public void Run()
{
// Set random seed for repeatable example
Rand.Restart(12347);
// Create a new model
WetGlassSprinklerRainModel model = new WetGlassSprinklerRainModel();
if (model.Engine.Algorithm is GibbsSampling)
{
Console.WriteLine("This example does not run with Gibbs Sampling");
return;
}
// In this example, each variable just takes two states - true (index 0)
// and false (index 1). The example is written so that extensions to
// problems of more than two states is straightforward.
// -------------------------------------------------------------
// Usage 1: Query the model when we know the parameters exactly
// -------------------------------------------------------------
Console.WriteLine("\n*********************************************");
Console.WriteLine("Querying the model with a known ground truth");
Console.WriteLine("*********************************************");
Vector probCloudy = Vector.FromArray(0.5, 0.5);
Vector[] cptSprinkler = new Vector[] { Vector.FromArray(0.1, 0.9) /* cloudy */, Vector.FromArray(0.5, 0.5) /* not cloudy */ };
Vector[] cptRain = new Vector[] { Vector.FromArray(0.8, 0.2) /* cloudy */, Vector.FromArray(0.2, 0.8) /* not cloudy */ };
Vector[][] cptWetGrass = new Vector[][]
{
new Vector[] { Vector.FromArray(0.99, 0.01) /* rain */, Vector.FromArray(0.9, 0.1) /* not rain */ }, // Sprinkler
new Vector[] { Vector.FromArray(0.9, 0.1) /* rain */, Vector.FromArray(0.0, 1.0) /* not rain */ } // Not sprinkler
};
double probRainGivenWetGrass = model.ProbRain(
null, null, 0,
probCloudy, cptSprinkler, cptRain, cptWetGrass);
double probRainGivenWetGrassNotCloudy = model.ProbRain(
1, null, 0,
probCloudy, cptSprinkler, cptRain, cptWetGrass);
Console.WriteLine("P(rain | grass is wet) = {0:0.0000}", probRainGivenWetGrass);
Console.WriteLine("P(rain | grass is wet, not cloudy ) = {0:0.0000}", probRainGivenWetGrassNotCloudy);
// -------------------------------------------------------------
// Usage 2: Learn posterior distributions for the parameters
// -------------------------------------------------------------
// To validate the learning, first sample from a known model
int[][] sample = WetGlassSprinklerRainModel.Sample(1000, probCloudy, cptSprinkler, cptRain, cptWetGrass);
Console.WriteLine("\n*********************************************");
Console.WriteLine("Learning parameters from data (uniform prior)");
Console.WriteLine("*********************************************");
// Now see if we can recover the parameters from the data - assume uniform priors
model.LearnParameters(sample[0], sample[1], sample[2], sample[3]);
// The posteriors are distributions over the probabilities and CPTs. Print out the means of these
// distributions, and compare with the ground truth
Console.WriteLine("Prob. Cloudy: Ground truth: {0:0.00}, Inferred: {1:0.00}", 0.5, model.ProbCloudyPosterior.GetMean()[0]);
Console.WriteLine("Prob. Sprinkler | Cloudy: Ground truth: {0:0.00}, Inferred: {1:0.00}", 0.1, model.CPTSprinklerPosterior[0].GetMean()[0]);
Console.WriteLine("Prob. Sprinkler | Not Cloudy: Ground truth: {0:0.00}, Inferred: {1:0.00}", 0.5, model.CPTSprinklerPosterior[1].GetMean()[0]);
Console.WriteLine("Prob. Rain | Cloudy: Ground truth: {0:0.00}, Inferred: {1:0.00}", 0.8, model.CPTRainPosterior[0].GetMean()[0]);
Console.WriteLine("Prob. Rain | Not Cloudy: Ground truth: {0:0.00}, Inferred: {1:0.00}", 0.2, model.CPTRainPosterior[1].GetMean()[0]);
Console.WriteLine("Prob. Wet Grass | Sprinkler, Rain: Ground truth: {0:0.00}, Inferred: {1:0.00}", 0.99, model.CPTWetGrassPosterior[0][0].GetMean()[0]);
Console.WriteLine("Prob. Wet Grass | Sprinkler, Not Rain Ground truth: {0:0.00}, Inferred: {1:0.00}", 0.9, model.CPTWetGrassPosterior[0][1].GetMean()[0]);
Console.WriteLine("Prob. Wet Grass | Not Sprinkler, Rain: Ground truth: {0:0.00}, Inferred: {1:0.00}", 0.9, model.CPTWetGrassPosterior[1][0].GetMean()[0]);
Console.WriteLine("Prob. Wet Grass | Not Sprinkler, Not Rain: Ground truth: {0:0.00}, Inferred: {1:0.00}", 0.0, model.CPTWetGrassPosterior[1][1].GetMean()[0]);
// -------------------------------------------------------------
// Usage 3: Querying the model taking into account uncertainty
// of the parameters
// -------------------------------------------------------------
// Use posteriors we have just learnt
Console.WriteLine("\n**********************************************");
Console.WriteLine("Querying the model with uncertain ground truth");
Console.WriteLine("**********************************************");
double probRainGivenWetGrass1 = model.ProbRain(
null, null, 0,
model.ProbCloudyPosterior, model.CPTSprinklerPosterior, model.CPTRainPosterior, model.CPTWetGrassPosterior);
double probRainGivenWetGrassNotCloudy1 = model.ProbRain(
1, null, 0,
model.ProbCloudyPosterior, model.CPTSprinklerPosterior, model.CPTRainPosterior, model.CPTWetGrassPosterior);
Console.WriteLine("P(rain | grass is wet) = {0:0.0000}", probRainGivenWetGrass1);
Console.WriteLine("P(rain | grass is wet, not cloudy ) = {0:0.0000}", probRainGivenWetGrassNotCloudy1);
Console.WriteLine("");
}
public void Run()
{
// Set random seed for repeatable example
Rand.Restart(12347);
// Create a new model
WetGlassSprinklerRainModel model = new WetGlassSprinklerRainModel();
if (model.Engine.Algorithm is GibbsSampling)
{
Console.WriteLine("This example does not run with Gibbs Sampling");
return;
}
// In this example, each variable just takes two states - true (index 0)
// and false (index 1). The example is written so that extensions to
// problems of more than two states is straightforward.
// -------------------------------------------------------------
// Usage 1: Query the model when we know the parameters exactly
// -------------------------------------------------------------
Console.WriteLine("\n*********************************************");
Console.WriteLine("Querying the model with a known ground truth");
Console.WriteLine("*********************************************");
Vector probCloudy = Vector.FromArray(0.5, 0.5);
Vector[] cptSprinkler = new Vector[] { Vector.FromArray(0.1, 0.9) /* cloudy */, Vector.FromArray(0.5, 0.5) /* not cloudy */ };
Vector[] cptRain = new Vector[] { Vector.FromArray(0.8, 0.2) /* cloudy */, Vector.FromArray(0.2, 0.8) /* not cloudy */ };
Vector[][] cptWetGrass = new Vector[][]
{
new Vector[] { Vector.FromArray(0.99, 0.01) /* rain */, Vector.FromArray(0.9, 0.1) /* not rain */}, // Sprinkler
new Vector[] { Vector.FromArray(0.9, 0.1) /* rain */, Vector.FromArray(0.0, 1.0) /* not rain */} // Not sprinkler
};
double probRainGivenWetGrass = model.ProbRain(
null, null, 0,
probCloudy, cptSprinkler, cptRain, cptWetGrass);
double probRainGivenWetGrassNotCloudy = model.ProbRain(
1, null, 0,
probCloudy, cptSprinkler, cptRain, cptWetGrass);
Console.WriteLine("P(rain | grass is wet) = {0:0.0000}", probRainGivenWetGrass);
Console.WriteLine("P(rain | grass is wet, not cloudy ) = {0:0.0000}", probRainGivenWetGrassNotCloudy);
// -------------------------------------------------------------
// Usage 2: Learn posterior distributions for the parameters
// -------------------------------------------------------------
// To validate the learning, first sample from a known model
int[][] sample = WetGlassSprinklerRainModel.Sample(1000, probCloudy, cptSprinkler, cptRain, cptWetGrass);
Console.WriteLine("\n*********************************************");
Console.WriteLine("Learning parameters from data (uniform prior)");
Console.WriteLine("*********************************************");
// Now see if we can recover the parameters from the data - assume uniform priors
model.LearnParameters(sample[0], sample[1], sample[2], sample[3]);
// The posteriors are distributions over the probabilities and CPTs. Print out the means of these
// distributions, and compare with the ground truth
Console.WriteLine("Prob. Cloudy: Ground truth: {0:0.00}, Inferred: {1:0.00}", 0.5, model.ProbCloudyPosterior.GetMean()[0]);
Console.WriteLine("Prob. Sprinkler | Cloudy: Ground truth: {0:0.00}, Inferred: {1:0.00}", 0.1, model.CPTSprinklerPosterior[0].GetMean()[0]);
Console.WriteLine("Prob. Sprinkler | Not Cloudy: Ground truth: {0:0.00}, Inferred: {1:0.00}", 0.5, model.CPTSprinklerPosterior[1].GetMean()[0]);
Console.WriteLine("Prob. Rain | Cloudy: Ground truth: {0:0.00}, Inferred: {1:0.00}", 0.8, model.CPTRainPosterior[0].GetMean()[0]);
Console.WriteLine("Prob. Rain | Not Cloudy: Ground truth: {0:0.00}, Inferred: {1:0.00}", 0.2, model.CPTRainPosterior[1].GetMean()[0]);
Console.WriteLine("Prob. Wet Grass | Sprinkler, Rain: Ground truth: {0:0.00}, Inferred: {1:0.00}", 0.99, model.CPTWetGrassPosterior[0][0].GetMean()[0]);
Console.WriteLine("Prob. Wet Grass | Sprinkler, Not Rain Ground truth: {0:0.00}, Inferred: {1:0.00}", 0.9, model.CPTWetGrassPosterior[0][1].GetMean()[0]);
Console.WriteLine("Prob. Wet Grass | Not Sprinkler, Rain: Ground truth: {0:0.00}, Inferred: {1:0.00}", 0.9, model.CPTWetGrassPosterior[1][0].GetMean()[0]);
Console.WriteLine("Prob. Wet Grass | Not Sprinkler, Not Rain: Ground truth: {0:0.00}, Inferred: {1:0.00}", 0.0, model.CPTWetGrassPosterior[1][1].GetMean()[0]);
// -------------------------------------------------------------
// Usage 3: Querying the model taking into account uncertainty
// of the parameters
// -------------------------------------------------------------
// Use posteriors we have just learnt
Console.WriteLine("\n**********************************************");
Console.WriteLine("Querying the model with uncertain ground truth");
Console.WriteLine("**********************************************");
double probRainGivenWetGrass1 = model.ProbRain(
null, null, 0,
model.ProbCloudyPosterior, model.CPTSprinklerPosterior, model.CPTRainPosterior, model.CPTWetGrassPosterior);
double probRainGivenWetGrassNotCloudy1 = model.ProbRain(
1, null, 0,
model.ProbCloudyPosterior, model.CPTSprinklerPosterior, model.CPTRainPosterior, model.CPTWetGrassPosterior);
Console.WriteLine("P(rain | grass is wet) = {0:0.0000}", probRainGivenWetGrass1);
Console.WriteLine("P(rain | grass is wet, not cloudy ) = {0:0.0000}", probRainGivenWetGrassNotCloudy1);
Console.WriteLine("");
}
