private static int buildNominalNode(RandomTree tree, Attribute attribute, RandomTree.Tree node, int nodeId, Node parentPMMLNode) {
        List<Object> values = new ArrayList<>();
        Enumeration enumeration = attribute.enumerateValues();
        while (enumeration.hasMoreElements()) {
            values.add(enumeration.nextElement());
        }

        assert values.size() == node.m_Successors.length : "Number of successors expected to be the same as the number of attribute values";

        List<Node> children = new ArrayList<>();

        for (int i = 0; i < values.size(); i++) {
            Object value = values.get(i);

            SimplePredicate predicate = new SimplePredicate(new FieldName(attribute.name()), SimplePredicate.Operator.EQUAL).withValue(String.valueOf(value));
            Node child = new Node().withId(String.valueOf(++nodeId)).withPredicate(predicate);

            nodeId = buildTreeNode(tree, node.m_Successors[i], nodeId, child);

            // Training proportion extension.
            child.withExtensions(new Extension().withName(TRAINING_PROPORTION_ELEMENT).withValue(String.valueOf(node.m_Prop[i])));

            children.add(child);
        }

        parentPMMLNode.withNodes(children);

        return nodeId;
    }
private static int buildNominalNode(RandomTree tree, Attribute attribute, RandomTree.Tree node, int nodeId, Node parentPMMLNode) {
        List<Object> values = new ArrayList<>();
        Enumeration enumeration = attribute.enumerateValues();
        while (enumeration.hasMoreElements()) {
            values.add(enumeration.nextElement());
        }

        assert values.size() == node.m_Successors.length : "Number of successors expected to be the same as the number of attribute values";

        List<Node> children = new ArrayList<>();

        for (int i = 0; i < values.size(); i++) {
            Object value = values.get(i);

            SimplePredicate predicate = new SimplePredicate(new FieldName(attribute.name()), SimplePredicate.Operator.EQUAL).withValue(String.valueOf(value));
            Node child = new Node().withId(String.valueOf(++nodeId)).withPredicate(predicate);

            nodeId = buildTreeNode(tree, node.m_Successors[i], nodeId, child);

            
            child.withExtensions(new Extension().withName(TRAINING_PROPORTION_ELEMENT).withValue(String.valueOf(node.m_Prop[i])));

            children.add(child);
        }

        parentPMMLNode.withNodes(children);

        return nodeId;
    }
private static int buildNominalNode ( RandomTree tree , Attribute attribute , RandomTree . Tree node , int nodeId , Node parentPMMLNode ) { List < Object > values = new ArrayList <> ( ) ; Enumeration enumeration = attribute . enumerateValues ( ) ; while ( enumeration . hasMoreElements ( ) ) { values . add ( enumeration . nextElement ( ) ) ; } assert values . size ( ) == node . m_Successors . length : "Number of successors expected to be the same as the number of attribute values" ; List < Node > children = new ArrayList <> ( ) ; for ( int i = 0 ; i < values . size ( ) ; i ++ ) { Object value = values . get ( i ) ; SimplePredicate predicate = new SimplePredicate ( new FieldName ( attribute . name ( ) ) , SimplePredicate . Operator . EQUAL ) . withValue ( String . valueOf ( value ) ) ; Node child = new Node ( ) . withId ( String . valueOf ( ++ nodeId ) ) . withPredicate ( predicate ) ; nodeId = buildTreeNode ( tree , node . m_Successors [ i ] , nodeId , child ) ; // Training proportion extension. child . withExtensions ( new Extension ( ) . withName ( TRAINING_PROPORTION_ELEMENT ) . withValue ( String . valueOf ( node . m_Prop [ i ] ) ) ) ; children . add ( child ) ; } parentPMMLNode . withNodes ( children ) ; return nodeId ; }
Builds the {@link org.dmg.pmml.Node PMML Node} for a nominal attribute.
<p/>
In PMML these nodes are represented with multiple children, one for each of the attribute's values.
<p/>
For example, consider a nominal attribute, named "nominalAttribute", with values "cold", "hot" and "warm". In PMML this translates to:
<pre>
{@code
<Node id="2" score="1">
<SimplePredicate field="nominalAttribute" operator="equal" value="cold"/>
</Node>
<Node id="3" score="0">
<SimplePredicate field="nominalAttribute" operator="equal" value="hot"/>
</Node>
<Node id="4" score="1.5">
<SimplePredicate field="nominalAttribute" operator="equal" value="warm"/>
</Node>
}
</pre>

@param tree           The {@link weka.classifiers.trees.RandomTree Weka RandomTree} being converted to a {@link org.dmg.pmml.PMML TreeModel}.
@param attribute      The {@link weka.core.Attribute} to which the node to build refers to.
@param node           The {@link weka.classifiers.trees.RandomTree.Tree Weka RandomTree node} we are converting to PMML.
@param nodeId         The Id to give to the generted {@link org.dmg.pmml.Node PMML Node}.
@param parentPMMLNode The parent {@link org.dmg.pmml.Node PMML Node}.
@return The incremented Id given to recursively created {@link org.dmg.pmml.Node PMML Nodes}.
Builds the { @link org . dmg . pmml . Node PMML Node } for a nominal attribute . <p / > In PMML these nodes are represented with multiple children one for each of the attribute s values . <p / > For example consider a nominal attribute named nominalAttribute with values cold hot and warm . In PMML this translates to : <pre > { @code <Node id = 2 score = 1 > <SimplePredicate field = nominalAttribute operator = equal value = cold / > < / Node > <Node id = 3 score = 0 > <SimplePredicate field = nominalAttribute operator = equal value = hot / > < / Node > <Node id = 4 score = 1 . 5 > <SimplePredicate field = nominalAttribute operator = equal value = warm / > < / Node > } < / pre >