103 lines
3.3 KiB
C#
103 lines
3.3 KiB
C#
/*
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* BotSharp.NLP Library
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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;
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using BotSharp.Algorithm.Bayes;
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using BotSharp.Algorithm.Estimators;
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using BotSharp.Algorithm.Extensions;
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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.IO;
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using System.Linq;
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using System.Text;
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using System.Threading.Tasks;
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namespace BotSharp.NLP.Classify
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{
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/// <summary>
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/// This is a simple (naive) classification method based on Bayes rule.
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/// It relies on a very simple representation of the document (called the bag of words representation)
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/// This technique works well for topic classification;
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/// say we have a set of academic papers, and we want to classify them into different topics (computer science, biology, mathematics).
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/// Naive Bayes is best for Less training data
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/// </summary>
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public class NaiveBayesClassifier : IClassifier
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{
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private List<Probability> labelDist;
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private MultinomiaNaiveBayes nb = new MultinomiaNaiveBayes();
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/// <summary>
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/// Cache all categories' prior probability
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/// </summary>
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private Dictionary<string, double> PriorPropDictionary = new Dictionary<string, double>();
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public void Train(List<Tuple<string, double[]>> featureSets, ClassifyOptions options)
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{
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labelDist = featureSets.GroupBy(x => x.Item1)
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.Select(x => new Probability
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{
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Value = x.Key,
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Freq = x.Count()
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})
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.ToList();
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nb.LabelDist = labelDist;
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nb.FeatureSet = featureSets;
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// calculate prior prob
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labelDist.ForEach(l => l.Prob = nb.CalPriorProb(l.Value));
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// calculate posterior prob
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}
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public List<Tuple<string, double>> Classify(double[] features, ClassifyOptions options)
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{
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var results = new List<Tuple<string, double>>();
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// calculate prop
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labelDist.ForEach(lf =>
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{
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var prob = nb.PosteriorProb(lf.Value, features, lf.Prob);
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results.Add(new Tuple<string, double>(lf.Value, prob));
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});
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/*Parallel.ForEach(labelDist, (lf) =>
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{
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nb.Y = lf.Value;
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lf.Prob = nb.PosteriorProb();
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});*/
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return results;
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}
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}
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public class FeaturesWithLabel
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{
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public List<Feature> Features { get; set; }
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public string Label { get; set; }
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public FeaturesWithLabel()
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{
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this.Features = new List<Feature>();
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}
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}
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}
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