public bool Save( string  file )

Save the entire network to a configuration file.

public bool Save( string  file )

Save the entire network to a configuration file.

public bool Save( string  file )

Save the entire network to a configuration file.

public int SaveToFixed( string  file )

Saves the entire network to a configuration file.

public int SaveToFixed( string  file )

Saves the entire network to a configuration file.

public int SaveToFixed( string  file )

Saves the entire network to a configuration file.

public bool SaveTrain( string  filename )

Save the training structure to a file, with the format as specified in ReadTrainFromFile

public bool SaveTrain( string  filename )

Save the training structure to a file, with the format as specified in ReadTrainFromFile

public bool SaveTrain( string  filename )

Save the training structure to a file, with the format as specified in ReadTrainFromFile

public bool SaveTrainToFixed( string  filename, uint  decimalPoint )

Saves the training structure to a fixed point data file.

public bool SaveTrainToFixed( string  filename, uint  decimalPoint )

Saves the training structure to a fixed point data file.

public bool SaveTrainToFixed( string  filename, uint  decimalPoint )

Saves the training structure to a fixed point data file.

public void ScaleInput( DataAccessor  input )

Scale data in input vector before feed it to ann based on previously calculated parameters.

public void ScaleInput( DataAccessor  input )

Scale data in input vector before feed it to ann based on previously calculated parameters.

public void ScaleInputTrainData( double  new_min, double  new_max )

Scales the inputs in the training data to the specified range.

public void ScaleInputTrainData( int  new_min, int  new_max )

Scales the inputs in the training data to the specified range.

public void ScaleInputTrainData( float  new_min, float  new_max )

Scales the inputs in the training data to the specified range.

public void ScaleOutput( DataAccessor  output )

Scale data in output vector before feed it to ann based on previously calculated parameters.

public void ScaleOutput( DataAccessor  output )

Scale data in output vector before feed it to ann based on previously calculated parameters.

public void ScaleOutputTrainData( double  new_min, double  new_max )

Scales the outputs in the training data to the specified range.

public void ScaleOutputTrainData( int  new_min, int  new_max )

Scales the outputs in the training data to the specified range.

public void ScaleOutputTrainData( float  new_min, float  new_max )

Scales the outputs in the training data to the specified range.

public void ScaleTrain( TrainingData  data )

Scale input and output data based on previously calculated parameters.

public void ScaleTrain( TrainingData  data )

Scale input and output data based on previously calculated parameters.

public void ScaleTrainData( double  new_min, double  new_max )

Scales the inputs and outputs in the training data to the specified range.

public void ScaleTrainData( int  new_min, int  new_max )

Scales the inputs and outputs in the training data to the specified range.

public void ScaleTrainData( float  new_min, float  new_max )

Scales the inputs and outputs in the training data to the specified range.

public void SetActivationFunction( ActivationFunction  function, int  layer, int  neuron )

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

public void SetActivationFunction( ActivationFunction  function, int  layer, int  neuron )

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

public void SetActivationFunction( ActivationFunction  function, int  layer, int  neuron )

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

public void SetActivationFunctionLayer( ActivationFunction  function, int  layer )

Set the activation function for all the neurons in the layer number layer, counting the input layer as layer 0.

public void SetActivationFunctionLayer( ActivationFunction  function, int  layer )

Set the activation function for all the neurons in the layer number layer, counting the input layer as layer 0.

public void SetActivationFunctionLayer( ActivationFunction  function, int  layer )

Set the activation function for all the neurons in the layer number layer, counting the input layer as layer 0.

public void SetActivationSteepness( double  steepness, int  layer, int  neuron )

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

public void SetActivationSteepness( int  steepness, int  layer, int  neuron )

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

public void SetActivationSteepness( float  steepness, int  layer, int  neuron )

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

public void SetActivationSteepnessLayer( double  steepness, int  layer )

Set the activation steepness all of the neurons in layer number layer, counting the input layer as layer 0.

public void SetActivationSteepnessLayer( int  steepness, int  layer )

Set the activation steepness all of the neurons in layer number layer, counting the input layer as layer 0.

public void SetActivationSteepnessLayer( float  steepness, int  layer )

Set the activation steepness all of the neurons in layer number layer, counting the input layer as layer 0.

public void SetCallback( TrainingCallback  callback, Object  userData )

Sets the callback function for use during training.

public void SetCallback( TrainingCallback  callback, Object  userData )

Sets the callback function for use during training.

public bool SetInputScalingParams( TrainingData  data, float  newInputMin, float  newInputMax )

Calculate scaling parameters for future use based on training data.

public bool SetInputScalingParams( TrainingData  data, float  newInputMin, float  newInputMax )

Calculate scaling parameters for future use based on training data.

public bool SetOutputScalingParams( TrainingData  data, float  newOutputMin, float  newOutputMax )

Calculate scaling parameters for future use based on training data.

public bool SetOutputScalingParams( TrainingData  data, float  newOutputMin, float  newOutputMax )

Calculate scaling parameters for future use based on training data.

public bool SetScalingParams( TrainingData  data, float  newInputMin, float  newInputMax, float  newOutputMin, float  newOutputMax )

Calculate scaling parameters for future use based on training data.

public bool SetScalingParams( TrainingData  data, float  newInputMin, float  newInputMax, float  newOutputMin, float  newOutputMax )

Calculate scaling parameters for future use based on training data.

public void SetTrainData( double[][]  input, double[][]  output )

Set the training data to the input and output data provided.

public void SetTrainData( int[][]  input, int[][]  output )

Set the training data to the input and output data provided.

public void SetTrainData( float[][]  input, float[][]  output )

Set the training data to the input and output data provided.

public void SetWeight( uint  fromNeuron, uint  toNeuron, double  weight )

Set a connection in the network.

public void SetWeight( uint  fromNeuron, uint  toNeuron, int  weight )

Set a connection in the network.

public void SetWeight( uint  fromNeuron, uint  toNeuron, float  weight )

Set a connection in the network.

public void ShuffleTrainData()

Shuffles training data, randomizing the order.

public void ShuffleTrainData()

Shuffles training data, randomizing the order.

public void ShuffleTrainData()

Shuffles training data, randomizing the order.

public void SubsetTrainData( uint  pos, uint  length )

Changes the training data to a subset, starting at position pos and length elements forward.

public void SubsetTrainData( uint  pos, uint  length )

Changes the training data to a subset, starting at position pos and length elements forward.

public void SubsetTrainData( uint  pos, uint  length )

Changes the training data to a subset, starting at position pos and length elements forward.