@Override
public String toResultString() {
StringBuilder builder = new StringBuilder();
builder.append(
"Denormalization Model of the following Normalization:" + Tools.getLineSeparator());
builder.append(invertedModel.toResultString());
return builder.toString();
}
@Override
public void doWork() throws OperatorException {
AbstractNormalizationModel model = modelInput.getData(AbstractNormalizationModel.class);
// check how to behave if an Attribute is missing in the input ExampleSet
if (getParameter(PARAMETER_MISSING_ATTRIBUTES_KEY).equals(FAIL_ON_MISSING)) {
failOnMissingAttributes = true;
} else {
failOnMissingAttributes = false;
}
Map<String, LinearTransformation> attributeTransformations = new HashMap<>();
for (Attribute attribute : model.getTrainingHeader().getAttributes()) {
double b = model.computeValue(attribute, 0);
double a = model.computeValue(attribute, 1) - b;
attributeTransformations.put(attribute.getName(), new LinearTransformation(a, b));
}
modelOutput.deliver(
new DenormalizationModel(
model.getTrainingHeader(), attributeTransformations, model, failOnMissingAttributes));
originalModelOutput.deliver(model);
}