public static BayesianNetwork ToBayesianNetwork(this JObject json)
{
string networkName = json["name"].Value <string>();
var bn = new BayesianNetwork(networkName);
// Load variables and note parents.
var variableList = new List <string>();
var parentsList = new Dictionary <string, string[]>();
foreach (var jrv in json.Property("variables").Value)
{
var rv = ToRandomVariable(jrv);
bn.AddVariable(rv);
variableList.Add(rv.Name);
parentsList.Add(rv.Name, jrv["parents"].Values <string>().ToArray());
}
// Reparent.
foreach (var rv in variableList)
{
string[] parents = parentsList[rv];
foreach (var parentName in parents)
{
bn.ConnectVariables(parentName, rv);
}
}
return(bn);
}
public static BayesianNetwork ToBayesianNetwork(this JObject json)
{
string networkName = json["name"].Value<string>();
var bn = new BayesianNetwork(networkName);
// Load variables and note parents.
var variableList = new List<string>();
var parentsList = new Dictionary<string, string[]>();
foreach (var jrv in json.Property("variables").Value)
{
var rv = ToRandomVariable(jrv);
bn.AddVariable(rv);
variableList.Add(rv.Name);
parentsList.Add(rv.Name, jrv["parents"].Values<string>().ToArray());
}
// Reparent.
foreach (var rv in variableList)
{
string[] parents = parentsList[rv];
foreach (var parentName in parents)
{
bn.ConnectVariables(parentName, rv);
}
}
return bn;
}
protected void DoLearning(LearningTask learningTask)
{
if (learningTask.LearningState == ComputationState.Done)
{
return;
}
// Grab options.
var options = learningTask.Options;
// Grab training set.
var trainingSet = learningTask.TrainingSet;
// Grab variable names.
var firstObservation = learningTask.TrainingSet.First();
var variableNames = firstObservation.VariableNames;
// Build a starter Bayesian network.
var bn = new BayesianNetwork(learningTask.TrainingSet.Name);
// Build variables for each variable in the training set.
foreach (var variableName in variableNames)
{
var space = trainingSet.Variables.TryFind(variableName).Value;
var rv = new RandomVariable(variableName, space);
bn.AddVariable(rv);
}
// Store in task so that task can send updates to its listeners.
learningTask.BayesianNetwork = bn;
learningTask.LearningState = ComputationState.Computing;
// Build Dirichlet parameters
IDictionary<string, DirichletDistribution> priors = new Dictionary<string, DirichletDistribution>();
foreach (var variableName in variableNames)
{
var space = trainingSet.Variables.TryFind(variableName).Value;
DirichletDistribution prior;
if (options.DistributionDirichletAlpha > 0)
{
prior = new DirichletDistribution();
foreach (var value in space.Values)
{
prior.SetParameter(value, options.DistributionDirichletAlpha);
}
priors[variableName] = prior;
}
}
// Initialize a sufficient statistics.
SufficientStatistics sufficientStatistics
= new SufficientStatistics(trainingSet, Utils.Some(priors));
// Learn structure.
switch (options.Structure)
{
case LearningOptions.StructureEnum.DisconnectedStructure:
break;
case LearningOptions.StructureEnum.RandomStructure:
bn.GenerateStructure(
StructureClass.Random,
Utils.Some(options.StructureSeed),
Utils.Some(options.StructureParentLimit));
break;
case LearningOptions.StructureEnum.TreeStructure:
bn.LearnStructure(
sufficientStatistics,
StructureClass.Tree,
Utils.None<int>(),
Utils.None<int>());
break;
case LearningOptions.StructureEnum.GeneralStructure:
bn.LearnStructure(
sufficientStatistics,
StructureClass.General,
Utils.None<int>(),
Utils.Some(options.StructureParentLimit));
break;
}
// Learn distributions.
bn.LearnDistributions(sufficientStatistics);
// Done.
learningTask.LearningState = ComputationState.Done;
}
protected void DoLearning(LearningTask learningTask)
{
if (learningTask.LearningState == ComputationState.Done)
{
return;
}
// Grab options.
var options = learningTask.Options;
// Grab training set.
var trainingSet = learningTask.TrainingSet;
// Grab variable names.
var firstObservation = learningTask.TrainingSet.First();
var variableNames = firstObservation.VariableNames;
// Build a starter Bayesian network.
var bn = new BayesianNetwork(learningTask.TrainingSet.Name);
// Build variables for each variable in the training set.
foreach (var variableName in variableNames)
{
var space = trainingSet.Variables.TryFind(variableName).Value;
var rv = new RandomVariable(variableName, space);
bn.AddVariable(rv);
}
// Store in task so that task can send updates to its listeners.
learningTask.BayesianNetwork = bn;
learningTask.LearningState = ComputationState.Computing;
// Build Dirichlet parameters
IDictionary <string, DirichletDistribution> priors = new Dictionary <string, DirichletDistribution>();
foreach (var variableName in variableNames)
{
var space = trainingSet.Variables.TryFind(variableName).Value;
DirichletDistribution prior;
if (options.DistributionDirichletAlpha > 0)
{
prior = new DirichletDistribution();
foreach (var value in space.Values)
{
prior.SetParameter(value, options.DistributionDirichletAlpha);
}
priors[variableName] = prior;
}
}
// Initialize a sufficient statistics.
SufficientStatistics sufficientStatistics
= new SufficientStatistics(trainingSet, Utils.Some(priors));
// Learn structure.
switch (options.Structure)
{
case LearningOptions.StructureEnum.DisconnectedStructure:
break;
case LearningOptions.StructureEnum.RandomStructure:
bn.GenerateStructure(
StructureClass.Random,
Utils.Some(options.StructureSeed),
Utils.Some(options.StructureParentLimit));
break;
case LearningOptions.StructureEnum.TreeStructure:
bn.LearnStructure(
sufficientStatistics,
StructureClass.Tree,
Utils.None <int>(),
Utils.None <int>());
break;
case LearningOptions.StructureEnum.GeneralStructure:
bn.LearnStructure(
sufficientStatistics,
StructureClass.General,
Utils.None <int>(),
Utils.Some(options.StructureParentLimit));
break;
}
// Learn distributions.
bn.LearnDistributions(sufficientStatistics);
// Done.
learningTask.LearningState = ComputationState.Done;
}
