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

namespace AForge.Neuro
{
	using System;

	/// <summary>
	/// Activation neuron
	/// </summary>
	/// 
	/// <remarks>Activation neuron computes weighted sum of its inputs, adds
	/// threshold value and then applies activation function. The neuron is
	/// usually used in multi-layer neural networks.</remarks>
	/// 
	public class ActivationNeuron : Neuron
	{
		/// <summary>
		/// Threshold value
		/// </summary>
		/// 
		/// <remarks>The value is added to inputs weighted sum.</remarks>
		/// 
		protected double threshold = 0.0f;

		/// <summary>
		/// Activation function
		/// </summary>
		/// 
		/// <remarks>The function is applied to inputs weighted sum plus
		/// threshold value.</remarks>
		/// 
		protected IActivationFunction function = null;

		/// <summary>
		/// Threshold value
		/// </summary>
		/// 
		/// <remarks>The value is added to inputs weighted sum.</remarks>
		/// 
		public double Threshold
		{
			get { return threshold; }
			set { threshold = value; }
		}

		/// <summary>
		/// Neuron's activation function
		/// </summary>
		/// 
		public IActivationFunction ActivationFunction
		{
			get { return function; }
		}
		
		/// <summary>
		/// Initializes a new instance of the <see cref="ActivationNeuron"/> class
		/// </summary>
		/// 
		/// <param name="inputs">Neuron's inputs count</param>
		/// <param name="function">Neuron's activation function</param>
		/// 
		public ActivationNeuron( int inputs, IActivationFunction function ) : base( inputs )
		{
			this.function = function;
		}

		/// <summary>
		/// Randomize neuron 
		/// </summary>
		/// 
		/// <remarks>Calls base class <see cref="Neuron.Randomize">Randomize</see> method
		/// to randomize neuron's weights and then randomize threshold's value.</remarks>
		/// 
		public override void Randomize( )
		{
			// randomize weights
			base.Randomize( );
			// randomize threshold
			threshold = rand.NextDouble( ) * ( randRange.Length ) + randRange.Min;
		}

		/// <summary>
		/// Computes output value of neuron
		/// </summary>
		/// 
		/// <param name="input">Input vector</param>
		/// 
		/// <returns>Returns neuron's output value</returns>
		/// 
		/// <remarks>The output value of activation neuron is equal to value
		/// of nueron's activation function, which parameter is weighted sum
		/// of its inputs plus threshold value. The output value is also stored
		/// in <see cref="Neuron.Output">Output</see> property.</remarks>
		/// 
		public override double Compute( double[] input )
		{
			// check for corrent input vector
			if ( input.Length != inputsCount )
				throw new ArgumentException( );

			// initial sum value
			double sum = 0.0;

			// compute weighted sum of inputs
			for ( int i = 0; i < inputsCount; i++ )
			{
				sum += weights[i] * input[i];
			}
			sum += threshold;

			return ( output = function.Function( sum ) );
		}
	}
}

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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 (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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