/*
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 *  * This program and the accompanying materials are made available under the
 *  * terms of the Apache License, Version 2.0 which is available at
 *  * https://www.apache.org/licenses/LICENSE-2.0.
 *  *
 *  *  See the NOTICE file distributed with this work for additional
 *  *  information regarding copyright ownership.
 *  * Unless required by applicable law or agreed to in writing, software
 *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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package org.deeplearning4j.earlystopping.scorecalc;

import org.deeplearning4j.nn.api.Model;
import com.fasterxml.jackson.annotation.JsonInclude;
import com.fasterxml.jackson.annotation.JsonSubTypes;
import com.fasterxml.jackson.annotation.JsonTypeInfo;

import java.io.Serializable;

@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
@JsonInclude(JsonInclude.Include.NON_NULL)
@JsonSubTypes(value = {
                @JsonSubTypes.Type(value = DataSetLossCalculator.class, name = "BestScoreEpochTerminationCondition"),
                @JsonSubTypes.Type(value = DataSetLossCalculatorCG.class, name = "MaxEpochsTerminationCondition"),

})
public interface ScoreCalculator<T extends Model> extends Serializable {

    /** Calculate the score for the given MultiLayerNetwork */
    double calculateScore(T network);

    /**
     * @return If true: the score should be minimized. If false: the score should be maximized.
     */
    boolean minimizeScore();
}
