public ActionResult Edit(LanguageVm model)
{
if (ModelState.IsValid)
{
var languages = Mapper.Map <Language>(model);
bool isUpdate = _languageManager.Update(languages);
if (isUpdate)
{
return(RedirectToAction("Index"));
}
}
return(View());
}
public ActionResult Prediction()
{
LanguageVm eng = new LanguageVm
{
Home = "Home",
Graph = "Graphs",
Chart = "Charts",
About = "About",
Contact = "Contact",
MapSrc = "https://ryoeun0.carto.com/builder/3237491e-11e9-11e7-89c3-0e05a8b3e3d7/embed"
};
return(View(eng));
}
public ActionResult Francais()
{
LanguageVm fr = new LanguageVm
{
Home = "Acceuil",
Graph = "Graphiques",
Chart = "Diagrammes",
About = "À propos",
Contact = "Contact",
MapSrc = "https://ryoeun0.carto.com/builder/4ce9f9cc-245d-4d16-95b4-f9ec3a0f9522/embed"
};
return(View("Index", fr));
}
public ActionResult Index()
{
LanguageVm eng = new LanguageVm
{
Home = "Home",
Graph = "Graphs",
Chart = "Charts",
About = "About",
Contact = "Contact",
MapSrc = "https://ryoeun0.carto.com/builder/3237491e-11e9-11e7-89c3-0e05a8b3e3d7/embed"
};
// public string Home { get; set; }
//public string Graph { get; set; }
//public string Chart { get; set; }
//public string About { get; set; }
//public string Contact { get; set; }
//public string MapSrc { get; set; }
return(View(eng));
}
public ActionResult PredictionCalculate(CropYieldRequest mvcModel)
{
// Multiple linear regression can be performed using
// the LinearRegressionModel class.
//
//
// This QuickStart sample uses data test scores of 200 high school
// students, including science, math, and reading.
// First, read the data from a file into a data frame.
var data = DataFrame.ReadCsv(@"C:\SF.Code\Extreme\Extreme\test3.csv");
//// Now create the regression model. Parameters are the data frame,
//// the name of the dependent variable, and a string array containing
//// the names of the independent variables.
//var model = new LinearRegressionModel(data, "science", new string[] {"math", "female", "socst", "read"});
// Alternatively, we can use a formula to describe the variables
// in the model. The dependent variable goes on the left, the
// independent variables on the right of the ~:
//var model2 = new LinearRegressionModel(data,
// "science ~ math + female + socst + read");
var model = new LinearRegressionModel(data, "Yield ~ Temperature + NDVI + Rainfall");
// We can set model options now, such as whether to exclude
// the constant term:
// model.NoIntercept = false;
// The Compute method performs the actual regression analysis.
model.Compute();
// The Parameters collection contains information about the regression
// parameters.
//Console.WriteLine("Variable\t Value Std.Error t-stat p-Value");
double[] ValuesToSend = new double[10];
int count = 0;
foreach (Parameter parameter in model.Parameters)
{
// Parameter objects have the following properties:
Console.WriteLine("{0,-20}\t {1,10:F2}\t {2,10:F6} {3,8:F2}\t {4,7:F5}",
// Name, usually the name of the variable:
parameter.Name,
// Estimated value of the parameter:
parameter.Value,
// Standard error:
parameter.StandardError,
// The value of the t statistic for the hypothesis that the parameter
// is zero.
parameter.Statistic,
// Probability corresponding to the t statistic.
parameter.PValue);
ValuesToSend[count] = parameter.Value; count++;
}
//Console.WriteLine();
// In addition to these properties, Parameter objects have
// a GetConfidenceInterval method that returns
// a confidence interval at a specified confidence level.
// Notice that individual parameters can be accessed
// using their numeric index. Parameter 0 is the intercept,
// if it was included.
//Interval confidenceInterval = model.Parameters[0].GetConfidenceInterval(0.95);
//Console.WriteLine(" for intercept: {0:F4} - {1:F4}",
// confidenceInterval.LowerBound, confidenceInterval.UpperBound);
// Parameters can also be accessed by name:
//confidenceInterval = model.Parameters.Get("NDVI").GetConfidenceInterval(0.95);
//Console.WriteLine("95% confidence interval for 'NDVI': {0:F4} - {1:F4}",
// confidenceInterval.LowerBound, confidenceInterval.UpperBound);
//Console.WriteLine();
// There is also a wealth of information about the analysis available
// through various properties of the LinearRegressionModel object:
//Console.WriteLine("Residual standard error: {0:F3}", model.StandardError);
//Console.WriteLine("R-Squared:\t {0:F4}", model.RSquared);
//Console.WriteLine("Adjusted R-Squared:\t {0:F4}", model.AdjustedRSquared);
//Console.WriteLine("F-statistic: {0:F4}", model.FStatistic);
//Console.WriteLine("Corresponding p-value: {0:F5}", model.PValue);
//Console.WriteLine();
// Much of this data can be summarized in the form of an ANOVA table:
//Console.WriteLine(model.AnovaTable.ToString());
// All this information can be printed using the Summarize method.
// You will also see summaries using the library in C# interactive.
//Console.WriteLine(model.Summarize());
LanguageVm eng = new LanguageVm
{
Home = "Home",
Graph = "Graphs",
Chart = "Charts",
About = "About",
Contact = "Contact",
MapSrc = "https://ryoeun0.carto.com/builder/3237491e-11e9-11e7-89c3-0e05a8b3e3d7/embed"
};
ViewBag.CropYield = (ValuesToSend[0] +
ValuesToSend[1] * mvcModel.Temperature +
ValuesToSend[2] * mvcModel.NDVI +
ValuesToSend[3] * mvcModel.Rainfall).ToString("f2");
ViewBag.RSquared = model.RSquared.ToString("f4");
return(View("Prediction", eng));
}
public ActionResult Create()
{
var model = new LanguageVm();
return(View(model));
}
