public Form1()
{
InitializeComponent();
forecaster = new Forecaster(Appid);
getWeather(currentCityName);
getForecast(currentCityName);
}
public GameState()
{
FastCoord.Init();
FastShipPosition.Init();
forecaster = new Forecaster(this);
admiral = new Admiral(this);
strateg = new Strateg(this);
}
public void Run()
{
base.Prepare(segment);
var trainer = new Trainer(base.MLContext, base.DataLoader, horizon);
var evaluator = new Evaluator(base.MLContext, base.DataLoader, trainer);
var forecaster = new Forecaster(base.MLContext, trainer);
//UI
var grid = new Grid(90);
//General info
grid.PrintLine();
grid.PrintRow("Mean Absolute Error", evaluator.MAE.ToString("F3"));
grid.PrintRow("Root Mean Squared Error", evaluator.RMSE.ToString("F3"));
grid.PrintLine();
//Evaluation results
var testData = base.DataLoader.TestData;
var predictions = forecaster.Predict();
var forecastOutput =
base.MLContext.Data.CreateEnumerable <ModelInput>(testData, reuseRowObject: false)
.Take(horizon)
.Select((ModelInput model, int index) =>
{
var date = model.Date;
var actualTemps = model.LandAverageTemperature;
var lowerEstimate = predictions.LowerBoundLandAverageTemperature[index];
var estimate = predictions.ForecastedLandAverageTemperature[index];
var upperEstimate = predictions.UpperBoundLandAverageTemperature[index];
return(new List <string> {
date.ToShortDateString(),
"Yellow|" + actualTemps.ToString("F3"),
"Yellow|" + estimate.ToString("F3"),
lowerEstimate.ToString("F3"),
upperEstimate.ToString("F3")
});
});
grid.PrintLine();
grid.PrintRow("Date", "Yellow|Actual", "Yellow|Forecase", "Lower", "Upper");
grid.PrintLine();
// Output predictions
foreach (var prediction in forecastOutput)
{
grid.PrintRow(prediction.ToArray());
grid.PrintLine();
}
}
static int Main(string[] args)
{
ServiceProvider serviceProvider = CreateServiceProvider();
var logger = serviceProvider.GetService <ILogger <Program> >();
var writer = serviceProvider.GetService <FileWriter>();
return(Parser.Default.ParseArguments <NormalizeOptions, GenerateOptions, PaintOptions, ClusterizationOptions, ForecastOptions>(args)
.MapResult(
(GenerateOptions opts) => Generator.Generate(opts, logger, writer),
(NormalizeOptions opts) => Normalizer.Normalize(opts, logger),
(PaintOptions opts) => Painter.Paint(opts, writer, logger),
(ClusterizationOptions opts) => Clusterizer.Clusterize(opts, logger, writer),
(ForecastOptions opts) => Forecaster.Forecast(opts, writer, logger),
errs => HandleParseError(errs, logger)));
}
public async Task ShouldReturnForecastWeCalled()
{
// Assign
var forecaster = new Forecaster();
var client = new Mock <IWeatherServiceClient>();
IWeatherForecast forecastToReturn = new WeatherForecast(10);
client.Setup(c => c.FetchForecast()).Returns(Task.FromResult(forecastToReturn));
// Act
var result = await forecaster.Forecast(client.Object);
// Assert
var expected = 10;
Assert.AreEqual(expected, result.DegreesForToday);
}
public void Run(int year, int month)
{
var predictionForIndex = ((year - segment.Year) * 12) + month;
var trainer = new Trainer(base.MLContext, base.DataLoader, predictionForIndex);
var forecaster = new Forecaster(base.MLContext, trainer);
//UI
var grid = new Grid(90);
//Results
var predictions = forecaster.Predict(predictionForIndex);
grid.PrintLine();
//grid.PrintRow("Date", "Yellow|Forecase", "Lower", "Upper");
//grid.PrintLine();
grid.PrintRow(
DateTime.Parse(year + "-" + month + "-1").ToShortDateString(),
"Yellow|" + predictions.ForecastedLandAverageTemperature[predictionForIndex - 1].ToString("F3"),
predictions.LowerBoundLandAverageTemperature[predictionForIndex - 1].ToString("F3"),
predictions.UpperBoundLandAverageTemperature[predictionForIndex - 1].ToString("F3")
);
}