111 lines
3.4 KiB
C#
111 lines
3.4 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.Estimators;
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using BotSharp.Algorithm.Features;
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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.Bayes
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{
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/// <summary>
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/// https://en.wikipedia.org/wiki/Bayes%27_theorem
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/// </summary>
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public class MultinomiaNaiveBayes
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{
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public List<Probability> LabelDist { get; set; }
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public List<Tuple<string, double[]>> FeatureSet { get; set; }
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public double Alpha { get; set; }
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/// <summary>
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/// prior probability
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/// </summary>
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/// <param name="Y"></param>
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/// <returns></returns>
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public double CalPriorProb(string Y)
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{
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int N = FeatureSet.Count;
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int k = LabelDist.Count;
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int Nyk = LabelDist.First(x => x.Value == Y).Freq;
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return (Nyk + Alpha) / (N + k * Alpha);
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}
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/// <summary>
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/// calculate posterior probability P(Y|X)
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/// X is feature set, Y is label
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/// P(X1,...,Xn|Y) = Sum(P(X1|Y) +...+ P(Xn|Y)
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/// P(X, Y) = P(Y|X)P(X) = P(X|Y)P(Y) => P(Y|X) = P(Y)P(X|Y)/P(X)
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/// </summary>
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public double PosteriorProb(string Y, double[] features, double priorProb)
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{
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Alpha = 0.5;
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int featureCount = features.Length;
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double postProb = priorProb;
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// posterior probability P(X1,...,Xn|Y) = Sum(P(X1|Y) +...+ P(Xn|Y)
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var featuresIfY = FeatureSet.Where(fd => fd.Item1 == Y).ToList();
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var matrix = ConstructMatrix(featuresIfY);
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// loop features
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for (int x = 0; x < featureCount; x++)
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{
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int freq = 0;
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for (int y = 0; y < featuresIfY.Count; y++)
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{
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if(matrix[y, x] == features[x])
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{
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freq++;
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}
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}
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int Nyk = featuresIfY.Count;
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int n = featureCount;
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int Nykx = freq;
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postProb += Math.Log((Nykx + Alpha) / (Nyk + n * Alpha));
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}
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return Math.Pow(2, postProb);
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}
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private double[,] ConstructMatrix(List<Tuple<string, double[]>> featuresIfY)
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{
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var featureCount = featuresIfY[0].Item2.Length;
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double[,] matrix = new double[featuresIfY.Count, featureCount];
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for (int y = 0; y < featuresIfY.Count; y++)
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{
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for (int x = 0; x < featureCount; x++)
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{
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matrix[y, x] = featuresIfY[y].Item2[x];
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}
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}
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return matrix;
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}
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}
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}
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