137 lines
4.7 KiB
C#
137 lines
4.7 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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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<FeaturesDistribution> featuresDist;
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private List<Probability> labelDist;
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public void Train(List<FeaturesWithLabel> featureSets, ClassifyOptions options)
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{
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labelDist = featureSets.GroupBy(x => x.Label)
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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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var fNames = featureSets[0].Features.Select(x => x.Name)
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.Distinct()
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.OrderBy(x => x)
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.ToList();
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// combine all features.
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var allFeatureValues = new List<Feature>();
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featureSets.ForEach(fs => fNames.ForEach(fName => allFeatureValues.Add(new Feature(fName, fs.Features.First(x => x.Name == fName).Value))));
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var featureValues = fNames.Select(fn => new
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{
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Name = fn,
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Values = allFeatureValues.Where(x => x.Name == fn).Select(x => x.Value).Distinct().ToList()
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}).ToList();
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featuresDist = new List<FeaturesDistribution>();
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labelDist.Select(x => x.Value).ToList().ForEach(label =>
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{
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var fSets = featureSets.Where(x => x.Label == label);
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fNames.ForEach(fName =>
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{
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var fsv = fSets.Select(fs => fs.Features.First(f => f.Name == fName))
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.GroupBy(f => f.Value)
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.Select(f => new Probability
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{
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Value = f.Key,
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Freq = f.Count()
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})
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.OrderBy(f => f.Value)
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.ToList();
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featuresDist.Add(new FeaturesDistribution
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{
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Label = label,
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FeatureName = fName,
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FeatureValues = fsv
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});
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});
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});
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}
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public List<Tuple<string, double>> Classify(List<Feature> features, ClassifyOptions options)
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{
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// calculate prop
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var nb = new NaiveBayes<Lidstone>();
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nb.LabelDist = labelDist;
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nb.FeaturesDist = featuresDist;
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labelDist.ForEach(lf => lf.Prob = nb.PosteriorProb(lf.Value, features));
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// add log
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double[] logs = labelDist.Select(x => x.Prob).ToArray();
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var sumLogs = logs.Reduce((log1, next) =>
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{
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double min = log1;
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if (next < log1)
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{
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min = next;
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}
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return min + Math.Log(Math.Pow(2, log1 - min) + Math.Pow(2, next - min), 2);
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});
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labelDist.ForEach(d => d.Prob -= sumLogs);
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return labelDist.Select(x => new Tuple<string, double>(x.Value, x.Prob)).ToList();
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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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