@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); }