// Copyright Yahoo. 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)); }

}