public ActivationFunction ActivationFunctionHidden { set }

Set the activation function for all of the hidden layers.

public ActivationFunction ActivationFunctionHidden { set }

Set the activation function for all of the hidden layers.

public ActivationFunction ActivationFunctionHidden { set }

Set the activation function for all of the hidden layers.

public ActivationFunction ActivationFunctionOutput { set }

Set the activation function for the output layer.

public ActivationFunction ActivationFunctionOutput { set }

Set the activation function for the output layer.

public ActivationFunction ActivationFunctionOutput { set }

Set the activation function for the output layer.

public double ActivationSteepnessHidden { set }

Set the steepness of the activation steepness in all of the hidden layers.

public int ActivationSteepnessHidden { set }

Set the steepness of the activation steepness in all of the hidden layers.

public float ActivationSteepnessHidden { set }

Set the steepness of the activation steepness in all of the hidden layers.

public double ActivationSteepnessOutput { set }

Set the steepness of the activation steepness in the output layer.

public int ActivationSteepnessOutput { set }

Set the steepness of the activation steepness in the output layer.

public float ActivationSteepnessOutput { set }

Set the steepness of the activation steepness in the output layer.

Copies the Accessor’s array’s values to arrays and puts then in array

Copies the Accessor’s values to an array

public uint[] BiasArray { get }

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

public uint[] Biases { get }

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

public uint[] Biases { get }

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

public uint BitFail { get }

Gets or set the number of fail bits.

public uint BitFail { get }

Gets or set the number of fail bits.

public uint BitFail { get }

Gets or set the number of fail bits.

public double BitFailLimit { get, set }

Gets or sets the bit fail limit used during training.

public int BitFailLimit { get, set }

Gets or sets the bit fail limit used during training.

public float BitFailLimit { get, set }

Gets or sets the bit fail limit used during training.

public ActivationFunction[] CascadeActivationFunctions { get, set }

The cascade activation functions array is an array of the different activation functions used by the candidates.

public ActivationFunction[] CascadeActivationFunctions { get, set }

The cascade activation functions array is an array of the different activation functions used by the candidates.

public uint CascadeActivationFunctionsCount { get }

The number of activation functions in the CascadeActivationFunctions array.

public uint CascadeActivationFunctionsCount { get }

The number of activation functions in the CascadeActivationFunctions array.

public double[] CascadeActivationSteepnesses { get, set }

The cascade activation steepnesses array is an array of the different activation functions used by the candidates.

public float[] CascadeActivationSteepnesses { get, set }

The cascade activation steepnesses array is an array of the different activation functions used by the candidates.

public uint CascadeActivationSteepnessesCount { get }

The number of activation steepnesses in the CascadeActivationFunctions array.

public uint CascadeActivationSteepnessesCount { get }

The number of activation steepnesses in the CascadeActivationFunctions array.

public float CascadeCandidateChangeFraction { get, set }

The cascade candidate change fraction is a number between 0 and 1 determining how large a fraction the MSE value should change within CascadeCandidateStagnationEpochs during training of the candidate neurons, in order for the training not to stagnate.

public float CascadeCandidateChangeFraction { get, set }

The cascade candidate change fraction is a number between 0 and 1 determining how large a fraction the MSE value should change within CascadeCandidateStagnationEpochs during training of the candidate neurons, in order for the training not to stagnate.

public uint CascadeCandidateGroupsCount { get, set }

The number of candidate groups is the number of groups of identical candidates which will be used during training.

public uint CascadeCandidateGroupsCount { get, set }

The number of candidate groups is the number of groups of identical candidates which will be used during training.

public double CascadeCandidateLimit { get, set }

The candidate limit is a limit for how much the candidate neuron may be trained.

public float CascadeCandidateLimit { get, set }

The candidate limit is a limit for how much the candidate neuron may be trained.

public uint CascadeCandidatesCount { get }

The number of candidates used during training (calculated by multiplying CascadeActivationFunctionsCount, CascadeActivationSteepnessesCount and CascadeCandidateGroupsCount).

public uint CascadeCandidatesCount { get }

The number of candidates used during training (calculated by multiplying CascadeActivationFunctionsCount, CascadeActivationSteepnessesCount and CascadeCandidateGroupsCount).

public uint CascadeCandidateStagnationEpochs { get, set }

The number of cascade candidate stagnation epochs determines the number of epochs training is allowed to continue without changing the MSE by a fraction of CascadeCandidateChangeFraction.

public uint CascadeCandidateStagnationEpochs { get, set }

The number of cascade candidate stagnation epochs determines the number of epochs training is allowed to continue without changing the MSE by a fraction of CascadeCandidateChangeFraction.

public uint CascadeMaxCandEpochs { get, set }

The maximum candidate epochs determines the maximum number of epochs the input connections to the candidates may be trained before adding a new candidate neuron.

public uint CascadeMaxCandEpochs { get, set }

The maximum candidate epochs determines the maximum number of epochs the input connections to the candidates may be trained before adding a new candidate neuron.

public uint CascadeMaxOutEpochs { get, set }

The maximum out epochs determines the maximum number of epochs the output connections may be trained after adding a new candidate neuron.

public uint CascadeMaxOutEpochs { get, set }

The maximum out epochs determines the maximum number of epochs the output connections may be trained after adding a new candidate neuron.

public float CascadeOutputChangeFraction { get, set }

The cascade output change fraction is a number between 0 and 1 determining how large a fraction the MSE value should change within CascadeOutputStagnationEpochs during training of the output connections, in order for the training not to stagnate.

public float CascadeOutputChangeFraction { get, set }

The cascade output change fraction is a number between 0 and 1 determining how large a fraction the MSE value should change within CascadeOutputStagnationEpochs during training of the output connections, in order for the training not to stagnate.

public uint CascadeOutputStagnationEpochs { get, set }

The number of cascade output stagnation epochs determines the number of epochs training is allowed to continue without changing the MSE by a fraction of CascadeOutputChangeFraction.

public uint CascadeOutputStagnationEpochs { get, set }

The number of cascade output stagnation epochs determines the number of epochs training is allowed to continue without changing the MSE by a fraction of CascadeOutputChangeFraction.

public double CascadeWeightMultiplier { get, set }

The weight multiplier is a parameter which is used to multiply the weights from the candidate neuron before adding the neuron to the neural network.

public float CascadeWeightMultiplier { get, set }

The weight multiplier is a parameter which is used to multiply the weights from the candidate neuron before adding the neuron to the neural network.

public Connection[] ConnectionArray { get }

Get the connections in the network.

public float ConnectionRate { get }

Get the connection rate used when the network was created

public float ConnectionRate { get }

Get the connection rate used when the network was created

public float ConnectionRate { get }

Get the connection rate used when the network was created

public Connection[] Connections { get }

Get the connections in the network.

public Connection[] Connections { get }

Get the connections in the network.

The number of DataAccessors (arrays of doubles) this object holds

The number of doubles this object holds

int Count { get }

The number of elements in the accessor.

public T Current { get }

Returns the item in the collection currently being referenced by the internal reference

public uint DecimalPoint { get }

Returns the position of the decimal point in the ann.

public uint ErrNo { get }

Returns the last error number.

public uint ErrNo { get }

Returns the last error number.

public uint ErrNo { get }

Returns the last error number.

public FannFile ErrorLog { set }

Change where errors are logged to.

public FannFile ErrorLog { set }

Change where errors are logged to.

public FannFile ErrorLog { set }

Change where errors are logged to.

public string ErrStr { get }

Returns the last errstr.

public string ErrStr { get }

Returns the last errstr.

public string ErrStr { get }

Returns the last errstr.

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 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