实现高斯朴素贝叶斯答案

【问题标题】：Implement Gaussian Naive Bayes实现高斯朴素贝叶斯
【发布时间】：2012-04-09 05:44:30
【问题描述】：

我正在尝试在 C# 中实现高斯朴素贝叶斯来对点进行分类。我有实现了第一部分（http://www.statsoft.com/textbook/naive-bayes-classifier/）概率部分，但我不明白如何实现高斯朴素贝叶斯算法正常模型。这是我的代码：

class NaiveBayesClassifier
    {
        private List<Point> listTrainPoints = new List<Point>();
        private int totalPoints = 0;

        public NaiveBayesClassifier(List<Point> listTrainPoints) 
        {
            this.listTrainPoints = listTrainPoints;
            this.totalPoints = this.listTrainPoints.Count;
        }

        private List<Point> vecinityPoints(Point p, double maxDist)
        {
            List<Point> listVecinityPoints = new List<Point>();
            for (int i = 0; i < listTrainPoints.Count; i++)
            {
                if (p.distance(listTrainPoints[i]) <= maxDist)
                {
                    listVecinityPoints.Add(listTrainPoints[i]);
                }
            }
            return listVecinityPoints;
        }

        public double priorProbabilityFor(double currentType)
        {
            double countCurrentType = 0;
            for (int i = 0; i < this.listTrainPoints.Count; i++)
            {
                if (this.listTrainPoints[i].Type == currentType)
                {
                    countCurrentType++;
                }
            }

            return (countCurrentType / this.totalPoints);
        }

        public double likelihoodOfXGiven(double currentType, List<Point> listVecinityPoints)
        {
            double countCurrentType = 0;
            for (int i = 0; i < listVecinityPoints.Count; i++)
            {
                if (listVecinityPoints[i].Type == currentType)
                {
                    countCurrentType++;
                }
            }

            return (countCurrentType / this.totalPoints);
        }

        public double posteriorProbabilityXBeing(double priorProbabilityFor, double likelihoodOfXGiven)
        {
            return (priorProbabilityFor * likelihoodOfXGiven);
        }

        public int allegedClass(Point p, double maxDist)
        {
            int type1 = 1, type2 = 2;

            List<Point> listVecinityPoints = this.vecinityPoints(p, maxDist);

            double priorProbabilityForType1 = this.priorProbabilityFor(type1);
            double priorProbabilityForType2 = this.priorProbabilityFor(type2);

            double likelihoodOfXGivenType1 = likelihoodOfXGiven(type1, listVecinityPoints);
            double likelihoodOfXGivenType2 = likelihoodOfXGiven(type2, listVecinityPoints);

            double posteriorProbabilityXBeingType1 = posteriorProbabilityXBeing(priorProbabilityForType1, likelihoodOfXGivenType1);
            double posteriorProbabilityXBeingType2 = posteriorProbabilityXBeing(priorProbabilityForType2, likelihoodOfXGivenType2);

            if (posteriorProbabilityXBeingType1 > posteriorProbabilityXBeingType2)
                return type1;
            else
                return type2;
        }
    }

在这个 pdf 文件（问题 5）中描述了我需要做什么（http://romanager.ro/s.10-701.hw1.sol.pdf）。我的工作是实现 Gaussina Naive Bayes 和 kNN 算法，并在一组数据上比较结果。请教我在哪里以及如何实现高斯朴素贝叶斯算法。

谢谢！

【问题讨论】：

Urmelinho：提供赏金，有人可能会提供帮助 :-)
对于一些想法，我不认为有人想从我这里得到赏金......对于这部分算法，我完全出局了。您可能会认为我的感谢将是您对解决方案的奖励。我会考虑任何建议作为解决方案：D

标签： c# algorithm machine-learning bayesian