Index

$#! · 0-9 · A · B · C · D · E · F · G · H · I · J · K · L · M · N · O · P · Q · R · S · T · U · V · W · X · Y · Z

T
	Test FANNCSharp.Double.NeuralNet FANNCSharp.Fixed.NeuralNet FANNCSharp.Float.NeuralNet
	TestData FANNCSharp.Double.NeuralNet FANNCSharp.Fixed.NeuralNet FANNCSharp.Float.NeuralNet
	TestDataParallel FANNCSharp.Double.NeuralNet FANNCSharp.Float.NeuralNet
	ToNeuron FANNCSharp.Connection FANNCSharp.Fixed.Connection FANNCSharp.Float.Connection
	TotalConnections FANNCSharp.Double.NeuralNet FANNCSharp.Fixed.NeuralNet FANNCSharp.Float.NeuralNet
	TotalNeurons FANNCSharp.Double.NeuralNet FANNCSharp.Fixed.NeuralNet FANNCSharp.Float.NeuralNet
	Train FANNCSharp.Double.NeuralNet FANNCSharp.Float.NeuralNet
	TRAIN_BATCH, FANNCSharp.TrainingAlgorithm
	TRAIN_INCREMENTAL, FANNCSharp.TrainingAlgorithm
	TRAIN_QUICKPROP, FANNCSharp.TrainingAlgorithm
	TRAIN_RPROP, FANNCSharp.TrainingAlgorithm
	TrainDataLength FANNCSharp.Double.TrainingData FANNCSharp.Fixed.TrainingData FANNCSharp.Float.TrainingData
	TrainEpoch FANNCSharp.Double.NeuralNet FANNCSharp.Float.NeuralNet
	TrainEpochBatchParallel FANNCSharp.Double.NeuralNet FANNCSharp.Float.NeuralNet
	TrainEpochIncrementalMod FANNCSharp.Double.NeuralNet FANNCSharp.Float.NeuralNet
	TrainEpochIrpropmParallel FANNCSharp.Double.NeuralNet FANNCSharp.Float.NeuralNet
	TrainEpochQuickpropParallel FANNCSharp.Double.NeuralNet FANNCSharp.Float.NeuralNet
	TrainEpochSarpropParallel FANNCSharp.Double.NeuralNet FANNCSharp.Float.NeuralNet
	TrainErrorFunction FANNCSharp.Double.NeuralNet FANNCSharp.Fixed.NeuralNet FANNCSharp.Float.NeuralNet
	TrainingAlgorithm FANNCSharp FANNCSharp.Double.NeuralNet FANNCSharp.Fixed.NeuralNet FANNCSharp.Float.NeuralNet
	TrainingAlgorithm.cs
	TrainingCallback FANNCSharp.Double.NeuralNet FANNCSharp.Float.NeuralNet
	TrainingData FANNCSharp.Double.TrainingData FANNCSharp.Fixed.TrainingData FANNCSharp.Float.TrainingData
	TrainOnData FANNCSharp.Double.NeuralNet FANNCSharp.Float.NeuralNet
	TrainOnFile FANNCSharp.Double.NeuralNet FANNCSharp.Float.NeuralNet
	TrainStopFunction FANNCSharp.Double.NeuralNet FANNCSharp.Fixed.NeuralNet FANNCSharp.Float.NeuralNet
W
	Weight FANNCSharp.Connection FANNCSharp.Fixed.Connection FANNCSharp.Float.Connection
	WeightArray, FANNCSharp.Float.NeuralNet
	Weights FANNCSharp.Double.NeuralNet FANNCSharp.Fixed.NeuralNet

public double[] Test( double[] input,
double[] desiredOutput )

Test with a set of inputs, and a set of desired outputs.

public int[] Test( int[] input,
int[] desiredOutput )

Test with a set of inputs, and a set of desired outputs.

public float[] Test( float[] input,
float[] desiredOutput )

Test with a set of inputs, and a set of desired outputs.

public float TestData( TrainingData data )

Test a set of training data and calculates the MSE for the training data.

public float TestData( TrainingData data )

Test a set of training data and calculates the MSE for the training data.

public float TestData( TrainingData data )

Test a set of training data and calculates the MSE for the training data.

public float TestDataParallel( TrainingData data,
uint threadNumb )

public uint ToNeuron { get, set }

Unique number used to identify destination neuron

public uint ToNeuron { get, set }

Unique number used to identify destination neuron

public uint ToNeuron { get, set }

Unique number used to identify destination neuron

public uint TotalConnections { get }

Get the total number of connections in the entire network.

public uint TotalConnections { get }

Get the total number of connections in the entire network.

public uint TotalConnections { get }

Get the total number of connections in the entire network.

public uint TotalNeurons { get }

Get the total number of neurons in the entire network.

public uint TotalNeurons { get }

Get the total number of neurons in the entire network.

public uint TotalNeurons { get }

Get the total number of neurons in the entire network.

public void Train( double[] input,
double[] desiredOutput )

Train one iteration with a set of inputs, and a set of desired outputs.

public void Train( float[] input,
float[] desiredOutput )

Train one iteration with a set of inputs, and a set of desired outputs.

Standard backpropagation algorithm, where the weights are updated after calculating the mean square error for the whole training set.

Standard backpropagation algorithm, where the weights are updated after each training pattern.

A more advanced batch training algorithm which achieves good results for many problems.

public uint TrainDataLength { get }

Returns the number of training patterns in the TrainingData.

public uint TrainDataLength { get }

Returns the number of training patterns in the TrainingData.

public uint TrainDataLength { get }

Returns the number of training patterns in the TrainingData.

public float TrainEpoch( TrainingData data )

Train one epoch with a set of training data.

public float TrainEpoch( TrainingData data )

Train one epoch with a set of training data.

public float TrainEpochBatchParallel( TrainingData data,
uint threadNumb )

public float TrainEpochIncrementalMod( TrainingData data )

public float TrainEpochIrpropmParallel( TrainingData data,
uint threadNumb )

public float TrainEpochQuickpropParallel( TrainingData data,
uint threadNumb )

public float TrainEpochSarpropParallel( TrainingData data,
uint threadNumb )

public ErrorFunction TrainErrorFunction { get, set }

Sets or returns the error function used during training.

public ErrorFunction TrainErrorFunction { get, set }

Sets or returns the error function used during training.

public ErrorFunction TrainErrorFunction { get, set }

Sets or returns the error function used during training.

The Training algorithms used when training on FANNCSharp.Float::TrainingData with functions like FANNCSharp.Float::NeuralNet::TrainOnData or FANNCSharp.Float::NeuralNet::TrainOnFile.

public TrainingAlgorithm TrainingAlgorithm { get, set }

Return or set the training algorithm as described by TrainingAlgorithm.

public TrainingAlgorithm TrainingAlgorithm { get, set }

Return or set the training algorithm as described by TrainingAlgorithm.

public TrainingAlgorithm TrainingAlgorithm { get, set }

Return or set the training algorithm as described by TrainingAlgorithm.

public delegate int TrainingCallback( NeuralNet net,
TrainingData data,
uint maxEpochs,
uint epochsBetweenReports,
float desiredError,
uint epochs,
Object userData )

This callback function can be called during training when using TrainOnData, TrainOnFile or CascadetrainOnData

public delegate int TrainingCallback( NeuralNet net,
TrainingData data,
uint maxEpochs,
uint epochsBetweenReports,
float desiredError,
uint epochs,
Object userData )

This callback function can be called during training when using TrainOnData, TrainOnFile or CascadetrainOnData

public TrainingData()

Default constructor creates an empty training data.

public TrainingData()

Default constructor creates an empty training data.

public TrainingData()

Default constructor creates an empty training data.

public void TrainOnData( TrainingData data,
uint maxEpochs,
uint epochsBetweenReports,
float desiredError )

Trains on an entire dataset, for a period of time.

public void TrainOnData( TrainingData data,
uint maxEpochs,
uint epochsBetweenReports,
float desiredError )

Trains on an entire dataset, for a period of time.

public void TrainOnFile( string filename,
uint maxEpochs,
uint epochsBetweenReports,
float desiredError )

Does the same as TrainOnData, but reads the training data directly from a file.

public void TrainOnFile( string filename,
uint maxEpochs,
uint epochsBetweenReports,
float desiredError )

Does the same as TrainOnData, but reads the training data directly from a file.

public StopFunction TrainStopFunction { get, set }

Gets or sets the stop function used during training.

public StopFunction TrainStopFunction { get, set }

Gets or sets the stop function used during training.

public StopFunction TrainStopFunction { get, set }

Gets or sets the stop function used during training.

public double Weight { get, set }

The numerical value of the weight

public int Weight { get, set }

The numerical value of the weight

public float Weight { get, set }

The numerical value of the weight

public Connection[] WeightArray { set }

Set connections in the network.

public Connection[] Weights { set }

Set connections in the network.

public Connection[] Weights { set }

Set connections in the network.