/**
* Normalize a single column to the input vector.
*
* @param colDef The column to normalize.
* @param outputColumn The current position in the vector.
* @param output The vector to output to.
* @param isInput Is this an input column.
* @param value The value to normalize.
* @return The new current position in the vector.
*/
public int normalizeToVector(
ColumnDefinition colDef, int outputColumn, double[] output, boolean isInput, String value) {
MissingHandler handler = null;
if (this.unknownValues.contains(value)) {
if (!this.missingHandlers.containsKey(colDef)) {
throw new EncogError(
"Do not know how to process missing value \""
+ value
+ "\" in field: "
+ colDef.getName());
}
handler = this.missingHandlers.get(colDef);
}
if (colDef.getDataType() == ColumnType.continuous) {
double d = parseDouble(value);
if (handler != null) {
d = handler.processDouble(colDef);
}
return this.normStrategy.normalizeColumn(colDef, isInput, d, output, outputColumn);
} else {
if (handler != null) {
value = handler.processString(colDef);
}
return this.normStrategy.normalizeColumn(colDef, isInput, value, output, outputColumn);
}
}
/**
* Calculate the error for the given method and dataset.
*
* @param method The method to use.
* @param data The data to use.
* @return The error.
*/
public double calculateError(MLMethod method, MLDataSet data) {
if (this.dataset.getNormHelper().getOutputColumns().size() == 1) {
ColumnDefinition cd = this.dataset.getNormHelper().getOutputColumns().get(0);
if (cd.getDataType() == ColumnType.nominal) {
return EncogUtility.calculateClassificationError((MLClassification) method, data);
}
}
return EncogUtility.calculateRegressionError((MLRegression) method, data);
}