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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
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
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 Neural Net Library
//
// Copyright � Andrew Kirillov, 2005-2006
// andrew.kirillov@gmail.com
//

namespace AForge.Neuro
{
	using System;

	/// <summary>
	/// Base neural network class
	/// </summary>
	/// 
	/// <remarks>This is a base neural netwok class, which represents
	/// collection of neuron's layers.</remarks>
	/// 
	public abstract class Network
	{
		/// <summary>
		/// Network's inputs count
		/// </summary>
		protected int	inputsCount;

		/// <summary>
		/// Network's layers count
		/// </summary>
		protected int	layersCount;

		/// <summary>
		/// Network's layers
		/// </summary>
		protected Layer[]	layers;

		/// <summary>
		/// Network's output vector
		/// </summary>
		protected double[]	output;

		/// <summary>
		/// Network's inputs count
		/// </summary>
		public int InputsCount
		{
			get { return inputsCount; }
		}

		/// <summary>
		/// Network's layers count
		/// </summary>
		public int LayersCount
		{
			get { return layersCount; }
		}

		/// <summary>
		/// Network's output vector
		/// </summary>
		/// 
		/// <remarks>The calculation way of network's output vector is determined by
		/// inherited class.</remarks>
		/// 
		public double[] Output
		{
			get { return output; }
		}

		/// <summary>
		/// Network's layers accessor
		/// </summary>
		/// 
		/// <param name="index">Layer index</param>
		/// 
		/// <remarks>Allows to access network's layer.</remarks>
		/// 
		public Layer this[int index]
		{
			get { return layers[index]; }
		}


		/// <summary>
		/// Initializes a new instance of the <see cref="Network"/> class
		/// </summary>
		/// 
		/// <param name="inputsCount">Network's inputs count</param>
		/// <param name="layersCount">Network's layers count</param>
		/// 
		/// <remarks>Protected constructor, which initializes <see cref="inputsCount"/>,
		/// <see cref="layersCount"/> and <see cref="layers"/> members.</remarks>
		/// 
		protected Network( int inputsCount, int layersCount )
		{
			this.inputsCount = Math.Max( 1, inputsCount );
			this.layersCount = Math.Max( 1, layersCount );
			// create collection of layers
			layers = new Layer[this.layersCount];
		}

		/// <summary>
		/// Compute output vector of the network
		/// </summary>
		/// 
		/// <param name="input">Input vector</param>
		/// 
		/// <returns>Returns network's output vector</returns>
		/// 
		/// <remarks>The actual network's output vecor is determined by inherited class and it
		/// represents an output vector of the last layer of the network. The output vector is
		/// also stored in <see cref="Output"/> property.</remarks>
		/// 
		public virtual double[] Compute( double[] input )
		{
			output = input;

			// compute each layer
			foreach ( Layer layer in layers )
			{
				output = layer.Compute( output );
			}

			return output;
		}
		
		/// <summary>
		/// Randomize layers of the network
		/// </summary>
		/// 
		/// <remarks>Randomizes network's layers by calling <see cref="Layer.Randomize"/> method
		/// of each layer.</remarks>
		/// 
		public virtual void Randomize( )
		{
			foreach ( Layer layer in layers )
			{
				layer.Randomize();
			}
		}
	}
}

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This article, along with any associated source code and files, is licensed under The GNU General Public License (GPLv3)

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About the Author

Andrew Kirillov
Software Developer IBM
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 and not so open Computer Vision Sandbox.

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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