/// <summary>
/// Creates an Infer.NET inference algorithm for making predictions from a binary Bayes point machine classifier
/// with <see cref="Gaussian"/> prior distributions over factorized weights and features in a dense representation.
/// </summary>
/// <param name="generatedSourceFolder">The folder to drop the generated prediction algorithm to.</param>
public static void CreateDenseBinaryPredictionAlgorithm(string generatedSourceFolder)
{
// Create the model
var model = new DenseBinaryModel(computeModelEvidence: false, useCompoundWeightPriorDistributions: false);
// Add the inferred variables
model.Labels.AddAttribute(QueryTypes.Marginal);
// Apply the query to the model and compile the inference algorithm
GetCompiledInferenceAlgorithm("GaussianDenseBinaryBpmPrediction", generatedSourceFolder, model.Labels);
}
/// <summary>
/// Creates an Infer.NET inference algorithm which trains a binary Bayes point machine classifier
/// with factorized weight distributions on features in a dense representation.
/// </summary>
/// <param name="generatedSourceFolder">The folder to drop the generated training algorithm to.</param>
/// <param name="computeModelEvidence">If true, the generated training algorithm computes evidence.</param>
/// <param name="useCompoundWeightPriorDistributions">
/// If true, the generated training algorithm uses compound prior distributions over weights. Otherwise
/// <see cref="Gaussian"/> prior distributions are used.
/// </param>
public static void CreateDenseBinaryTrainingAlgorithm(
string generatedSourceFolder,
bool computeModelEvidence,
bool useCompoundWeightPriorDistributions)
{
// Create the model
var model = new DenseBinaryModel(computeModelEvidence, useCompoundWeightPriorDistributions);
// Add the observed variables
model.Labels.ObservedValue = default(bool[]);
// Add the inferred variables
var queryVariables = new List <IVariable> {
model.Weights
};
model.Weights.AddAttribute(QueryTypes.Marginal);
model.Weights.AddAttribute(QueryTypes.MarginalDividedByPrior);
if (useCompoundWeightPriorDistributions)
{
queryVariables.Add(model.WeightPrecisionRates);
model.WeightPrecisionRates.AddAttribute(QueryTypes.MarginalDividedByPrior);
if (computeModelEvidence)
{
// This is required to compute evidence corrections
model.WeightPrecisionRates.AddAttribute(QueryTypes.Marginal);
}
}
if (computeModelEvidence)
{
queryVariables.Add(model.ModelSelector);
model.ModelSelector.AddAttribute(QueryTypes.Marginal);
}
// Apply the query to the model and compile the inference algorithm
string queryName =
(useCompoundWeightPriorDistributions ? "Compound" : "Gaussian")
+ "DenseBinaryBpmTraining"
+ (computeModelEvidence ? "Evidence" : string.Empty);
GetCompiledInferenceAlgorithm(queryName, generatedSourceFolder, queryVariables.ToArray());
}