public static void validatePMMLVsSchema(PMML pmml, InputSchema schema) {
    """
	Validates that the encoded PMML model received matches expected schema.
	
	@param pmml {@link PMML} encoding of KMeans Clustering
	@param schema expected schema attributes of KMeans Clustering
    """
    List<Model> models = pmml.getModels();
    Preconditions.checkArgument(models.size() == 1,
        "Should have exactly one model, but had %s", models.size());

    Model model = models.get(0);
    Preconditions.checkArgument(model instanceof ClusteringModel);
    Preconditions.checkArgument(model.getMiningFunction() == MiningFunction.CLUSTERING);

    DataDictionary dictionary = pmml.getDataDictionary();
    Preconditions.checkArgument(
        schema.getFeatureNames().equals(AppPMMLUtils.getFeatureNames(dictionary)),
        "Feature names in schema don't match names in PMML");

    MiningSchema miningSchema = model.getMiningSchema();
    Preconditions.checkArgument(schema.getFeatureNames().equals(
        AppPMMLUtils.getFeatureNames(miningSchema)));

  }
public static void validatePMMLVsSchema(PMML pmml, InputSchema schema) {
    List<Model> models = pmml.getModels();
    Preconditions.checkArgument(models.size() == 1,
        "Should have exactly one model, but had %s", models.size());

    Model model = models.get(0);
    Preconditions.checkArgument(model instanceof ClusteringModel);
    Preconditions.checkArgument(model.getMiningFunction() == MiningFunction.CLUSTERING);

    DataDictionary dictionary = pmml.getDataDictionary();
    Preconditions.checkArgument(
        schema.getFeatureNames().equals(AppPMMLUtils.getFeatureNames(dictionary)),
        "Feature names in schema don't match names in PMML");

    MiningSchema miningSchema = model.getMiningSchema();
    Preconditions.checkArgument(schema.getFeatureNames().equals(
        AppPMMLUtils.getFeatureNames(miningSchema)));

  }
public static void validatePMMLVsSchema ( PMML pmml , InputSchema schema ) { List < Model > models = pmml . getModels ( ) ; Preconditions . checkArgument ( models . size ( ) == 1 , "Should have exactly one model, but had %s" , models . size ( ) ) ; Model model = models . get ( 0 ) ; Preconditions . checkArgument ( model instanceof ClusteringModel ) ; Preconditions . checkArgument ( model . getMiningFunction ( ) == MiningFunction . CLUSTERING ) ; DataDictionary dictionary = pmml . getDataDictionary ( ) ; Preconditions . checkArgument ( schema . getFeatureNames ( ) . equals ( AppPMMLUtils . getFeatureNames ( dictionary ) ) , "Feature names in schema don't match names in PMML" ) ; MiningSchema miningSchema = model . getMiningSchema ( ) ; Preconditions . checkArgument ( schema . getFeatureNames ( ) . equals ( AppPMMLUtils . getFeatureNames ( miningSchema ) ) ) ; }
Validates that the encoded PMML model received matches expected schema.

@param pmml {@link PMML} encoding of KMeans Clustering
@param schema expected schema attributes of KMeans Clustering
Validates that the encoded PMML model received matches expected schema .