private PredictionViewModel GetModel(SaleDataInputModel inputModel)
{
var displayModel = new PredictionViewModel();
SetMlDisplayPredictionData(displayModel, inputModel);
SetFourthDisplayPredictionData(displayModel);
return(displayModel);
}
public ActionResult Create(SaleDataInputModel inputModel)
{
if (!ModelState.IsValid)
{
RedirectToAction("Index");
}
var model = GetModel(inputModel);
return(View(model));
}
private static void SetMlDisplayPredictionData(PredictionViewModel displayModel, SaleDataInputModel inputModel)
{
var uri = Properties.Settings.Default.Uri;
var key = Properties.Settings.Default.Key;
var client = new SalesDemandService(uri, key);
var estimatedSales = new List <double>();
double[] garlicBreadMay = new double[] { 3, 3, 2, 2, 0, 0, 0 };
var tempDate = inputModel.StartDate;
while (tempDate <= inputModel.EndDate)
{
var data = new Dictionary <string, string>()
{
{ "Locationid", "69" },
{ "RecipeName", "" },
{ "PLU", inputModel.PLU }, // "2549"},
{ "Salesdate", tempDate.ToString() },
{ "Quantity", "1" },
{ "NetSalesPrice", "1" },
{ "CostPrice", "1" },
{ "Year", tempDate.Year.ToString() },
{ "Month", tempDate.Month.ToString() },
{ "Day", tempDate.Day.ToString() },
{ "WeekDay", "1" },
{ "YearDay", "1" }
};
var task = client.GetPrediction1(data);
task.Wait();
var predictionResult = GetScoredLabelsValue(task.Result);
estimatedSales.Add(predictionResult);
tempDate = tempDate.AddDays(1);
}
displayModel.MeanAbsoluteError = MLMathFormulas.MeanAbsoluteError(garlicBreadMay, estimatedSales.ToArray());
displayModel.MeanSquaredError = MLMathFormulas.MeanSquaredError(garlicBreadMay, estimatedSales.ToArray());
var coeff = MLMathFormulas.CoefficientOfDetermination(garlicBreadMay, estimatedSales.ToArray());
displayModel.CoefficientOfDetermination =
//coeff > 0
//? Math.Round(coeff, 6)
//:
estimatedSales.First();
}
