/**
* 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);
}
/** {@inheritDoc} */
public String toString() {
StringBuilder result = new StringBuilder();
result.append("[NormalizationHelper:\n");
for (ColumnDefinition colDef : this.sourceColumns) {
result.append(colDef.toString());
result.append("\n");
}
result.append("]");
return result.toString();
}
/** {@inheritDoc} */
@Override
public String denormalizeColumn(ColumnDefinition colDef, MLData data, int dataColumn) {
double value = data.getData(dataColumn);
final double result =
((colDef.getLow() - colDef.getHigh()) * value
- this.normalizedHigh * colDef.getLow()
+ colDef.getHigh() * this.normalizedLow)
/ (this.normalizedLow - this.normalizedHigh);
// typically caused by a number that should not have been normalized
// (i.e. normalization or actual range is infinitely small.
if (Double.isNaN(result)) {
return "" + (((this.normalizedHigh - this.normalizedLow) / 2) + this.normalizedLow);
}
return "" + result;
}
/** {@inheritDoc} */
@Override
public int normalizeColumn(
ColumnDefinition colDef, double value, double[] outputData, int outputColumn) {
double result =
((value - colDef.getLow()) / (colDef.getHigh() - colDef.getLow()))
* (this.normalizedHigh - this.normalizedLow)
+ this.normalizedLow;
// typically caused by a number that should not have been normalized
// (i.e. normalization or actual range is infinitely small.
if (Double.isNaN(result)) {
result = ((this.normalizedHigh - this.normalizedLow) / 2) + this.normalizedLow;
}
outputData[outputColumn] = result;
return outputColumn + 1;
}
/**
* Define a source column. These define the raw input. Use this function if you know the index of
* the column in a non-header file.
*
* @param name The name of the column.
* @param index The index of the column, needed for non-headered files.
* @param colType The column type.
* @return The column definition
*/
public ColumnDefinition defineSourceColumn(String name, int index, ColumnType colType) {
ColumnDefinition result = new ColumnDefinition(name, colType);
result.setIndex(index);
addSourceColumn(result);
return result;
}
/**
* Add a source column. These define the raw input.
*
* @param def The column definition.
*/
public void addSourceColumn(ColumnDefinition def) {
this.sourceColumns.add(def);
def.setOwner(this);
}
