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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.ignite.ml.tree.randomforest.data;
import java.util.Arrays;
import java.util.List;
import org.apache.ignite.ml.IgniteModel;
import org.apache.ignite.ml.math.primitives.vector.Vector;
/**
* Decision tree node class.
*/
public class TreeNode implements IgniteModel<Vector, Double> {
/** Serial version uid. */
private static final long serialVersionUID = -8546263332508653661L;
/**
* Type of node.
*/
public enum Type {
/** Unknown. */
UNKNOWN,
/** Leaf node. */
LEAF,
/** Conditional node. */
CONDITIONAL
}
/** Id. */
private NodeId id;
/** Feature id. */
private int featureId;
/** Value. */
private double val;
/** Type. */
private Type type;
/** Impurity. */
private double impurity;
/** Depth. */
private int depth;
/** Left branch. */
private TreeNode left;
/** Right branch. */
private TreeNode right;
/**
* Create an instance of TreeNode.
*
* @param id Id in according to breadth-first search ordering.
* @param treeId Tree id.
*/
public TreeNode(long id, int treeId) {
this.id = new NodeId(treeId, id);
this.val = -1;
this.type = Type.UNKNOWN;
this.impurity = Double.POSITIVE_INFINITY;
this.depth = 1;
}
/** */
public TreeNode() {
}
/** {@inheritDoc} */
@Override public Double predict(Vector features) {
assert type != Type.UNKNOWN;
if (type == Type.LEAF)
return val;
else {
if (features.get(featureId) <= val)
return left.predict(features);
else
return right.predict(features);
}
}
/**
* Returns leaf node for feature vector in according to decision tree.
*
* @param features Features.
* @return Node.
*/
public NodeId predictNextNodeKey(Vector features) {
switch (type) {
case UNKNOWN:
return id;
case LEAF:
return id;
default:
if (features.get(featureId) <= val)
return left.predictNextNodeKey(features);
else
return right.predictNextNodeKey(features);
}
}
/**
* Convert node to conditional node.
*
* @param featureId Feature id.
* @param val Value.
*/
public List<TreeNode> toConditional(int featureId, double val) {
assert type == Type.UNKNOWN;
toLeaf(val);
left = new TreeNode(2 * id.getNodeId(), id.getTreeId());
right = new TreeNode(2 * id.getNodeId() + 1, id.getTreeId());
this.type = Type.CONDITIONAL;
this.featureId = featureId;
left.depth = right.depth = depth + 1;
return Arrays.asList(left, right);
}
/**
* Convert node to leaf.
*
* @param val Value.
*/
public void toLeaf(double val) {
assert type == Type.UNKNOWN;
this.val = val;
this.type = Type.LEAF;
this.left = null;
this.right = null;
}
/** */
public NodeId getId() {
return id;
}
/** */
public void setVal(double val) {
this.val = val;
}
/** */
public Type getType() {
return type;
}
/** */
public double getVal() {
return val;
}
/** */
public void setImpurity(double impurity) {
this.impurity = impurity;
}
/**
* @return Impurity in current node.
*/
public double getImpurity() {
return impurity;
}
/**
* @return Depth of current node.
*/
public int getDepth() {
return depth;
}
/**
* @return Feature id.
*/
public int getFeatureId() {
return featureId;
}
/**
* @return Right subtree.
*/
public TreeNode getLeft() {
return left;
}
/**
* @return Left subtree.
*/
public TreeNode getRight() {
return right;
}
}