static void Main(string[] args)
{
IFileLoader fileLoader = new FileLoader.FileLoader();
var arffFilePath = fileLoader.GetPath(string.Empty);
IFileProcesser<string> fileProcesser = new SimpleFileProcceser();
using (var textReader = new StreamReader(arffFilePath, Encoding.UTF8))
{
var processedFile = fileProcesser.Process(textReader.ReadToEnd());
var naiveBayes = new NaiveBayes.NaiveBayes(processedFile, "Drug");
naiveBayes.TrainFromSet();
//for (var i = 0; i < processedFile.Data.Count; i++)
//{
// var testData = new TestDataModel(processedFile.Data[i]);
// var currentResult = naiveBayes.TestNewData(testData.TestData);
// PrintResultValue(currentResult);
//}
var testModel = new TestDataModel();
var result = naiveBayes.TestNewData(testModel.TestData);
PrintResultValue(result);
Console.ReadKey();
};
}
private ResultModel ExecuteNaiveClassiffication(TestDataModel testModel)
{
IFileLoader fileLoader = new FileLoader.FileLoader();
var arffFilePath = fileLoader.GetPath(string.Empty);
IFileProcesser<string> fileProcesser = new SimpleFileProcceser();
using (var textReader = new StreamReader(arffFilePath, Encoding.UTF8))
{
var processedFile = fileProcesser.Process(textReader.ReadToEnd());
var naiveBayes = new NaiveBayes.NaiveBayes(processedFile, "Drug");
naiveBayes.TrainFromSet();
var currentResult = naiveBayes.TestNewData(testModel.TestData);
return currentResult;
}
}
private void loadDataToolStripMenuItem_Click(object sender, EventArgs e)
{
var start = System.DateTime.Now;
IFileLoader fileLoader = new FileLoader.FileLoader();
var arffFilePath = fileLoader.GetPath(string.Empty);
IFileProcesser<string> fileProcesser = new SimpleFileProcceser();
using (var textReader = new StreamReader(arffFilePath, Encoding.UTF8))
{
var processedFile = fileProcesser.Process(textReader.ReadToEnd());
var naiveBayes = new NaiveBayes.NaiveBayes(processedFile, "Drug");
naiveBayes.TrainFromSet();
fileLoader.SaveJsonFileToText(JsonConvert.SerializeObject(naiveBayes.GetModel()), string.Empty);
}
var end = System.DateTime.Now;
var difference = end.Subtract(start).TotalMilliseconds;
MessageBox.Show(@"Processed! in " + difference + " milliseconds", @"Time elapsed", MessageBoxButtons.OK);
}
public void TrainFromSet()
{
_naiveBayesModel = new NaiveBayesModel();
_naiveBayesModel.DataModel = _model;
_naiveBayesModel.TargetAttribute = _targetAttribute;
ArffFileProcesser.Attribute targetAttribute = FindTargetAttribute();
var targetAttribIndex = FindTargetAttribIndex();
if (targetAttribute != null)
{
var totalData = targetAttribute.Values.Count();
// calculate precision
CalculatePrecision();
//calculate probability of the classes identified
CalculateProbability(targetAttribute, totalData);
}
//change foreach continuous values the value according to the formula value = ParteEntera (original value / precision ) * precision
//calculate possibilities foreach class within each value of other attribute
//find the target attribute index
CreateNormalizedData(targetAttribIndex);
IFileLoader loader = new FileLoader.FileLoader();
loader.SaveJsonFileToText(JsonConvert.SerializeObject(_naiveBayesModel), string.Empty);
}
