C# 클래스 Encog.Normalize.DataNormalization

상속: Encog.Persist.BasicPersistedObject

파일 보기 프로젝트 열기: encog/encog-silverlight-core

공개 메소드들

메소드	설명
AddInputField ( IInputField f ) : void	Add an input field.
AddOutputField ( IOutputField outputField ) : void	Add an output field. This output field will be added as a "neural network input field", not an "ideal output field".
AddOutputField ( IOutputField outputField, bool ideal ) : void	Add a field and allow it to be specified as an "ideal output field". An "ideal" field is the expected output that the neural network is training towards.
AddSegregator ( ISegregator segregator ) : void	Add a segregator.
BuildForNetworkInput ( double data ) : INeuralData	Build "input data for a neural network" based on the input values provided. This allows input for a neural network to be normalized. This is typically used when data is to be presented to a trained neural network.
Clone ( ) : object	Not implemented.
CreatePersistor ( ) : IPersistor	A persistor to persist this DataNormalization object.
FindInputField ( Type clazz, int count ) : IInputField	Find an input field by its class.
FindOutputField ( Type clazz, int count ) : IOutputField	Find an output field by its class.
GetNetworkInputLayerSize ( ) : int	Calculate the number of output fields that are not used as ideal values, these will be the input to the neural network. This is the input layer size for the neural network.
GetNetworkOutputLayerSize ( ) : int	The number of output fields that are used as ideal values, these will be the ideal output from the neural network. This is the output layer size for the neural network.
GetOutputFieldCount ( ) : int	The total size of all output fields. This takes into account output fields that generate more than one value.
InitForOutput ( ) : void	Setup the row for output.
InitForPass ( ) : void	Setup the row for output.
Process ( ) : void	Call this method to begin the normalization process. Any status updates will be sent to the class specified in the constructor.
TwoPassesNeeded ( ) : bool	Determine if two passes will be needed.

비공개 메소드들

메소드	설명
ApplyMinMax ( ) : void	Called internally to allow each of the input fields to update their min/max values in the first pass.
DetermineInputFieldValue ( IInputField field, int index ) : double	Called internally to obtain the current value for an input field.
DetermineInputFieldValues ( int index ) : void	Called internally to determine all of the input field values.
FirstPass ( ) : void	First pass, count everything, establish min/max.
Next ( ) : bool	Called internally to advance to the next row.
OpenCSV ( ) : void	Called internally to open the CSV file.
OpenDataSet ( ) : void	Open any datasets that were used by the input layer.
ReportResult ( String message, int total, int current ) : void	Report on the current progress.
SecondPass ( ) : void	The second pass actually writes the data to the output files.
ShouldInclude ( ) : bool	Should this row be included? Check the segregatprs.

메소드 상세

AddInputField() 공개 메소드

Add an input field.

public AddInputField ( IInputField f ) : void
f	IInputField	The input field to add.
리턴	void

AddOutputField() 공개 메소드

Add an output field. This output field will be added as a "neural network input field", not an "ideal output field".

public AddOutputField ( IOutputField outputField ) : void
outputField	IOutputField	The output field to add.
리턴	void

AddOutputField() 공개 메소드

Add a field and allow it to be specified as an "ideal output field". An "ideal" field is the expected output that the neural network is training towards.

public AddOutputField ( IOutputField outputField, bool ideal ) : void
outputField	IOutputField	The output field.
ideal	bool	True if this is an ideal field.
리턴	void

AddSegregator() 공개 메소드

Add a segregator.

public AddSegregator ( ISegregator segregator ) : void
segregator	ISegregator	The segregator to add.
리턴	void

BuildForNetworkInput() 공개 메소드

Build "input data for a neural network" based on the input values provided. This allows input for a neural network to be normalized. This is typically used when data is to be presented to a trained neural network.

public BuildForNetworkInput ( double data ) : INeuralData
data	double	The input values to be normalized.
리턴	INeuralData

Clone() 공개 메소드

Not implemented.

public Clone ( ) : object
리턴	object

CreatePersistor() 공개 메소드

A persistor to persist this DataNormalization object.

public CreatePersistor ( ) : IPersistor
리턴	IPersistor

FindInputField() 공개 메소드

Find an input field by its class.

public FindInputField ( Type clazz, int count ) : IInputField
clazz	System.Type	The input field class type you are looking for.
count	int	The instance of the input field needed, 0 for the first.
리턴	IInputField

FindOutputField() 공개 메소드

Find an output field by its class.

public FindOutputField ( Type clazz, int count ) : IOutputField
clazz	System.Type	The output field class type you are looking for.
count	int	The instance of the output field needed, 0 for the first.
리턴	IOutputField

GetNetworkInputLayerSize() 공개 메소드

Calculate the number of output fields that are not used as ideal values, these will be the input to the neural network. This is the input layer size for the neural network.

public GetNetworkInputLayerSize ( ) : int
리턴	int

GetNetworkOutputLayerSize() 공개 메소드

The number of output fields that are used as ideal values, these will be the ideal output from the neural network. This is the output layer size for the neural network.

public GetNetworkOutputLayerSize ( ) : int
리턴	int

GetOutputFieldCount() 공개 메소드

The total size of all output fields. This takes into account output fields that generate more than one value.

public GetOutputFieldCount ( ) : int
리턴	int

InitForOutput() 공개 메소드

Setup the row for output.

public InitForOutput ( ) : void
리턴	void

InitForPass() 공개 메소드

Setup the row for output.

public InitForPass ( ) : void
리턴	void

Process() 공개 메소드

Call this method to begin the normalization process. Any status updates will be sent to the class specified in the constructor.

public Process ( ) : void
리턴	void

TwoPassesNeeded() 공개 메소드

Determine if two passes will be needed.

public TwoPassesNeeded ( ) : bool
리턴	bool