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// Copyright Vespa.ai. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
package com.yahoo.searchlib.rankingexpression.evaluation.gbdtoptimization;
import com.yahoo.searchlib.rankingexpression.Reference;
import com.yahoo.searchlib.rankingexpression.evaluation.Context;
import com.yahoo.searchlib.rankingexpression.evaluation.DoubleValue;
import com.yahoo.searchlib.rankingexpression.evaluation.Value;
import com.yahoo.searchlib.rankingexpression.rule.CompositeNode;
import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode;
import com.yahoo.searchlib.rankingexpression.rule.SerializationContext;
import com.yahoo.tensor.TensorType;
import com.yahoo.tensor.evaluation.TypeContext;
import java.util.Arrays;
import java.util.Deque;
import java.util.Objects;
/**
* An optimized version of a sum of consecutive decision trees.
*
* @author bratseth
*/
public class GBDTForestNode extends ExpressionNode {
private final double[] values;
public GBDTForestNode(double[] values) {
this.values=values;
}
@Override
public final TensorType type(TypeContext<Reference> context) { return TensorType.empty; }
@Override
public final Value evaluate(Context context) {
int pc = 0;
double treeSum = 0;
while (pc < values.length) {
int nextTree = (int)values[pc++];
treeSum += GBDTNode.evaluate(values, pc, context);
pc += nextTree;
}
return new DoubleValue(treeSum);
}
/** Returns (optimized sum of condition trees) */
@Override
public StringBuilder toString(StringBuilder string, SerializationContext context, Deque<String> path, CompositeNode parent) {
return string.append("(optimized sum of condition trees of size ").append(values.length*8).append(" bytes)");
}
@Override
public int hashCode() { return Objects.hash("gbdtForest", Arrays.hashCode(values)); }
}
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