/**
* Portions Copyright 2006 DFKI GmbH.
* Portions Copyright 2001 Sun Microsystems, Inc.
* Portions Copyright 1999-2001 Language Technologies Institute,
* Carnegie Mellon University.
* All Rights Reserved. Use is subject to license terms.
*
* Permission is hereby granted, free of charge, to use and distribute
* this software and its documentation without restriction, including
* without limitation the rights to use, copy, modify, merge, publish,
* distribute, sublicense, and/or sell copies of this work, and to
* permit persons to whom this work is furnished to do so, subject to
* the following conditions:
*
* 1. The code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* 2. Any modifications must be clearly marked as such.
* 3. Original authors' names are not deleted.
* 4. The authors' names are not used to endorse or promote products
* derived from this software without specific prior written
* permission.
*
* DFKI GMBH AND THE CONTRIBUTORS TO THIS WORK DISCLAIM ALL WARRANTIES WITH
* REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF
* MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL DFKI GMBH NOR THE
* CONTRIBUTORS BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL
* DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR
* PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF
* THIS SOFTWARE.
*/
package marytts.cart;
import java.util.Properties;
import marytts.features.FeatureDefinition;
import marytts.features.FeatureVector;
import marytts.unitselection.select.Target;
/**
* A tree is a specific kind of directed graph in which each node can have only a single parent node. It consists exclusively of
* DecisionNode and LeafNode nodes.
*
* @author marc
*
*/
public class CART extends DirectedGraph {
/**
* Build a new empty cart
*
*/
public CART() {
}
/**
* Build a new empty cart with the given feature definition.
*
* @param featDef
* featDef
*/
public CART(FeatureDefinition featDef) {
super(featDef);
}
/**
* Build a new cart with the given node as the root node
*
* @param rootNode
* the root node of the CART
* @param featDef
* the feature definition used for interpreting the meaning of decision node criteria.
*/
public CART(Node rootNode, FeatureDefinition featDef) {
super(rootNode, featDef);
}
/**
* Build a new cart with the given node as the root node
*
* @param rootNode
* the root node of the CART
* @param featDef
* the feature definition used for interpreting the meaning of decision node criteria.
* @param properties
* a generic properties object, which can be used to encode information about the tree and the way the data in it
* should be represented.
*/
public CART(Node rootNode, FeatureDefinition featDef, Properties properties) {
super(rootNode, featDef, properties);
}
/**
* Passes the given item through this CART and returns the leaf Node, or the Node it stopped walking down.
*
* @param target
* the target to analyze
* @param minNumberOfData
* the minimum number of data requested. If this is 0, walk down the CART until the leaf level.
*
* @return the Node
*/
public Node interpretToNode(Target target, int minNumberOfData) {
return interpretToNode(target.getFeatureVector(), minNumberOfData);
}
/**
* Passes the given item through this CART and returns the leaf Node, or the Node it stopped walking down.
*
* @param featureVector
* the target to analyze
* @param minNumberOfData
* the minimum number of data requested. If this is 0, walk down the CART until the leaf level.
*
* @return the Node
*/
public Node interpretToNode(FeatureVector featureVector, int minNumberOfData) {
Node currentNode = rootNode;
Node prevNode = null;
// logger.debug("Starting cart at "+nodeIndex);
while (currentNode != null && currentNode.getNumberOfData() > minNumberOfData && !(currentNode instanceof LeafNode)) {
// while we have not reached the bottom,
// get the next node based on the features of the target
prevNode = currentNode;
currentNode = ((DecisionNode) currentNode).getNextNode(featureVector);
// logger.debug(decision.toString() + " result '"+
// decision.findFeature(item) + "' => "+ nodeIndex);
}
// Now usually we will have gone down one level too far
if (currentNode == null || currentNode.getNumberOfData() < minNumberOfData && prevNode != null) {
currentNode = prevNode;
}
assert currentNode.getNumberOfData() >= minNumberOfData || currentNode == rootNode;
try {
assert minNumberOfData > 0 || (currentNode instanceof LeafNode);
} catch (AssertionError e) {
logger.debug(e.getMessage());
}
return currentNode;
}
/**
* Passes the given item through this CART and returns the interpretation.
*
* @param target
* the target to analyze
* @param minNumberOfData
* the minimum number of data requested. If this is 0, walk down the CART until the leaf level.
*
* @return the interpretation
*/
public Object interpret(Target target, int minNumberOfData) {
// get the indices from the leaf node
Object result = this.interpretToNode(target, minNumberOfData).getAllData();
return result;
}
/**
* In this tree, replace the given leaf with the given CART
*
* @param cart
* the CART
* @param leaf
* the leaf
* @return the ex-root node from cart which now replaces leaf.
*/
public static Node replaceLeafByCart(CART cart, LeafNode leaf) {
DecisionNode mother = (DecisionNode) leaf.getMother();
Node newNode = cart.getRootNode();
mother.replaceDaughter(newNode, leaf.getNodeIndex());
newNode.setIsRoot(false);
return newNode;
}
}