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)); }