// 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); }