C# Class Encog.Neural.NeuralData.Temporal.TemporalNeuralDataSet

This class implements a temporal neural data set. A temporal neural dataset is designed to use a neural network to predict. A temporal dataset is a stream of data over a time range. This time range is broken up into "points". Each point can contain one or more values. These values are either the values that you would like to predict, or use to predict. It is possible for a value to be both predicted and used to predict. For example, if you were trying to predict a trend in a stock's price fluctuations you might very well use the security price for both. Each point that we have data for is stored in the TemporalPoint class. Each TemporalPoint will contain one more data values. These data values are described by the TemporalDataDescription class. For example, if you had five TemporalDataDescription objects added to this class, each Temporal point object would contain five values. Points are arranged by sequence number. No two points can have the same sequence numbers. Methods are provided to allow you to add points using the Date class. These dates are resolved to sequence number using the level of granularity specified for this class. No two points can occupy the same granularity increment.
Inheritance: Encog.Neural.Data.Basic.BasicNeuralDataSet
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Public Methods

Method Description
Add ( INeuralData data ) : void

Adding directly is not supported. Rather, add temporal points and generate the training data.
Add ( INeuralData inputData, INeuralData idealData ) : void

Adding directly is not supported. Rather, add temporal points and generate the training data.
Add ( INeuralDataPair inputData ) : void

Adding directly is not supported. Rather, add temporal points and generate the training data.
AddDescription ( TemporalDataDescription desc ) : void

Add a data description.
CalculateActualSetSize ( ) : int

Calculate the actual set size, this is the number of training set entries that will be generated.
CalculateNeuronCounts ( ) : void

Calculate how many input and output neurons will be needed for the current data.
CalculatePointsInRange ( ) : int

Calculate how many points are in the high and low range. These are the points that the training set will be generated on.
CalculateStartIndex ( ) : int

Calculate the index to start at.
Clear ( ) : void

Clear the entire dataset.
CreatePoint ( System.DateTime when ) : TemporalPoint

Create a temporal point from a time. Using the grandularity each date is given a unique sequence number. No two dates that fall in the same grandularity should be specified.
CreatePoint ( int sequence ) : TemporalPoint

Create a temporal data point using a sequence number. They can also be created using time. No two points should have the same sequence number.
Generate ( ) : void

Generate the training sets.
GenerateInputNeuralData ( int index ) : BasicNeuralData

Generate input neural data for the specified index.
GenerateOutputNeuralData ( int index ) : BasicNeuralData

Generate neural ideal data for the specified index.
GetSequenceFromDate ( System.DateTime when ) : int

Create a sequence number from a time. The first date will be zero, and subsequent dates will be increased according to the grandularity specified.
IsPointInRange ( TemporalPoint point ) : bool

Is the specified point within the range. If a point is in the selection range, then the point will be used to generate the training sets.
SortPoints ( ) : void

Sort the points.
TemporalNeuralDataSet ( int inputWindowSize, int predictWindowSize ) : System

Construct a dataset.

Private Methods

Method Description
FormatData ( TemporalDataDescription desc, int index ) : double

Format data according to the type specified in the description.
GetDataDeltaChange ( TemporalDataDescription desc, int index ) : double

Get data between two points in delta form.
GetDataPercentChange ( TemporalDataDescription desc, int index ) : double

Get data between two points in percent form.
GetDataRAW ( TemporalDataDescription desc, int index ) : double

Get data between two points in raw form.

Method Details

Add() public method

Adding directly is not supported. Rather, add temporal points and generate the training data.
public Add ( INeuralData data ) : void
data INeuralData Not used.
return void

Add() public method

Adding directly is not supported. Rather, add temporal points and generate the training data.
public Add ( INeuralData inputData, INeuralData idealData ) : void
inputData INeuralData Not used
idealData INeuralData Not used
return void

Add() public method

Adding directly is not supported. Rather, add temporal points and generate the training data.
public Add ( INeuralDataPair inputData ) : void
inputData INeuralDataPair Not used.
return void

AddDescription() public method

Add a data description.
public AddDescription ( TemporalDataDescription desc ) : void
desc TemporalDataDescription The data description to add.
return void

CalculateActualSetSize() public method

Calculate the actual set size, this is the number of training set entries that will be generated.
public CalculateActualSetSize ( ) : int
return int

CalculateNeuronCounts() public method

Calculate how many input and output neurons will be needed for the current data.
public CalculateNeuronCounts ( ) : void
return void

CalculatePointsInRange() public method

Calculate how many points are in the high and low range. These are the points that the training set will be generated on.
public CalculatePointsInRange ( ) : int
return int

CalculateStartIndex() public method

Calculate the index to start at.
public CalculateStartIndex ( ) : int
return int

Clear() public method

Clear the entire dataset.
public Clear ( ) : void
return void

CreatePoint() public method

Create a temporal point from a time. Using the grandularity each date is given a unique sequence number. No two dates that fall in the same grandularity should be specified.
public CreatePoint ( System.DateTime when ) : TemporalPoint
when System.DateTime The time that this point should be created at.
return TemporalPoint

CreatePoint() public method

Create a temporal data point using a sequence number. They can also be created using time. No two points should have the same sequence number.
public CreatePoint ( int sequence ) : TemporalPoint
sequence int The sequence number.
return TemporalPoint

Generate() public method

Generate the training sets.
public Generate ( ) : void
return void

GenerateInputNeuralData() public method

Generate input neural data for the specified index.
public GenerateInputNeuralData ( int index ) : BasicNeuralData
index int The index to generate neural data for.
return Encog.Neural.Data.Basic.BasicNeuralData

GenerateOutputNeuralData() public method

Generate neural ideal data for the specified index.
public GenerateOutputNeuralData ( int index ) : BasicNeuralData
index int The index to generate for.
return Encog.Neural.Data.Basic.BasicNeuralData

GetSequenceFromDate() public method

Create a sequence number from a time. The first date will be zero, and subsequent dates will be increased according to the grandularity specified.
public GetSequenceFromDate ( System.DateTime when ) : int
when System.DateTime The date to generate the sequence number for.
return int

IsPointInRange() public method

Is the specified point within the range. If a point is in the selection range, then the point will be used to generate the training sets.
public IsPointInRange ( TemporalPoint point ) : bool
point TemporalPoint The point to consider.
return bool

SortPoints() public method

Sort the points.
public SortPoints ( ) : void
return void

TemporalNeuralDataSet() public method

Construct a dataset.
public TemporalNeuralDataSet ( int inputWindowSize, int predictWindowSize ) : System
inputWindowSize int What is the input window size.
predictWindowSize int What is the prediction window size.
return System