public static List<double> GetCorrespondingData(Enums.Data data, int productId)
{
FFCEntities db = new FFCEntities();
List<double> values = new List<double>();
if (data == Enums.Data.Daily)
{
var list = db.sp_Forecast_GetProductCountYearDayByProductId(productId).ToList();
values = list.Select(r => Double.Parse(r.Count.ToString())).ToList();
}
else if (data == Enums.Data.Day)
{
var list = db.sp_Forecast_GetProductCountDayByProductId(productId).ToList();
values = list.Select(r => Double.Parse(r.Count.ToString())).ToList();
}
return values;
}
public static List <double> GetCorrespondingData(DataPeriod data, int productId)
{
FFCEntities db = new FFCEntities();
List <double> values = new List <double>();
if (data == DataPeriod.Daily)
{
var list = db.sp_Forecast_GetProductCountYearDayByProductId(productId).ToList();
values = list.Select(r => Double.Parse(r.Count.ToString())).ToList();
}
else if (data == DataPeriod.Day)
{
var list = db.sp_Forecast_GetProductCountDayByProductId(productId).ToList();
values = list.Select(r => Double.Parse(r.Count.ToString())).ToList();
}
return(values);
}
public static void TestMethod2()
{
FFCEntities db = new FFCEntities();
var list = db.sp_Forecast_GetProductCountYearDayByProductId(1).ToList();
List <double> values = list.Select(r => Double.Parse(r.Count.ToString())).ToList();
REngine.SetEnvironmentVariables();
// There are several options to initialize the engine, but by default the following suffice:
REngine engine = REngine.GetInstance();
// .NET Framework array to R vector.
NumericVector testTs = engine.CreateNumericVector(new double[] { 30.02, 29.99, 30.11, 29.97, 30.01, 29.99, 1000 });
//NumericVector testTs = engine.CreateNumericVector(new double[] { 1,2,3,4,5,6});
//NumericVector testTs = engine.CreateNumericVector(values);
//DataFrame datafrate = engine.CreateDataFrame(new List<string> { },);
engine.SetSymbol("testTs", testTs);
// Direct parsing from R script.
//NumericVector group2 = engine.Evaluate("group2 <- c(29.89, 29.93, 29.72, 29.98, 30.02, 29.98)").AsNumeric();
//auto arima for monthly
engine.Evaluate("tsValue <- ts(testTs, frequency=1, start=c(2010, 1, 1))");
engine.Evaluate("library(forecast)");
engine.Evaluate("arimaFit <- Arima(tsValue)");
engine.Evaluate("fcast <- forecast(tsValue, h=5)");
var a = engine.Evaluate("fcast <- forecast(tsValue, h=5)").AsCharacter();
NumericVector forecasts = engine.Evaluate("fcast$mean").AsNumeric();
foreach (var item in forecasts)
{
Console.WriteLine(item);
}
engine.Dispose();
}
public static void TestMethod2()
{
FFCEntities db = new FFCEntities();
var list = db.sp_Forecast_GetProductCountYearDayByProductId(1).ToList();
List<double> values = list.Select(r => Double.Parse(r.Count.ToString())).ToList();
REngine.SetEnvironmentVariables();
// There are several options to initialize the engine, but by default the following suffice:
REngine engine = REngine.GetInstance();
// .NET Framework array to R vector.
NumericVector testTs = engine.CreateNumericVector(new double[] { 30.02, 29.99, 30.11, 29.97, 30.01, 29.99, 1000 });
//NumericVector testTs = engine.CreateNumericVector(new double[] { 1,2,3,4,5,6});
//NumericVector testTs = engine.CreateNumericVector(values);
//DataFrame datafrate = engine.CreateDataFrame(new List<string> { },);
engine.SetSymbol("testTs", testTs);
// Direct parsing from R script.
//NumericVector group2 = engine.Evaluate("group2 <- c(29.89, 29.93, 29.72, 29.98, 30.02, 29.98)").AsNumeric();
//auto arima for monthly
engine.Evaluate("tsValue <- ts(testTs, frequency=1, start=c(2010, 1, 1))");
engine.Evaluate("library(forecast)");
engine.Evaluate("arimaFit <- Arima(tsValue)");
engine.Evaluate("fcast <- forecast(tsValue, h=5)");
var a = engine.Evaluate("fcast <- forecast(tsValue, h=5)").AsCharacter();
NumericVector forecasts = engine.Evaluate("fcast$mean").AsNumeric();
foreach (var item in forecasts)
{
Console.WriteLine(item);
}
engine.Dispose();
}