/// <summary>
/// Results for loopy belief propagation section
/// </summary>
public void LoopyExample()
{
string section = Contents.S3Loopiness.NumberedName;
Inputs inputs = LoadInputData("Toy4");
Experiment loopyExperiment = new Experiment
{
// version without plates
Inputs = inputs,
Model = new UnrolledModel
{
Name = "Loopy",
ProbabilityOfGuess = 0.2,
ProbabilityOfNotMistake = 0.9,
ProbabilityOfSkillTrue = 0.5,
ShowFactorGraph = false
}
};
AnnounceAndRun(loopyExperiment, section);
Experiment exactLoopyExperiment = new Experiment
{
// exact results
Inputs = inputs,
Model = new UnrolledModel
{
Name = "LoopyExact",
ProbabilityOfGuess = 0.2,
ProbabilityOfNotMistake = 0.9,
ProbabilityOfSkillTrue = 0.5,
ShowFactorGraph = false,
ExactInference = true
}
};
AnnounceAndRun(exactLoopyExperiment, section);
ExperimentComparison comparison = new ExperimentComparison(
new[]
{
loopyExperiment,
exactLoopyExperiment
});
//learningSkills.Run();
var histories = ((UnrolledModel)loopyExperiment.Model).MessageHistories;
// Show message progress for edges A, B, C, D
var messageHistories = new Dictionary <string, object>
{
{ "Iteration", new[] { 1, 2, 3, 4, 5 } },
{ "A", histories["sql_uses_B[1]"].ToArray() },
{ "B", histories["sql_uses_F[2]"].ToArray() },
{ "C", histories["csharp_uses_B[2]"].ToArray() },
{ "D", histories["csharp_uses_F[1]"].ToArray() }
};
outputter.Out(messageHistories, section, "Loop Messages");
outputter.Out(comparison, section, "Comparison");
}
/// <summary>
/// Real data inference. Try with guess probability fixed and inferred
/// </summary>
/// <param name="inputs">The inputs.</param>
/// <param name="name">The name.</param>
public void RealDataInference()
{
// Define priors (fixed and learnt)
const double ProbGuess = 0.2;
const double ProbNotMistake = 0.9;
const double ProbSkillTrue = 0.5;
Inputs inputs = LoadInputData("InputData");
Console.WriteLine($"\n{Contents.S4MovingToRealData.NumberedName}\n");
outputter.Out(inputs, Contents.S4MovingToRealData.NumberedName, "Inputs");
// Original model (point mass priors)
Experiment originalExperiment = new Experiment
{
Inputs = inputs,
Model = new NoisyAndModel
{
Name = "Original",
ProbabilityOfGuess = ProbGuess,
ProbabilityOfNotMistake = ProbNotMistake,
ProbabilityOfSkillTrue = ProbSkillTrue,
ShowFactorGraph = false,
IsReal = true
}
};
AnnounceAndRun(originalExperiment, Contents.S4MovingToRealData.NumberedName);
Console.WriteLine($"\n{Contents.S5DiagnosingTheProblem.NumberedName}\n");
// Sampled model using ground truth skills
Experiment samleSkillsObservedExperiment = new Experiment
{
Inputs = originalExperiment.Model.SampleFromModel(
inputs, inputs.NumberOfPeople, new object[] { ProbSkillTrue, true }),
Model = new NoisyAndModel
{
Name = "SampleSkillsObserved",
ProbabilityOfGuess = ProbGuess,
ProbabilityOfNotMistake = ProbNotMistake,
ProbabilityOfSkillTrue = ProbSkillTrue,
IsReal = false
}
};
AnnounceAndRun(samleSkillsObservedExperiment, Contents.S5DiagnosingTheProblem.NumberedName);
// Sampled model using sampled skills
Experiment sampleSkillsSampledExperiment = new Experiment
{
Inputs = originalExperiment.Model.SampleFromModel(
inputs, inputs.NumberOfPeople, new object[] { ProbSkillTrue, false }),
Model = new NoisyAndModel
{
Name = "SampleSkillsSampled",
ProbabilityOfGuess = ProbGuess,
ProbabilityOfNotMistake = ProbNotMistake,
ProbabilityOfSkillTrue = ProbSkillTrue,
IsReal = false
}
};
AnnounceAndRun(sampleSkillsSampledExperiment, Contents.S5DiagnosingTheProblem.NumberedName);
Console.WriteLine($"\n{Contents.S6LearningTheGuessProbabilities.NumberedName}\n");
outputter.Out(inputs, Contents.S6LearningTheGuessProbabilities.NumberedName, "Inputs");
Beta guessPrior = BetaFromMeanAndTotalCount(0.25, 10);
outputter.Out(
guessPrior,
Contents.S6LearningTheGuessProbabilities.NumberedName,
"Priors");
// Random model
Experiment randomExperiment = new Experiment
{
Inputs = inputs,
Model = new RandomModel
{
Name = "Random",
ProbabilityOfGuess = 0.5,
ProbabilityOfNotMistake = 0.5,
ProbabilityOfSkillTrue = 0.5,
Index = 8,
ShowFactorGraph = false,
IsReal = false
}
};
AnnounceAndRun(randomExperiment, Contents.S6LearningTheGuessProbabilities.NumberedName);
// Model with Beta prior over guess probabilities
Experiment learnedExperiment = new Experiment
{
Inputs = inputs,
Model = new LearnedNoisyAndModel
{
Name = "Learned",
ProbabilityOfGuess = ProbGuess,
ProbabilityOfNotMistake = ProbNotMistake,
ProbabilityOfSkillTrue = ProbSkillTrue,
GuessPrior = guessPrior,
ShowFactorGraph = false,
IsReal = true
}
};
AnnounceAndRun(learnedExperiment, Contents.S6LearningTheGuessProbabilities.NumberedName);
Experiment perfectExperiment = new Experiment
{
FullyObserved = true,
Inputs = inputs,
Model = new NoisyAndModel {
Name = "Perfect", Index = 9, IsReal = false
}
};
AnnounceAndRun(perfectExperiment, Contents.S6LearningTheGuessProbabilities.NumberedName);
ExperimentComparison comparison = new ExperimentComparison();
comparison.Experiments.Add(randomExperiment);
comparison.Experiments.Add(originalExperiment);
comparison.Experiments.Add(samleSkillsObservedExperiment);
comparison.Experiments.Add(sampleSkillsSampledExperiment);
comparison.Experiments.Add(learnedExperiment);
comparison.Experiments.Add(perfectExperiment);
//Selected guess posteriors
outputter.Out(
learnedExperiment.Results.GuessPosteriors
.Select((ia, i) => new { ia, i })
.Where((ia, i) => i % 5 == 0)
.ToDictionary <dynamic, string, Beta>(x => "Question " + (x.i + 1), x => x.ia),
Contents.S6LearningTheGuessProbabilities.NumberedName,
"SelectedGuessPosteriors");
outputter.Out(comparison, Contents.S6LearningTheGuessProbabilities.NumberedName, "Comparison");
}
