a man loving his family, enjoying traveling, a bit of books, a bit of movies and a mixture of everything else. Always wanted to learn playing guitar, but it seems like 6 strings are much harder than few dozens of keyboard’s keys. Will keep progressing ...

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