static void Main(string[] args)
{
if (args.Length == 0)
{
Console.WriteLine("Provide training examples and a test set");
return;
}
List <string> features;
List <KeyValuePair <string, Dictionary <string, object> > > examples = GetExamples(args[0], out features);
NaiveBayesUpdateable bayes = new NaiveBayesUpdateable(features);
foreach (KeyValuePair <string, Dictionary <string, object> > example in examples)
{
bayes.AddExample(example.Key, example.Value);
}
bayes.UpdateClassifier();
NaiveBayes classifier = bayes.Classifier;
List <KeyValuePair <string, Dictionary <string, object> > > testCases = GetExamples(args[1], out features);
foreach (KeyValuePair <string, Dictionary <string, object> > pair in testCases)
{
string className = pair.Key;
Dictionary <string, double> results = classifier.Classify(pair.Value);
Console.WriteLine();
Console.WriteLine("Actual Class: " + className);
foreach (KeyValuePair <string, double> result in results)
{
Console.WriteLine(" " + result.Key + ": " + result.Value.ToString());
}
}
}
protected void btnOut_Click(object sender, EventArgs e)
{
double Symptom = double.Parse(ddlSymptom.SelectedValue);
double Job = double.Parse(ddlJob.SelectedValue);
/*
* 打喷嚏--1
* 头痛--2
*
* 护士--1
* 农夫--2
* 建筑工人--3
* 教师--4
*/
//training data
DataTable table = new DataTable();
table.Columns.Add("disease");
table.Columns.Add("symptom", typeof(double));
table.Columns.Add("job", typeof(double));
table.Rows.Add("感冒", 1, 1);
table.Rows.Add("过敏", 1, 2);
table.Rows.Add("脑震荡", 2, 3);
table.Rows.Add("感冒", 2, 3);
table.Rows.Add("感冒", 1, 4);
table.Rows.Add("脑震荡", 2, 4);
NaiveBayes classifier = new NaiveBayes();
classifier.TrainClassifier(table);
Response.Write(classifier.Classify(new double[] { Symptom, Job }));//新来了打喷嚏的建筑工人，问可能得什么病
}
public void TestParser(string path)
{
var parser = new Parser();
var cases = parser.GetCases(UsersXml, path);
Func <Case, Document> caseTransform =
i =>
i.ToDoc(@case => @case.Area,
@case => @case.Title);
#region Event Text comment
//@case.Events.Aggregate("", (acc, bug) => acc + " " + Regex.Replace(bug.Text, Regex.Escape("[") + "code" + Regex.Escape("]") + ".*" + Regex.Escape("[") + "/code" + Regex.Escape("]"), "")));
#endregion
var total = cases.Count();
var trainingSet = cases.Take((int)(total * (3.0 / 4))).Select(caseTransform).ToList();
var validationSet = cases.Skip((int)(total * (3.0 / 4))).Select(caseTransform).ToList();
var trainedData = NaiveBayes.TrainBayes(trainingSet);
var successRate = 0.0;
foreach (var @case in validationSet)
{
if (NaiveBayes.Classify(@case, trainedData).Name == @case.Class.Name)
{
successRate++;
}
}
successRate = successRate / validationSet.Count();
foreach (var type in trainedData.Probabilities)
{
Console.WriteLine(type.Class.Name);
Console.WriteLine("--------------------");
type.Top(10).ForEach(i => Console.WriteLine("[{1:0.00}] {0}", i.Word, i.Probability));
Console.WriteLine();
Console.WriteLine();
}
Console.WriteLine("Prediction success rate is {0:0.00}%", successRate * 100);
}
private void classifyButton_Click(object sender, RoutedEventArgs e)
{
MessageBox.Show(string.Format("Classified as '{0}'", classifier.Classify(textBoxDescription.Text)));
}
public void calculate()
{
result.text = b.Classify(classType);
}
