public static TreeNode TreeNodeFactory(GeneticAlgorithmManager ga_mgr, bool ShouldForceTerminal, Tree tree)
{
TreeNode node_output;
bool term_node = ga_mgr.rando.NextDouble() > ga_mgr._gaOptions.prob_node_terminal;
//TODO: consider changing this or using some other scheme to prevent runaway initial trees.
if (term_node || ShouldForceTerminal || tree._nodes.Count > ga_mgr._gaOptions.max_node_count_for_new_tree)
{
var node = new ClassificationTreeNode();
node.CreateRandom(ga_mgr);
node_output = node;
}
else
{
var node = new YesNoMissingTreeNode();
node.CreateRandom(ga_mgr);
node_output = node;
}
//TODO there might be a better place for this
node_output.matrix = new ConfusionMatrix(ga_mgr.dataPointMgr.classes.Length);
tree.AddNodeWithoutChildren(node_output);
return(node_output);
}
public static TreeNode SplitClassificationNode(ClassificationTreeNode nodeToReplace, GeneticAlgorithmManager ga_mgr)
{
var nodeNewDecision = new YesNoMissingTreeNode();
nodeNewDecision.CreateRandom(ga_mgr);
//TODO unhardcode this items
var item1 = 0.0;
var item2 = 0.0;
var item3 = 0.0;
//create the two classification nodes
var nodeTrue = new ClassificationTreeNode();
nodeTrue.Classification = item1;
nodeTrue._parent = nodeNewDecision;
var nodeFalse = new ClassificationTreeNode();
nodeFalse.Classification = item2;
nodeFalse._parent = nodeNewDecision;
var nodeMissing = new ClassificationTreeNode();
nodeMissing.Classification = item3;
nodeMissing._parent = nodeNewDecision;
nodeNewDecision._trueNode = nodeTrue;
nodeNewDecision._falseNode = nodeFalse;
nodeNewDecision._missingNode = nodeMissing;
ReplaceOneNodeWithAnother(nodeToReplace, nodeNewDecision);
//return the new tree with that change
//try to optimize the node
bool opTest = OptimizeTest(nodeNewDecision, ga_mgr);
return(nodeNewDecision);
}
public static IEnumerable <Tree> SplitNodeAndOptimizeTests(GeneticAlgorithmManager ga_mgr, List <Tree> treesInPopulation)
{
//find a grab a tree
Tree tree1 = treesInPopulation[ga_mgr.rando.Next(treesInPopulation.Count())];
//uses the traversal count for selecting
var node_picker = new WeightedSelector <ClassificationTreeNode>(
tree1.GetNodesOfType <ClassificationTreeNode>().Select(c => Tuple.Create(c, (double)c._traverseCount))
);
ClassificationTreeNode node1 = node_picker.PickRandom(ga_mgr.rando);
tree1.SetStructuralLocationsForNodes();
if (node1.matrix == null)
{
}
var matrix_rows = node1.matrix.GetRowsOrderedByCount().ToList();
//trap is here in case there are fewer than 2 "top" rows to split on
if (matrix_rows.Count >= 2)
{
//TODO improve this structural business to be cleaner and more obvious if it belongs to the Node or Tree
Tree tree1_copy = tree1.Copy();
TreeNode node1_copy = tree1_copy.GetNodeAtStructualLocation(node1._structuralLocation);
//grab the node with the greatest traverse count (that is terminal)
//determine the two most popular classes from there (rows in the confusion table)
//create a random test for the current node (change from classification to decision)
//TODO create a proper factory for this code
var node1_decision = new YesNoMissingTreeNode();
node1_decision.CreateRandom(ga_mgr);
node1_decision._parent = node1_copy._parent;
var item1 = node1.Classification;
var item2 = 0;
var item3 = 0;
//create the two classification nodes
var node1a_class = new ClassificationTreeNode();
node1a_class.Classification = item1;
node1a_class._parent = node1_decision;
var node1b_class = new ClassificationTreeNode();
node1b_class.Classification = item2;
node1b_class._parent = node1_decision;
var node1c_class = new ClassificationTreeNode();
node1c_class.Classification = item3;
node1c_class._parent = node1_decision;
node1_decision._trueNode = node1a_class;
node1_decision._falseNode = node1b_class;
node1_decision._missingNode = node1c_class;
//add the two nodes with the most popular classes
//TODO create a "replace node" operation to standardize this code
tree1_copy.RemoveNodeWithChildren(node1_copy);
node1_copy.UpdateParentReference(node1_decision);
tree1_copy.AddNodeWithChildren(node1_decision);
//return the new tree with that change
//try to optimize the node
bool opTest = OptimizeTest(node1_decision, ga_mgr);
tree1_copy._source = "node split";
if (opTest)
{
tree1_copy._source += " w/ op";
}
yield return(tree1_copy);
}
}