content: /*
   * Tencent is pleased to support the open source community by making Angel available.
   *
   * Copyright (C) 2017-2018 THL A29 Limited, a Tencent company. All rights reserved.
   *
   * Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in
   * compliance with the License. You may obtain a copy of the License at
   *
   * https://opensource.org/licenses/Apache-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.
   *
   */
  
  
  package com.tencent.angel.ml.math2.ufuncs;
  
  import com.tencent.angel.ml.math2.matrix.Matrix;
  import com.tencent.angel.ml.math2.ufuncs.executor.matrix.BinaryMatrixExecutor;
  import com.tencent.angel.ml.math2.ufuncs.expression.CrossEntropyLoss;
  import com.tencent.angel.ml.math2.ufuncs.expression.GradCrossEntropyLoss;
  import com.tencent.angel.ml.math2.ufuncs.expression.GradHingeLoss;
  import com.tencent.angel.ml.math2.ufuncs.expression.GradHuberLoss;
  import com.tencent.angel.ml.math2.ufuncs.expression.GradLogLoss;
  import com.tencent.angel.ml.math2.ufuncs.expression.HingeLoss;
  import com.tencent.angel.ml.math2.ufuncs.expression.HuberLoss;
  import com.tencent.angel.ml.math2.ufuncs.expression.LogLoss;
  
  public class LossFuncs {
  
    public static Matrix hingeloss(Matrix m1, Matrix m2) {
      return BinaryMatrixExecutor.apply(m1, false, m2, false, new HingeLoss(false));
    }
  
    public static Matrix gradhingeloss(Matrix m1, Matrix m2) {
      return BinaryMatrixExecutor.apply(m1, false, m2, false, new GradHingeLoss(false));
    }
  
    public static Matrix logloss(Matrix m1, Matrix m2) {
      return BinaryMatrixExecutor.apply(m1, false, m2, false, new LogLoss(false));
    }
  
    public static Matrix gradlogloss(Matrix m1, Matrix m2) {
      return BinaryMatrixExecutor.apply(m1, false, m2, false, new GradLogLoss(false));
    }
  
    public static Matrix entropyloss(Matrix m1, Matrix m2) {
      return BinaryMatrixExecutor.apply(m1, false, m2, false, new CrossEntropyLoss(false));
    }
  
    public static Matrix gradentropyloss(Matrix m1, Matrix m2) {
      return BinaryMatrixExecutor.apply(m1, false, m2, false, new GradCrossEntropyLoss(false));
    }
  
    public static Matrix huberloss(Matrix m1, Matrix m2, double delta) {
      return BinaryMatrixExecutor.apply(m1, false, m2, false, new HuberLoss(delta, false));
    }
  
    public static Matrix gradhuberloss(Matrix m1, Matrix m2, double delta) {
      return BinaryMatrixExecutor.apply(m1, false, m2, false, new GradHuberLoss(delta, false));
    }
  }