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.sona.tree.objective.loss;
  
  import com.tencent.angel.sona.tree.objective.metric.EvalMetric;
  import com.tencent.angel.sona.tree.util.MathUtil;
  
  import javax.inject.Singleton;
  
  @Singleton
  public class MultinomialLogisticLoss implements MultiLoss {
      private static MultinomialLogisticLoss instance;
  
      private MultinomialLogisticLoss() {}
  
      @Override
      public Kind getKind() {
          return Kind.MultiLogistic;
      }
  
      @Override
      public EvalMetric.Kind defaultEvalMetric() {
          return EvalMetric.Kind.CROSS_ENTROPY;
      }
  
      @Override
      public double[] firOrderGrad(float[] pred, float label) {
          double[] prob = MathUtil.floatArrayToDoubleArray(pred);
          MathUtil.softmax(prob);
          int trueLabel = (int) label;
          double[] grad = prob;
          for (int i = 0; i < grad.length; i++) {
              grad[i] = (trueLabel == i ? prob[i] - 1.0 : prob[i]);
          }
          return grad;
      }
  
      @Override
      public double[] secOrderGradDiag(float[] pred, float label) {
          double[] prob = MathUtil.floatArrayToDoubleArray(pred);
          MathUtil.softmax(prob);
          double[] hess = prob;
          for (int i = 0; i < hess.length; i++) {
              hess[i] = Math.max(prob[i] * (1.0f - prob[i]), MathUtil.EPSILON);
          }
          return hess;
      }
  
      @Override
      public double[] secOrderGradDiag(float[] pred, float label, double[] firGrad) {
          int trueLabel = (int) label;
          double[] hess = new double[pred.length];
          for (int i = 0; i < hess.length; i++) {
              double prob = trueLabel == i ? firGrad[i] + 1.0 : firGrad[i];
              hess[i] = Math.max(prob * (1.0 - prob), MathUtil.EPSILON);
          }
          return hess;
      }
  
      @Override
      public double[] secOrderGradFull(float[] pred, float label) {
          double[] prob = MathUtil.floatArrayToDoubleArray(pred);
          MathUtil.softmax(prob);
          int numLabel = pred.length;
          double[] hess = new double[numLabel * (numLabel + 1) / 2];
          for (int i = 0; i < numLabel; i++) {
              int rowI = MathUtil.indexOfLowerTriangularMatrix(i, 0);
              for (int j = 0; j < i; j++) {
                  hess[rowI + j] = Math.min(-prob[i] * prob[j], -MathUtil.EPSILON);
              }
              hess[rowI + i] = Math.max(prob[i] * (1.0 - prob[i]), MathUtil.EPSILON);
          }
          return hess;
      }
  
      @Override
      public double[] secOrderGradFull(float[] pred, float label, double[] firGrad) {
          int numLabel = pred.length;
          int trueLabel = (int) label;
          double[] prob = new double[numLabel];
          for (int i = 0; i < numLabel; i++)
              prob[i] = trueLabel == i ? firGrad[i] + 1.0 : firGrad[i];
          double[] hess = new double[numLabel * (numLabel + 1) / 2];
          for (int i = 0; i < numLabel; i++) {
              int rowI = MathUtil.indexOfLowerTriangularMatrix(i, 0);
              for (int j = 0; j < i; j++) {
                  hess[rowI + j] = Math.min(-prob[i] * prob[j], -MathUtil.EPSILON);
              }
              hess[rowI + i] = Math.max(prob[i] * (1.0 - prob[i]), MathUtil.EPSILON);
          }
          return hess;
      }
  
      public static MultinomialLogisticLoss getInstance() {
          if (instance == null)
              instance = new MultinomialLogisticLoss();
          return instance;
      }
  }
  
  