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/* ******************************************************************************
 * Copyright (c) 2015-2018 Skymind, Inc.
 * Copyright (c) 2019 Konduit K.K.
 *
 * 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.
 *
 * 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.
 *
 * SPDX-License-Identifier: Apache-2.0
 ******************************************************************************/

package org.nd4j.linalg.lossfunctions;

import org.nd4j.linalg.lossfunctions.impl.*;


/**
 * Central class for loss functions
 * @author Adam Gibson
 */
public class LossFunctions {

    /**
     * MSE: Mean Squared Error: Linear Regression - {@link LossMSE}

     * l1: L1 loss (absolute value) - {@link LossL1}

     * XENT: Cross Entropy: Binary Classification - {@link LossBinaryXENT}

     * MCXENT: Multiclass Cross Entropy - {@link LossMCXENT}

     * SPARSE_MCXENT: Sparse multi-class cross entropy - {@link LossSparseMCXENT}

     * SQUARED_LOSS: Alias for mean squared error - {@link LossMSE}

     * NEGATIVELOGLIKELIHOOD: Negative Log Likelihood - {@link LossNegativeLogLikelihood}

     * COSINE_PROXIMITY: Cosine proximity loss - {@link LossCosineProximity}

     * HINGE: Hinge loss - {@link LossHinge}

     * SQUARED_HINGE: Squared hinge loss - {@link LossSquaredHinge}

     * KL_DIVERGENCE: Kullback-Leibler divergence loss - {@link LossKLD}

     * MEAN_ABSOLUTE_ERROR: mean absolute error loss - {@link LossMAE}

     * L2: L2 loss (sum of squared errors) - {@link LossL2}

     * MEAN_ABSOLUTE_PERCENTAGE_ERROR: MAPE loss - {@link LossMAPE}

     * MEAN_SQUARED_LOGARITHMIC_ERROR: MSLE loss - {@link LossMSLE}

     * POISSON: Poisson loss - {@link LossPoisson}

     * WASSERSTEIN: Wasserstein loss - {@link LossWasserstein}
     */
    public enum LossFunction {
        MSE, L1, XENT, MCXENT, SPARSE_MCXENT, SQUARED_LOSS, RECONSTRUCTION_CROSSENTROPY, NEGATIVELOGLIKELIHOOD, COSINE_PROXIMITY, HINGE,
        SQUARED_HINGE, KL_DIVERGENCE, MEAN_ABSOLUTE_ERROR, L2, MEAN_ABSOLUTE_PERCENTAGE_ERROR, MEAN_SQUARED_LOGARITHMIC_ERROR, POISSON, WASSERSTEIN;

        public ILossFunction getILossFunction() {
            switch (this) {
                case MSE:
                case SQUARED_LOSS:
                    return new LossMSE();
                case L1:
                    return new LossL1();
                case XENT:
                    return new LossBinaryXENT();
                case MCXENT:
                    return new LossMCXENT();
                case SPARSE_MCXENT:
                    return new LossSparseMCXENT();
                case KL_DIVERGENCE:
                case RECONSTRUCTION_CROSSENTROPY:
                    return new LossKLD();
                case NEGATIVELOGLIKELIHOOD:
                    return new LossNegativeLogLikelihood();
                case COSINE_PROXIMITY:
                    return new LossCosineProximity();
                case HINGE:
                    return new LossHinge();
                case SQUARED_HINGE:
                    return new LossSquaredHinge();
                case MEAN_ABSOLUTE_ERROR:
                    return new LossMAE();
                case L2:
                    return new LossL2();
                case MEAN_ABSOLUTE_PERCENTAGE_ERROR:
                    return new LossMAPE();
                case MEAN_SQUARED_LOGARITHMIC_ERROR:
                    return new LossMSLE();
                case POISSON:
                    return new LossPoisson();
                case WASSERSTEIN:
                    return new LossWasserstein();
                default:
                    //Custom, RMSE_XENT
                    throw new UnsupportedOperationException("Unknown or not supported loss function: " + this);
            }
        }
    }


}