67 lines
2.1 KiB
C#
67 lines
2.1 KiB
C#
/*
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* BotSharp.Algorithm
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* Copyright (C) 2018 Haiping Chen
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*
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* This program is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with this program. If not, see <http://www.gnu.org/licenses/>.
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*/
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using BotSharp.Algorithm.Statistics;
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Text;
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namespace BotSharp.Algorithm.Estimators
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{
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/// <summary>
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/// Lidstone smoothing is a technique used to smooth categorical data.
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/// In statistics, it's called additive smoothing or Laplace smoothing.
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/// Given an observation x = (x1, …, xd) from a multinomial distribution with N trials, a "smoothed" version of the data gives the estimator.
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/// https://en.wikipedia.org/wiki/Additive_smoothing
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/// Used as Multinomial Naive Bayes
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/// </summary>
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public class Lidstone : IEstimator
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{
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/// <summary>
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/// α > 0 is the smoothing parameter
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/// </summary>
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public double Alpha { get; set; }
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/// <summary>
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/// Probability
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/// </summary>
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/// <param name="dist">distribution</param>
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/// <param name="sample">sample value</param>
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/// <returns></returns>
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public double Prob(List<Probability> dist, string sample)
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{
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if(Alpha == 0)
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{
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Alpha = 0.5D;
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}
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// observation x = (x1, ..., xd)
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var p = dist.Find(f => f.Value == sample);
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int x = p == null ? 0 : p.Freq;
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// N trials
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int _N = dist.Sum(f => f.Freq);
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int _d = dist.Count;
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return (x + Alpha) / (_N + Alpha * _d);
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}
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}
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}
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