public ForecastDto Calculate_forecast(string historyId, FeatureDto[] features)
{
var history = _repo.Load_history_by_id(historyId);
Update_lastused(history);
var forecast = _forecasting.Calculate(history.HistoricalData, Map_features());
var limitedForecast = Limit_to_range(forecast, 0.5f, 1.0f);
return(new ForecastDto {
Id = history.Id,
Distribution = limitedForecast.Distribution.Select(d => new ForecastDto.PossibleOutcomeDto {
Prognosis = d.Prognosis,
Count = d.Count,
CummulatedProbability = d.CummulatedProbability
}).ToArray(),
});
Forecast Limit_to_range(Forecast unlimitedForecast, float pMin, float pMax)
{
return(new Forecast {
Features = unlimitedForecast.Features,
Distribution = unlimitedForecast.Distribution.Where(e => pMin <= e.CummulatedProbability &&
e.CummulatedProbability <= pMax)
.ToArray()
});
}
Feature[] Map_features()
=> features.Select(f => new Feature {
Tags = f.Tags, Quantity = f.Quantity
}).ToArray();
void Update_lastused(History h)
{
h.LastUsed = DateTime.Now.ToUniversalTime();
_repo.Store_history(h);
}
}
public void Forecast()
{
var rndNumbers = new Queue <int>(new[] { 0, 2, 1, 1, 5, 3, 3, 4, 2 });
var montecarlo = new MonteCarloSimulation(_ => rndNumbers.Dequeue());
var sut = new Forecasting(montecarlo, 3, 2);
//TODO: integrationstest forecasting
var historicalData = new[] {
new History.Datapoint {
Value = 1f, Tags = new[] { "a" }
},
new History.Datapoint {
Value = 2f, Tags = new[] { "a" }
},
new History.Datapoint {
Value = 2f, Tags = new[] { "a" }
},
new History.Datapoint {
Value = 3f, Tags = new[] { "a" }
},
new History.Datapoint {
Value = 3f, Tags = new[] { "a" }
},
new History.Datapoint {
Value = 4f, Tags = new[] { "a" }
},
new History.Datapoint {
Value = 10f, Tags = new[] { "b" }
},
new History.Datapoint {
Value = 10f, Tags = new[] { "b" }
},
new History.Datapoint {
Value = 20f, Tags = new[] { "b" }
},
new History.Datapoint {
Value = 20f, Tags = new[] { "b" }
},
new History.Datapoint {
Value = 30f, Tags = new[] { "b" }
}
};
var features = new[] {
new Feature {
Quantity = 2, Tags = new[] { "a" }
},
new Feature {
Quantity = 1, Tags = new[] { "b" }
},
};
/*
* 3 Simulationen, 2 Intervalle
* 2a(0..5), 1b(0..4)
*
* a: 0=1 1=2 3=3
* a: 2=2 5=4 4=3
* b: 1=10 3=20 2=20
* 13 26 26
*
* 26-13=13
* 13/2=6,5
* 13..19,5, 19,5..26
* (13,1,0.33), (26,2,1.0)
*/
var result = sut.Calculate(historicalData, features);
foreach (var f in result.Features)
{
Debug.WriteLine($"{string.Join(",", f)}");
}
foreach (var po in result.Distribution)
{
Debug.WriteLine($"{po.Prognosis}, {po.CummulatedProbability}");
}
Assert.AreEqual(new[] { "a", "a", "b" }, result.Features);
Assert.AreEqual(2, result.Distribution.Length);
Assert.AreEqual(3, result.Distribution.Select(g => g.Count).Sum());
Assert.AreEqual(13f, result.Distribution[0].Prognosis);
Assert.AreEqual(1, result.Distribution[0].Count);
Assert.AreEqual(0.33f, result.Distribution[0].CummulatedProbability, 0.01f);
Assert.AreEqual(26f, result.Distribution[1].Prognosis);
Assert.AreEqual(2, result.Distribution[1].Count);
Assert.AreEqual(1f, result.Distribution[1].CummulatedProbability, 0.01f);
}