/*
 *  ******************************************************************************
 *  *
 *  *
 *  * 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
 *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
 *  * License for the specific language governing permissions and limitations
 *  * under the License.
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 *  * SPDX-License-Identifier: Apache-2.0
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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.MeanPairwiseSquaredErrorLossBp;

import java.util.List;

/**
 * Mean Pairwise Squared Error Loss
 *
 * @author Paul Dubs
 */
public class MeanPairwiseSquaredErrorLoss extends BaseLoss {
    public MeanPairwiseSquaredErrorLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels){
        super(sameDiff, lossReduce, predictions, weights, labels);
    }

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

    public MeanPairwiseSquaredErrorLoss(INDArray labels, INDArray predictions, INDArray weights, LossReduce lossReduce){
        super(lossReduce, predictions, weights, labels);
    }

    public MeanPairwiseSquaredErrorLoss(){ }

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

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