// Metodas, kuris nuskaito duomenis <Pusiau apmokytam modeliui>
public static List <Machine_LearningML.Model.ModelInput> GetDataWeak(string path)
{
classes.Clear();
List <Machine_LearningML.Model.ModelInput> data = new List <Machine_LearningML.Model.ModelInput>();
string line;
bool x = false;
System.IO.StreamReader file = new System.IO.StreamReader(path);
while ((line = file.ReadLine()) != null)
{
if (x)
{
string[] arr = line.Split(',');
Machine_LearningML.Model.ModelInput sampleData = new Machine_LearningML.Model.ModelInput()
{
Price = Convert.ToSingle(arr[0]),
CPU = Convert.ToSingle(arr[2]),
Cores = Convert.ToSingle(arr[3]),
RAM = Convert.ToSingle(arr[4]),
SSDorHDD = Convert.ToSingle(arr[5]),
StorageCapacity = Convert.ToSingle(arr[6]),
VRAM = Convert.ToSingle(arr[7]),
Diagonal = Convert.ToSingle(arr[8]),
Weight = Convert.ToSingle(arr[9]),
BatteryCapacity = Convert.ToSingle(arr[10]),
RefreshRate = Convert.ToSingle(arr[11]),
};
data.Add(sampleData);
classes.Add(Convert.ToInt32(arr[12]));
}
x = true;
}
file.Close();
return(data);
}
// For more info on consuming ML.NET models, visit https://aka.ms/mlnet-consume
// Method for consuming model in your app
public static ModelOutput Predict(ModelInput input)
{
ModelOutput result = PredictionEngine.Value.Predict(input);
return(result);
}
