實現高斯樸素貝葉斯

Question

我試圖在C＃中實現高斯樸素貝葉斯分類的點。我有 實現的第一部分（http://www.statsoft.com/textbook/naive-bayes-classifier/）的概率部分，但我不明白如何實現高斯樸素貝葉斯算法的正常模型。 這是我的代碼：

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; 
     } 
    } 

在這個PDF文件（問題5）是什麼，我需要做的（http://romanager.ro/s.10-701.hw1.sol.pdf）的說明。我的工作是實現Gaussina Naive Bayes和kNN算法，並將結果與​​一組數據進行比較。 請教我在何處以及如何實現高斯樸素貝葉斯算法。

謝謝！

Answer 1

很好的例子：

look at sex predictor at bottom of this page as an example with a real data set

這裏很好的解釋：

naive bayes breakdown on stackoverflow