public void CreateModel()
{
CommonEngine = new InferenceEngine();
cyclist1 = new CyclistPrediction() { InferenceEngine = CommonEngine };
cyclist1.CreateModel();
cyclist2 = new CyclistPrediction() { InferenceEngine = CommonEngine };
cyclist2.CreateModel();
TimeDifference = cyclist1.TomorrowsTime - cyclist2.TomorrowsTime;
Cyclist1IsFaster = cyclist1.TomorrowsTime < cyclist2.TomorrowsTime;
}
public static void RunCyclingTime2()
{
double[] trainingData = new double[] { 13, 17, 16, 12, 13, 12, 14, 18, 16, 16 };
ModelData initPriors = new ModelData(
Gaussian.FromMeanAndPrecision(1.0, 0.01),
Gamma.FromShapeAndScale(2.0, 0.5));
//Train the model
CyclistTraining cyclistTraining = new CyclistTraining();
cyclistTraining.CreateModel();
cyclistTraining.SetModelData(initPriors);
ModelData posteriors1 = cyclistTraining.InferModelData(trainingData);
Console.WriteLine("Average travel time = " + posteriors1.AverageTimeDist);
Console.WriteLine("Traffic noise = " + posteriors1.TrafficNoiseDist);
//Make predictions based on the trained model
CyclistPrediction cyclistPrediction = new CyclistPrediction();
cyclistPrediction.CreateModel();
cyclistPrediction.SetModelData(posteriors1);
Gaussian tomorrowsTimeDist = cyclistPrediction.InferTomorrowsTime();
double tomorrowsMean = tomorrowsTimeDist.GetMean();
double tomorrowsStdDev = Math.Sqrt(tomorrowsTimeDist.GetVariance());
Console.WriteLine("Tomorrows average time: {0:f2}", tomorrowsMean);
Console.WriteLine("Tomorrows standard deviation: {0:f2}", tomorrowsStdDev);
Console.WriteLine("Probability that tomorrow's time is < 18 min: {0}",
cyclistPrediction.InferProbabilityTimeLessThan(18.0));
//Second round of training
double[] trainingData2 = new double[] { 17, 19, 18, 21, 15 };
cyclistTraining.SetModelData(posteriors1);
ModelData posteriors2 = cyclistTraining.InferModelData(trainingData2);
Console.WriteLine("\n2nd training pass");
Console.WriteLine("Average travel time = " + posteriors2.AverageTimeDist);
Console.WriteLine("Traffic noise = " + posteriors2.TrafficNoiseDist);
//Predictions based on two rounds of training
cyclistPrediction.SetModelData(posteriors2);
tomorrowsTimeDist = cyclistPrediction.InferTomorrowsTime();
tomorrowsMean = tomorrowsTimeDist.GetMean();
tomorrowsStdDev = Math.Sqrt(tomorrowsTimeDist.GetVariance());
Console.WriteLine("Tomorrows average time: {0:f2}", tomorrowsMean);
Console.WriteLine("Tomorrows standard deviation: {0:f2}", tomorrowsStdDev);
Console.WriteLine("Probability that tomorrow's time is < 18 min: {0}",
cyclistPrediction.InferProbabilityTimeLessThan(18));
}
public void CreateModel()
{
CommonEngine = new InferenceEngine();
cyclist1 = new CyclistPrediction()
{
InferenceEngine = CommonEngine
};
cyclist1.CreateModel();
cyclist2 = new CyclistPrediction()
{
InferenceEngine = CommonEngine
};
cyclist2.CreateModel();
TimeDifference = cyclist1.TomorrowsTime - cyclist2.TomorrowsTime;
Cyclist1IsFaster = cyclist1.TomorrowsTime < cyclist2.TomorrowsTime;
}