simple naive bayes classifier in c#

NaiveBayes.cs

using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Linq;
using System.Text;
using System.Text.RegularExpressions;
using System.Threading.Tasks;
//original https://github.com/bazhenov/naive-bayes-example
namespace NaiveBayesianClasisifier
{
    class Program
    {
        static List<Document> _trainCorpus = new List<Document>
        {
            new Document("spam", "предоставляю услуги бухгалтера"), 
            new Document("spam", "спешите купить виагру"), 
            new Document("ham", "надо купить молоко")
        };

        static string test = "надо купить сигареты";

        static void Main(string[] args)
        {
            var c = new Classifier(_trainCorpus);
            var res = c.IsInClassProbability("spam", test);
            Console.WriteLine("Should be " + 0.327);
            Console.WriteLine("Actual " + res);
            Console.ReadKey();
        }
    }
    class Document
    {
        public Document(string @class, string text)
        {
            Class = @class;
            Text = text;
        }
        public string Class { get; set; }
        public string Text { get; set; }
    }
    public static class Helpers
    {
        public static List<String> ExtractFeatures(this String text)
        {
            return Regex.Replace(text, "\\p{P}+", "").Split(' ').ToList();
        }
    }

    class ClassInfo
    {
        public ClassInfo(string name, List<String> trainDocs)
        {
            Name = name;
            var features = trainDocs.SelectMany(x => x.ExtractFeatures());
            WordsCount = features.Count();
            WordCount = 
                features.GroupBy(x=>x)
                .ToDictionary(x=>x.Key, x=>x.Count());
            NumberOfDocs = trainDocs.Count;
        }
        public string Name { get; set; }
        public int WordsCount { get; set; }
        public Dictionary<string, int> WordCount { get; set; }
        public int NumberOfDocs { get; set; }
        public int NumberOfOccurencesInTrainDocs(String word)
        {
            if (WordCount.Keys.Contains(word)) return WordCount[word];
            return 0;
        }
    }

    class Classifier
    {
        List<ClassInfo> _classes;
        int _countOfDocs;
        int _uniqWordsCount;
        public Classifier(List<Document> train)
        {
            _classes = train.GroupBy(x => x.Class).Select(g => new ClassInfo(g.Key, g.Select(x=>x.Text).ToList())).ToList();
            _countOfDocs = train.Count;
            _uniqWordsCount = train.SelectMany(x=>x.Text.Split(' ')).GroupBy(x=>x).Count();
        }

        public double IsInClassProbability(string className, string text)
        {
            var words = text.ExtractFeatures();
            var classResults = _classes
                .Select(x => new
                {
                    Result = Math.Pow(Math.E, Calc(x.NumberOfDocs, _countOfDocs, words, x.WordsCount, x, _uniqWordsCount)),
                    ClassName = x.Name
                });


            return classResults.Single(x=>x.ClassName == className).Result / classResults.Sum(x=>x.Result);
        }

        private static double Calc(double dc, double d, List<String> q, double lc, ClassInfo @class, double v)
        {
            return Math.Log(dc / d) + q.Sum(x =>Math.Log((@class.NumberOfOccurencesInTrainDocs(x) + 1) / (v + lc))); 
        }
    }
}