public static void RunCyclingTime3()
{
ModelDataMixed initPriors;
double[] trainingData = new double[] { 13, 17, 16, 12, 13, 12, 14, 18, 16, 16, 27, 32 };
initPriors.AverageTimeDist = new Gaussian[] { new Gaussian(15.0, 100), new Gaussian(30.0, 100) };
initPriors.TrafficNoiseDist = new Gamma[] { new Gamma(2.0, 0.5), new Gamma(2.0, 0.5) };
initPriors.MixingDist = new Dirichlet(1, 1);
CyclistMixedTraining cyclistMixedTraining = new CyclistMixedTraining();
cyclistMixedTraining.CreateModel();
cyclistMixedTraining.SetModelData(initPriors);
ModelDataMixed posteriors = cyclistMixedTraining.InferModelData(trainingData);
Console.WriteLine("Average time distribution 1 = " + posteriors.AverageTimeDist[0]);
Console.WriteLine("Average time distribution 2 = " + posteriors.AverageTimeDist[1]);
Console.WriteLine("Noise distribution 1 = " + posteriors.TrafficNoiseDist[0]);
Console.WriteLine("Noise distribution 2 = " + posteriors.TrafficNoiseDist[1]);
Console.WriteLine("Mixing coefficient distribution = " + posteriors.MixingDist);
CyclistMixedPrediction cyclistMixedPrediction = new CyclistMixedPrediction();
cyclistMixedPrediction.CreateModel();
cyclistMixedPrediction.SetModelData(posteriors);
Gaussian tomorrowsTime = cyclistMixedPrediction.InferTomorrowsTime();
double tomorrowsMean = tomorrowsTime.GetMean();
double tomorrowsStdDev = Math.Sqrt(tomorrowsTime.GetVariance());
Console.WriteLine("Tomorrows expected time: {0:f2}", tomorrowsMean);
Console.WriteLine("Tomorrows standard deviation: {0:f2}", tomorrowsStdDev);
}
static void Main(string[] args)
{
ModelDataMixed initpriors;
double[] trainingData = new double[] { 13, 17, 16, 12, 13, 12, 14, 18, 16, 16, 27, 32 };
initpriors.averageTimeDist = new Gaussian[] { new Gaussian(15.0, 100.0), new Gaussian(30.0, 100.00) };
initpriors.trafficNoiseDist = new Gamma[] { new Gamma(2.0, 0.5), new Gamma(2.0, 0.5) };
initpriors.mixingDist = new Dirichlet(1, 1);
CyclingMixedTraining cyclingMixedTraining = new CyclingMixedTraining();
cyclingMixedTraining.CreateModel();
cyclingMixedTraining.SetModelData(initpriors);
ModelDataMixed posteriors = cyclingMixedTraining.InferModelData(trainingData);
CyclistMixedPrediction cyclistMixedPrediction = new CyclistMixedPrediction();
cyclistMixedPrediction.CreateModel();
cyclistMixedPrediction.SetModelData(posteriors);
Gaussian tomorrowsTime = cyclistMixedPrediction.InferTomorrowsTime();
Console.WriteLine("Tomorrow's predicted time is {0:f2}", tomorrowsTime.GetMean());
Console.WriteLine("Tomorrow's predicted standard deviation is {0:f2}", Math.Sqrt(tomorrowsTime.GetVariance()));
Console.ReadKey();
}
public void SetModelData(ModelDataMixed modelData)
{
averageTimePriors.ObservedValue = modelData.averageTimeDist;
trafficNoisePriors.ObservedValue = modelData.trafficNoiseDist;
mixingPrior.ObservedValue = modelData.mixingDist;
}
