public virtual void Train(
	Network network,
	double[,] xData,
	double[,] yData
)

Public Overridable Sub Train ( 
	network As Network,
	xData As Double(,),
	yData As Double(,)
)

public:
virtual void Train(
	Network^ network, 
	array<double,2>^ xData, 
	array<double,2>^ yData
)

abstract Train : 
        network : Network * 
        xData : float[,] * 
        yData : float[,] -> unit 
override Train : 
        network : Network * 
        xData : float[,] * 
        yData : float[,] -> unit 

Parameters

network

Type: Imsl.DataMining.NeuralNetwork
The Network to be trained.

xData

Type: SystemDouble
An input double matrix containing training patterns.

yData

Type: SystemDouble
An output double matrix containing output training patterns.

Implements

The number of columns in xData must equal the number of Nodes in the InputLayer.

The number of columns in yData must equal the number of Perceptrons in the OutputLayer.

Each row of xData and yData contains a training pattern. The number of rows in these two arrays must be at least equal to the number of weights in the Network.

Reference