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/*
 *  ******************************************************************************
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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.
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 *  *  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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 *  * License for the specific language governing permissions and limitations
 *  * under the License.
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package org.nd4j.linalg.api.ops.impl.loss;

import org.nd4j.autodiff.loss.LossReduce;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.loss.bp.LogLossBp;

import java.util.List;

public class LogLoss extends BaseLoss {
    public static final double DEFAULT_EPSILON = 1e-7;

    private double epsilon;

    public LogLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels, double epsilon){
        super(sameDiff, lossReduce, predictions, weights, labels);
        this.epsilon = epsilon;
        addTArgument(epsilon);
    }

    public LogLoss(SameDiff sameDiff, SDVariable labels, SDVariable predictions, SDVariable weights,
                   LossReduce lossReduce, double epsilon) {
        this(sameDiff, lossReduce, predictions, weights, labels, epsilon);
    }

    public LogLoss(INDArray labels, INDArray predictions, INDArray weights, LossReduce lossReduce, double epsilon){
        super(lossReduce, predictions, weights, labels);
        this.epsilon = epsilon;
        addTArgument(epsilon);
    }


    public LogLoss(){ }

    @Override
    public String opName() {
        return "log_loss";
    }

    @Override
    public List doDiff(List grad){
        //No external gradient
        //Args are: predictions, weights, label
        return new LogLossBp(sameDiff, lossReduce, arg(0), arg(1), arg(2), epsilon).outputs();
    }

}