Network.ComputeStatistics Method

NetworkComputeStatistics Method

Computes error statistics.

Namespace: Imsl.DataMining.Neural
Assembly: ImslCS (in ImslCS.dll) Version: 6.5.2.0

Syntax

public virtual double[] ComputeStatistics(
	double[,] xData,
	double[,] yData
)

Public Overridable Function ComputeStatistics ( 
	xData As Double(,),
	yData As Double(,)
) As Double()

public:
virtual array<double>^ ComputeStatistics(
	array<double,2>^ xData, 
	array<double,2>^ yData
)

abstract ComputeStatistics : 
        xData : float[,] * 
        yData : float[,] -> float[] 
override ComputeStatistics : 
        xData : float[,] * 
        yData : float[,] -> float[]

Parameters

xData: Type: SystemDouble
A double matrix containing the input values.
yData: Type: SystemDouble
A double array containing the observed values.

Return Value

Type: Double
A double array containing the above described statistics.

Remarks

This is a static method that can be used to compute the statistics regardless of the training class used to train the Network.

Computes statistics related to the error. In this table, the observed values are . The forecasted values are $\hat{y}_i$ . The mean observed value is $\bar{y} = \sum_i y_i / NC$ , where N is the number of observations and C is the number of classes per observation.

Index	Name	Formula
0	SSE	$\frac{1}{2} \sum_i\left(y_i-\hat{y}_i\right)^2$
1	RMS	$\frac{\sum_i \left(y_i-\hat{y}_i\right)^2}{\sum_i\left(y_i-\bar{y}_i\right)}$
2	Laplacian	$\sum_i \left\|y_i-\hat{y}_i\right\|$
3	Scaled Laplacian	$\frac{\sum_i \left\|y_i-\hat{y}_i\right\|}{\sum_i\left\|y_i-\bar{y}_i \right\|}$
4	Max residual	$\max_i\left\|y_i-\hat{y}_i\right\|$

Reference

Network Class

Imsl.DataMining.Neural Namespace