Index

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F
	FANN C#AccessorEnumerator<T>
	FANN C#ActivationFunction enumerator, FANNCSharp
	FANN C#ArrayAccessor double
	FANN C#ArrayAccessor float
	FANN C#ArrayAccessor int
	FANN C#Connection double
	FANN C#Connection float
	FANN C#Connection int
	FANN C#DataAccessor double
	FANN C#DataAccessor float
	FANN C#DataAccessor int
	FANN C#ErrorFunction enumerator, FANNCSharp
	FANN C#FannFile class
	FANN C#IAccessor<T>Interface
	FANN C#NetworkType enumerator, FANNCSharp
	FANN C#NeuralNet Double
	FANN C#NeuralNet Fixed
	FANN C#NeuralNet Float
	FANN C#StopFunction enumerator, FANNCSharp
	FANN C#TrainingAlgorithm enumerator, FANNCSharp
	FANN C#TrainingData Double
	FANN C#TrainingData Fixed
	FANN C#TrainingData Float
	FANN_COS_SYMMETRIC, FANNCSharp.ActivationFunction
	FANN_ELLIOT, FANNCSharp.ActivationFunction
	FANN_ELLIOT_SYMMETRIC, FANNCSharp.ActivationFunction
	FANN_GAUSSIAN, FANNCSharp.ActivationFunction
	FANN_GAUSSIAN_SYMMETRIC, FANNCSharp.ActivationFunction
	FANN_LINEAR, FANNCSharp.ActivationFunction
	FANN_LINEAR_PIECE, FANNCSharp.ActivationFunction
	FANN_LINEAR_PIECE_SYMMETRIC, FANNCSharp.ActivationFunction
	FANN_SIGMOID, FANNCSharp.ActivationFunction
	FANN_SIGMOID_STEPWISE, FANNCSharp.ActivationFunction
	FANN_SIGMOID_SYMMETRIC, FANNCSharp.ActivationFunction
	FANN_SIN_SYMMETRIC, FANNCSharp.ActivationFunction
	FANN_THRESHOLD, FANNCSharp.ActivationFunction
	FANN_THRESHOLD_SYMMETRIC, FANNCSharp.ActivationFunction
	FANN_TRAIN_SARPROP, FANNCSharp.TrainingAlgorithm
	FANNCSharp.Connection
	FANNCSharp.Double.NeuralNet
	FANNCSharp.Double.TrainingData
	FANNCSharp.Fixed.Connection
	FANNCSharp.Fixed.NeuralNet
	FANNCSharp.Fixed.TrainingData
	FANNCSharp.Float.Connection
	FANNCSharp.Float.NeuralNet
	FANNCSharp.Float.TrainingData
	FannFile, FANNCSharp.FannFile
	FromNeuron FANNCSharp.Connection FANNCSharp.Fixed.Connection FANNCSharp.Float.Connection
	Functions FANNCSharp.AccessorEnumerator FANNCSharp.Connection FANNCSharp.Double.TrainingData FANNCSharp.FannFile FANNCSharp.Fixed.Connection FANNCSharp.Fixed.TrainingData FANNCSharp.Float.Connection FANNCSharp.Float.TrainingData
	Functions and Properties ArrayAccessor DoubleArrayAccessor.cs FloatArrayAccessor.cs IntArrayAccessor.cs DataAccessor DoubleAccessor.cs FloatAccessor.cs IntAccessor.cs FANNCSharp.Double.NeuralNet FANNCSharp.Fixed.NeuralNet FANNCSharp.Float.NeuralNet
G
	Get, ArrayAccessor DoubleArrayAccessor.cs FloatArrayAccessor.cs IntArrayAccessor.cs
	GetActivationFunction FANNCSharp.Double.NeuralNet FANNCSharp.Fixed.NeuralNet FANNCSharp.Float.NeuralNet
	GetActivationSteepness FANNCSharp.Double.NeuralNet FANNCSharp.Fixed.NeuralNet FANNCSharp.Float.NeuralNet
	GetEnumerator ArrayAccessor DoubleArrayAccessor.cs FloatArrayAccessor.cs IntArrayAccessor.cs DataAccessor DoubleAccessor.cs FloatAccessor.cs IntAccessor.cs
	GetTrainInput FANNCSharp.Double.TrainingData FANNCSharp.Fixed.TrainingData FANNCSharp.Float.TrainingData
	GetTrainOutput FANNCSharp.Double.TrainingData FANNCSharp.Fixed.TrainingData FANNCSharp.Float.TrainingData
I
	IAccessor<T>
	InitWeights FANNCSharp.Double.NeuralNet FANNCSharp.Fixed.NeuralNet FANNCSharp.Float.NeuralNet
	Input FANNCSharp.Double.TrainingData FANNCSharp.Fixed.TrainingData FANNCSharp.Float.TrainingData
	InputAccessor FANNCSharp.Double.TrainingData FANNCSharp.Fixed.TrainingData FANNCSharp.Float.TrainingData
	InputCount FANNCSharp.Double.NeuralNet FANNCSharp.Double.TrainingData FANNCSharp.Fixed.NeuralNet FANNCSharp.Fixed.TrainingData FANNCSharp.Float.NeuralNet FANNCSharp.Float.TrainingData
	Item ArrayAccessor DoubleArrayAccessor.cs FloatArrayAccessor.cs IntArrayAccessor.cs DataAccessor DoubleAccessor.cs FloatAccessor.cs IntAccessor.cs FANNCSharp.IAccessor
L
	LAYER, FANNCSharp.NetworkType
	LayerArray, FANNCSharp.Float.NeuralNet
	LayerCount FANNCSharp.Double.NeuralNet FANNCSharp.Fixed.NeuralNet FANNCSharp.Float.NeuralNet
	Layers FANNCSharp.Double.NeuralNet FANNCSharp.Fixed.NeuralNet
	LearningMomentum FANNCSharp.Double.NeuralNet FANNCSharp.Fixed.NeuralNet FANNCSharp.Float.NeuralNet
	LearningRate FANNCSharp.Double.NeuralNet FANNCSharp.Fixed.NeuralNet FANNCSharp.Float.NeuralNet

public enum ActivationFunction

public enum ErrorFunction

public enum NetworkType

The Fann Wrapper for C# provides Six main classes: FANNCSharp.Float::NeuralNet, FANNCSharp.Double::NeuralNet, FANNCSharp.Fixed::NeuralNet, FANNCSharp.Float::TrainingData, FANNCSharp.Double::TrainingData, FANNCSharp.Fixed::TrainingData.

public enum StopFunction

public enum TrainingAlgorithm

Periodical cosinus activation function.

Fast (sigmoid like) activation function defined by David Elliott

Fast (symmetric sigmoid like) activation function defined by David Elliott

Gaussian activation function.

Symmetric gaussian activation function.

Linear activation function.

Bounded linear activation function.

Bounded Linear activation function.

Sigmoid activation function.

Stepwise linear approximation to sigmoid.

Symmetric sigmoid activation function, aka.

Periodical sinus activation function.

Threshold activation function.

THE SARPROP ALGORITHM: A SIMULATED ANNEALING ENHANCEMENT TO RESILIENT BACK PROPAGATION http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.47.8197&rep=rep1&type=pdf

Describes a connection between two neurons and its weight

NeuralNet is the main neural network class used for both training and execution using doubles

TrainingData is used to create and manipulate training data used by the NeuralNet

Describes a connection between two neurons and its weight

NeuralNet is the main neural network class used for both training and execution using ints

TrainingData is used to create and manipulate training data used by the NeuralNet

Describes a connection between two neurons and its weight

NeuralNet is the main neural network class used for both training and execution using floats

TrainingData is used to create and manipulate training data used by the NeuralNet

public FannFile( string filename,
string mode )

Encapsulates a C FILE pointer

public uint FromNeuron { get, set }

Unique number used to identify source neuron

public uint FromNeuron { get, set }

Unique number used to identify source neuron

public uint FromNeuron { get, set }

Unique number used to identify source neuron

public ActivationFunction GetActivationFunction( int layer,
int neuron )

Get the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0.

public ActivationFunction GetActivationFunction( int layer,
int neuron )

Get the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0.

public ActivationFunction GetActivationFunction( int layer,
int neuron )

Get the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0.

public double GetActivationSteepness( int layer,
int neuron )

Get the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0.

public int GetActivationSteepness( int layer,
int neuron )

Get the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0.

public float GetActivationSteepness( int layer,
int neuron )

Get the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0.

Returns an enumerator that can enumerate over the collection of DataAccessors

Returns an enumerator that can enumerate over the collection of ints

public DataAccessor GetTrainInput( uint position )

Gets the training input data at the given position

public DataAccessor GetTrainInput( uint position )

Gets the training input data at the given position

public DataAccessor GetTrainInput( uint position )

Gets the training input data at the given position

public DataAccessor GetTrainOutput( uint position )

Gets the training output data at the given position

public DataAccessor GetTrainOutput( uint position )

Gets the training output data at the given position

public DataAccessor GetTrainOutput( uint position )

Gets the training output data at the given position

An enumerable interface that provides a count of elements in the accessor and random, read-only access to its elements.

public void InitWeights( TrainingData data )

Initialize the weights using Widrow + Nguyen’s algorithm.

public void InitWeights( TrainingData data )

Initialize the weights using Widrow + Nguyen’s algorithm.

public void InitWeights( TrainingData data )

Initialize the weights using Widrow + Nguyen’s algorithm.

public double[][] Input { get }

Grant access to the encapsulated data since many situations and applications creates the data from sources other than files or uses the training data for testing and related functions.

public int[][] Input { get }

Grant access to the encapsulated data since many situations and applications creates the data from sources other than files or uses the training data for testing and related functions.

public float[][] Input { get }

Grant access to the encapsulated data since many situations and applications creates the data from sources other than files or uses the training data for testing and related functions.

public ArrayAccessor InputAccessor { get }

An alternative to Input that returns an accessor object that grants access to to the input data with no copying.

public ArrayAccessor InputAccessor { get }

An alternative to Input that returns an accessor object that grants access to to the input data with no copying.

public ArrayAccessor InputAccessor { get }

An alternative to Input that returns an accessor object that grants access to to the input data with no copying.

public uint InputCount { get }

Get the number of input neurons.

public uint InputCount { get }

Returns the number of inputs in each of the training patterns in the TrainingData.

public uint InputCount { get }

Get the number of input neurons.

public uint InputCount { get }

Returns the number of inputs in each of the training patterns in the TrainingData.

public uint InputCount { get }

Get the number of input neurons.

public uint InputCount { get }

Returns the number of inputs in each of the training patterns in the TrainingData.

Provides access to the element at index

Each layer only has connections to the next layer

public uint[] LayerArray { get }

Get the number of neurons in each layer in the network.

public uint LayerCount { get }

Get the number of layers in the network

public uint LayerCount { get }

Get the number of layers in the network

public uint LayerCount { get }

Get the number of layers in the network

public uint[] Layers { get }

Get the number of neurons in each layer in the network.

public uint[] Layers { get }

Get the number of neurons in each layer in the network.

public float LearningMomentum { get, set }

Get or set the learning momentum.

public float LearningMomentum { get, set }

Get or set the learning momentum.

public float LearningMomentum { get, set }

Get or set the learning momentum.

public float LearningRate { get, set }

Return or set the learning rate.

public float LearningRate { get, set }

Return or set the learning rate.

public float LearningRate { get, set }

Return or set the learning rate.