public ModelData[] InferModelData(double[] trainingData1,
double[] trainingData2)
{
ModelData[] posteriors = new ModelData[2];
posteriors[0] = cyclist1.InferModelData(trainingData1);
posteriors[1] = cyclist2.InferModelData(trainingData2);
return posteriors;
}
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 SetModelData(ModelData modelData)
{
cyclist1.SetModelData(modelData);
cyclist2.SetModelData(modelData);
}
public virtual void SetModelData(ModelData priors)
{
AverageTimePrior.ObservedValue = priors.AverageTimeDist;
TrafficNoisePrior.ObservedValue = priors.TrafficNoiseDist;
}
public void SetModelData(ModelData[] modelData)
{
cyclist1.SetModelData(modelData[0]);
cyclist2.SetModelData(modelData[1]);
}
public void SetModelData(ModelData modelData)
{
cyclist1.SetModelData(modelData);
cyclist2.SetModelData(modelData);
}
public static void RunCyclingTime5()
{
double[] trainingData1 = new double[] { 13, 17, 16, 12, 13, 12, 14, 18, 16, 16, 27, 32 };
double[] trainingData2 = new double[] { 16, 18, 21, 15, 17, 22, 28, 16, 19, 33, 20, 31 };
ModelData initPriors = new ModelData(
Gaussian.FromMeanAndPrecision(15.0, 0.01),
Gamma.FromShapeAndScale(2.0, 0.5));
//Train the model
TwoCyclistsTraining cyclistsTraining = new TwoCyclistsTraining();
cyclistsTraining.CreateModel();
cyclistsTraining.SetModelData(initPriors);
ModelData[] posteriors1 = cyclistsTraining.InferModelData(trainingData1, trainingData2);
Console.WriteLine("Cyclist 1 average travel time: {0}", posteriors1[0].AverageTimeDist);
Console.WriteLine("Cyclist 1 traffic noise: {0}", posteriors1[0].TrafficNoiseDist);
Console.WriteLine("Cyclist 1 average travel time: {0}", posteriors1[1].AverageTimeDist);
Console.WriteLine("Cyclist 2 traffic noise: {0}", posteriors1[1].TrafficNoiseDist);
//Make predictions based on the trained model
TwoCyclistsPrediction cyclistsPrediction = new TwoCyclistsPrediction();
cyclistsPrediction.CreateModel();
cyclistsPrediction.SetModelData(posteriors1);
Gaussian[] posteriors2 = cyclistsPrediction.InferTomorrowsTime();
Console.WriteLine("Cyclist1 tomorrow's travel time: {0}", posteriors2[0]);
Console.WriteLine("Cyclist2 tomorrow's travel time: {0}", posteriors2[1]);
Gaussian timeDifference = cyclistsPrediction.InferTimeDifference();
Bernoulli cyclist1IsFaster = cyclistsPrediction.InferCyclist1IsFaster();
Console.WriteLine("Time difference: {0}", timeDifference);
Console.WriteLine("Probability that cyclist 1 is faster: {0}", cyclist1IsFaster);
}
public static void RunCyclingTime4()
{
double[] trainingData = new double[] { 13, 17, 16, 12, 13, 12, 14, 18, 16, 16, 27, 32 };
ModelData initPriors = new ModelData(
Gaussian.FromMeanAndPrecision(15.0, 0.01),
Gamma.FromShapeAndScale(2.0, 0.5));
CyclistWithEvidence cyclistWithEvidence = new CyclistWithEvidence();
cyclistWithEvidence.CreateModel();
cyclistWithEvidence.SetModelData(initPriors);
double logEvidence = cyclistWithEvidence.InferEvidence(trainingData);
ModelDataMixed initPriorsMixed;
initPriorsMixed.AverageTimeDist = new Gaussian[] {
new Gaussian(15.0, 100),
new Gaussian(30.0, 100) };
initPriorsMixed.TrafficNoiseDist = new Gamma[] {
new Gamma(2.0, 0.5),
new Gamma(2.0, 0.5) };
initPriorsMixed.MixingDist = new Dirichlet(1, 1);
CyclistMixedWithEvidence cyclistMixedWithEvidence = new CyclistMixedWithEvidence();
cyclistMixedWithEvidence.CreateModel();
cyclistMixedWithEvidence.SetModelData(initPriorsMixed);
double logEvidenceMixed = cyclistMixedWithEvidence.InferEvidence(trainingData);
Console.WriteLine("Log evidence for single Gaussian: {0:f2}", logEvidence);
Console.WriteLine("Log evidence for mixture of two Gaussians: {0:f2}", logEvidenceMixed);
}
public virtual void SetModelData(ModelData priors)
{
AverageTimePrior.ObservedValue = priors.AverageTimeDist;
TrafficNoisePrior.ObservedValue = priors.TrafficNoiseDist;
}
