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Apache Ignite® is a Distributed Database For High-Performance Computing With In-Memory Speed.
/*
* 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;
/**
* Decision tree conditional (non-leaf) node.
*/
public class DecisionTreeConditionalNode implements DecisionTreeNode {
/** */
private static final long serialVersionUID = 981630737007982172L;
/** Column of the value to be tested. */
private final int col;
/** Threshold. */
private final double threshold;
/** Node that will be used in case tested value is greater then threshold. */
private final DecisionTreeNode thenNode;
/** Node that will be used in case tested value is not greater then threshold. */
private final DecisionTreeNode elseNode;
/**
* Constructs a new instance of decision tree conditional node.
*
* @param col Column of the value to be tested.
* @param threshold Threshold.
* @param thenNode Node that will be used in case tested value is greater then threshold.
* @param elseNode Node that will be used in case tested value is not greater then threshold.
*/
DecisionTreeConditionalNode(int col, double threshold, DecisionTreeNode thenNode, DecisionTreeNode elseNode) {
this.col = col;
this.threshold = threshold;
this.thenNode = thenNode;
this.elseNode = elseNode;
}
/** {@inheritDoc} */
@Override public Double apply(double[] features) {
return features[col] > threshold ? thenNode.apply(features) : elseNode.apply(features);
}
/** */
public int getCol() {
return col;
}
/** */
public double getThreshold() {
return threshold;
}
/** */
public DecisionTreeNode getThenNode() {
return thenNode;
}
/** */
public DecisionTreeNode getElseNode() {
return elseNode;
}
}
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