public override TableFactorTest.PartiallyObservedConstructorData Generate(SourceOfRandomness sourceOfRandomness, IGenerationStatus generationStatus)
{
int len = sourceOfRandomness.NextInt(1, 4);
ICollection <int> taken = new HashSet <int>();
int[] neighborIndices = new int[len];
int[] dimensions = new int[len];
int[] observations = new int[len];
int numObserved = 0;
for (int i = 0; i < len; i++)
{
int j = sourceOfRandomness.NextInt(8);
while (taken.Contains(j))
{
j = sourceOfRandomness.NextInt(8);
}
taken.Add(j);
neighborIndices[i] = j;
dimensions[i] = sourceOfRandomness.NextInt(1, 3);
if (sourceOfRandomness.NextBoolean() && numObserved + 1 < dimensions.Length)
{
observations[i] = sourceOfRandomness.NextInt(dimensions[i]);
numObserved++;
}
else
{
observations[i] = -1;
}
}
ConcatVectorTable t = new ConcatVectorTable(dimensions);
TableFactorTest.ConcatVectorGenerator gen = new TableFactorTest.ConcatVectorGenerator(typeof(ConcatVector));
foreach (int[] assn in t)
{
ConcatVector vec = gen.Generate(sourceOfRandomness, generationStatus);
t.SetAssignmentValue(assn, null);
}
TableFactorTest.PartiallyObservedConstructorData data = new TableFactorTest.PartiallyObservedConstructorData();
data.factor = new GraphicalModel.Factor(t, neighborIndices);
data.observations = observations;
return(data);
}
private void GenerateCliques(int[] variableSizes, IList <int> startSet, ICollection <int> alreadyRepresented, GraphicalModel model, SourceOfRandomness randomness)
{
if (alreadyRepresented.Count == variableSizes.Length)
{
return;
}
// Generate the clique variable set
IList <int> cliqueContents = new List <int>();
Sharpen.Collections.AddAll(cliqueContents, startSet);
Sharpen.Collections.AddAll(alreadyRepresented, startSet);
while (true)
{
if (alreadyRepresented.Count == variableSizes.Length)
{
break;
}
if (cliqueContents.Count == 0 || randomness.NextDouble(0, 1) < 0.7)
{
int gen;
do
{
gen = randomness.NextInt(variableSizes.Length);
}while (alreadyRepresented.Contains(gen));
alreadyRepresented.Add(gen);
cliqueContents.Add(gen);
}
else
{
break;
}
}
// Create the actual table
int[] neighbors = new int[cliqueContents.Count];
int[] neighborSizes = new int[neighbors.Length];
for (int j = 0; j < neighbors.Length; j++)
{
neighbors[j] = cliqueContents[j];
neighborSizes[j] = variableSizes[neighbors[j]];
}
ConcatVectorTable table = new ConcatVectorTable(neighborSizes);
foreach (int[] assignment in table)
{
// Generate a vector
ConcatVector v = new ConcatVector(ConcatVecComponents);
for (int x = 0; x < ConcatVecComponents; x++)
{
if (randomness.NextBoolean())
{
v.SetSparseComponent(x, randomness.NextInt(32), randomness.NextDouble());
}
else
{
double[] val = new double[randomness.NextInt(12)];
for (int y = 0; y < val.Length; y++)
{
val[y] = randomness.NextDouble();
}
v.SetDenseComponent(x, val);
}
}
// set vec in table
table.SetAssignmentValue(assignment, null);
}
model.AddFactor(table, neighbors);
// Pick the number of children
IList <int> availableVariables = new List <int>();
Sharpen.Collections.AddAll(availableVariables, cliqueContents);
availableVariables.RemoveAll(startSet);
int numChildren = randomness.NextInt(0, availableVariables.Count);
if (numChildren == 0)
{
return;
}
IList <IList <int> > children = new List <IList <int> >();
for (int i = 0; i < numChildren; i++)
{
children.Add(new List <int>());
}
// divide up the shared variables across the children
int cursor = 0;
while (true)
{
if (availableVariables.Count == 0)
{
break;
}
if (children[cursor].Count == 0 || randomness.NextBoolean())
{
int gen = randomness.NextInt(availableVariables.Count);
children[cursor].Add(availableVariables[gen]);
availableVariables.Remove(availableVariables[gen]);
}
else
{
break;
}
cursor = (cursor + 1) % numChildren;
}
foreach (IList <int> shared1 in children)
{
foreach (int i_1 in shared1)
{
foreach (IList <int> shared2 in children)
{
System.Diagnostics.Debug.Assert((shared1 == shared2 || !shared2.Contains(i_1)));
}
}
}
foreach (IList <int> shared in children)
{
if (shared.Count > 0)
{
GenerateCliques(variableSizes, shared, alreadyRepresented, model, randomness);
}
}
}
