public class NetError
extends java.lang.Object
The difference to the TrainingSignalGenerator is that these methods
calculate the errors for the whole network, not for each neuron.
| Constructor and Description |
|---|
NetError() |
| Modifier and Type | Method and Description |
|---|---|
static double[] |
netErrors(NeuralNet net,
PatternSet patterns,
double[][][] statValues,
double min_out,
double max_out)
Calculate SSE, MSE, NRMSE, SQEP, and classification winner error,
and return them in an array of five doubles.
|
static double |
sumSquareError(NeuralNet net,
PatternSet patterns)
Calculate the Square Sum Error on the network.
|
public static double sumSquareError(NeuralNet net, PatternSet patterns)
net - NeuralNet to calc the error for.patterns - Patterns to calc the error with.public static double[] netErrors(NeuralNet net, PatternSet patterns, double[][][] statValues, double min_out, double max_out)
This method can also return a stats array with per-pattern square error sums, and neuron output and error values for every neuron and pattern.
net - NeuralNet to calc the errors for.patterns - Patterns to calc the errors with.statValues - If non-null and length >= 3, more detailed statistics are returned: min_out - Minimum expected output (for squared error percentage);
nothing bad happens if the real output happens to be larger.max_out - Maximum expected output (for squared error percentage);
nothing bad happens if the real output happens to be larger.