public void CrossValidation(int howManyValidationFolds)
{
var dataCount = youtubeChannel.Count;
var range = dataCount / howManyValidationFolds;
var values = youtubeChannel.Values.ToList();
Console.WriteLine("Full Data: " + dataCount + " inputs. For testing we take 1/" + howManyValidationFolds + " of data. That is: " + range + "\n");
int start = 0;
Bayes bayes = new Bayes();
List <string> knnResults = new List <string>();
List <string> bayesResults = new List <string>();
for (int i = 0; i < howManyValidationFolds; i++)
{
var testData = values.GetRange(start, range).ToDictionary(x => x.channelName); //paimamas range kiekis duomeų - testavimui
var trainData = values.GetRange(0, start).Concat(values.GetRange(start + range, dataCount - start - range)).ToDictionary(x => x.channelName); //paimami likusieji duomenys mokymuisi
start += range;
var fullData = DivideData(youtubeChannel, howManyValidationFolds);
var dividedData = DivideData(trainData, howManyValidationFolds);
//čia kviečiam algoritmo magijas
KNN knn = new KNN(fullData, dividedData, howManyValidationFolds);
bayes.Train(dividedData);
bayesResults.Add(bayes.Test(fullData, testData));
knnResults.Add(knn.Test(testData));
}
Console.WriteLine(new string('-', 40));
foreach (var item in knnResults)
{
Console.WriteLine(item);
}
Console.WriteLine(new string('-', 40));
foreach (var item in bayesResults)
{
Console.WriteLine(item);
}
Console.WriteLine(new string('-', 40));
}
