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///////////////////////////////////////////////////////////////////////////////
//Copyright (C) 2014 Joliciel Informatique
//
//This file is part of Talismane.
//
//Talismane is free software: you can redistribute it and/or modify
//it under the terms of the GNU Affero General Public License as published by
//the Free Software Foundation, either version 3 of the License, or
//(at your option) any later version.
//
//Talismane is distributed in the hope that it will be useful,
//but WITHOUT ANY WARRANTY; without even the implied warranty of
//MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
//GNU Affero General Public License for more details.
//
//You should have received a copy of the GNU Affero General Public License
//along with Talismane.  If not, see .
//////////////////////////////////////////////////////////////////////////////
package com.joliciel.talismane.machineLearning.perceptron;

/**
 * Different methods of scoring perceptron classifiers.
 * 
 * @author Assaf Urieli
 *
 */
public enum PerceptronScoring {
  /**
   * Use standard additive perceptron scoring, where each state's score is the
   * sum of scores of incremental states.
   */
  additive,
  /**
   * Use a geometric mean of state probabilities, where the probability is
   * calculated by first transforming all scores to positive (minimum = 1), and
   * then dividing by the total.
   */
  normalisedLinear,
  /**
   * Use a geometric mean of state probabilities, where the probability is
   * e^{score/absmax(scores)}, where absmax is the maximum absolute value of
   * scores. This gives us positive scores from 1/e to e. We then divide by the
   * total.
   */
  normalisedExponential
}