public override GraphicalModel Generate(SourceOfRandomness sourceOfRandomness, IGenerationStatus generationStatus)
{
GraphicalModel model = new GraphicalModel();
// Create the variables and factors
int[] variableSizes = new int[20];
for (int i = 0; i < 20; i++)
{
variableSizes[i] = sourceOfRandomness.NextInt(1, 5);
}
int numFactors = sourceOfRandomness.NextInt(12);
for (int i_1 = 0; i_1 < numFactors; i_1++)
{
int[] neighbors = new int[sourceOfRandomness.NextInt(1, 3)];
int[] neighborSizes = new int[neighbors.Length];
for (int j = 0; j < neighbors.Length; j++)
{
neighbors[j] = sourceOfRandomness.NextInt(20);
neighborSizes[j] = variableSizes[neighbors[j]];
}
ConcatVectorTable table = new ConcatVectorTable(neighborSizes);
foreach (int[] assignment in table)
{
int numComponents = sourceOfRandomness.NextInt(7);
// Generate a vector
ConcatVector v = new ConcatVector(numComponents);
for (int x = 0; x < numComponents; x++)
{
if (sourceOfRandomness.NextBoolean())
{
v.SetSparseComponent(x, sourceOfRandomness.NextInt(32), sourceOfRandomness.NextDouble());
}
else
{
double[] val = new double[sourceOfRandomness.NextInt(12)];
for (int y = 0; y < val.Length; y++)
{
val[y] = sourceOfRandomness.NextDouble();
}
v.SetDenseComponent(x, val);
}
}
// set vec in table
table.SetAssignmentValue(assignment, null);
}
model.AddFactor(table, neighbors);
}
// Add metadata to the variables, factors, and model
GenerateMetaData(sourceOfRandomness, model.GetModelMetaDataByReference());
for (int i_2 = 0; i_2 < 20; i_2++)
{
GenerateMetaData(sourceOfRandomness, model.GetVariableMetaDataByReference(i_2));
}
foreach (GraphicalModel.Factor factor in model.factors)
{
GenerateMetaData(sourceOfRandomness, factor.GetMetaDataByReference());
}
return(model);
}
/// <summary>
/// This constructs a fresh vector that is sized correctly to accommodate all the known sparse values for vectors
/// that are possibly sparse.
/// </summary>
/// <returns>
/// a new, internally correctly sized ConcatVector that will work correctly as weights for features from
/// this namespace;
/// </returns>
public virtual ConcatVector NewWeightsVector()
{
ConcatVector vector = new ConcatVector(featureToIndex.Count);
foreach (string s in sparseFeatureIndex.Keys)
{
int size = sparseFeatureIndex[s].Count;
vector.SetDenseComponent(EnsureFeature(s), new double[size]);
}
return(vector);
}
public virtual void TestAddSparseToDense(double[] dense1, int sparseIndex, double v)
{
ConcatVector v1 = new ConcatVector(1);
v1.SetDenseComponent(0, dense1);
ConcatVector v2 = new ConcatVector(1);
v2.SetSparseComponent(0, (int)sparseIndex, v);
double expected = v1.DotProduct(v2) + 0.7f * (v2.DotProduct(v2));
v1.AddVectorInPlace(v2, 0.7f);
NUnit.Framework.Assert.AreEqual(v1.DotProduct(v2), 5.0e-4, expected);
}
public override ConcatVectorTest.DenseTestVector Generate(SourceOfRandomness sourceOfRandomness, IGenerationStatus generationStatus)
{
int length = sourceOfRandomness.NextInt(10);
double[][] trueValues = new double[length][];
bool[] sparse = new bool[length];
int[] sizes = new int[length];
// Generate sizes in advance, so we can pass the clues on to the constructor for the multivector
for (int i = 0; i < length; i++)
{
bool isSparse = sourceOfRandomness.NextBoolean();
sparse[i] = isSparse;
if (isSparse)
{
sizes[i] = -1;
}
else
{
int componentLength = sourceOfRandomness.NextInt(SparseVectorLength);
sizes[i] = componentLength;
}
}
ConcatVector mv = new ConcatVector(length);
for (int i_1 = 0; i_1 < length; i_1++)
{
if (sparse[i_1])
{
trueValues[i_1] = new double[SparseVectorLength];
int sparseIndex = sourceOfRandomness.NextInt(SparseVectorLength);
double sparseValue = sourceOfRandomness.NextDouble();
trueValues[i_1][sparseIndex] = sparseValue;
mv.SetSparseComponent(i_1, sparseIndex, sparseValue);
}
else
{
trueValues[i_1] = new double[sizes[i_1]];
// Ensure we have some null components in our generated vector
if (sizes[i_1] > 0)
{
for (int j = 0; j < sizes[i_1]; j++)
{
trueValues[i_1][j] = sourceOfRandomness.NextDouble();
}
mv.SetDenseComponent(i_1, trueValues[i_1]);
}
}
}
return(new ConcatVectorTest.DenseTestVector(trueValues, mv));
}
public virtual void TestAppendDenseComponent(double[] vector1, double[] vector2)
{
ConcatVector v1 = new ConcatVector(1);
ConcatVector v2 = new ConcatVector(1);
v1.SetDenseComponent(0, vector1);
v2.SetDenseComponent(0, vector2);
double sum = 0.0f;
for (int i = 0; i < Math.Min(vector1.Length, vector2.Length); i++)
{
sum += vector1[i] * vector2[i];
}
NUnit.Framework.Assert.AreEqual(v1.DotProduct(v2), 5.0e-4, sum);
}
public virtual void TestElementwiseSparseToDense(double[] dense1, int sparseIndex, double v)
{
ConcatVector v1 = new ConcatVector(1);
v1.SetDenseComponent(0, dense1);
ConcatVector v2 = new ConcatVector(1);
v2.SetSparseComponent(0, (int)sparseIndex, v);
v1.ElementwiseProductInPlace(v2);
for (int i = 0; i < dense1.Length; i++)
{
double expected = 0.0f;
if (i == sparseIndex)
{
expected = dense1[i] * v;
}
NUnit.Framework.Assert.AreEqual(v1.GetValueAt(0, i), 5.0e-4, expected);
}
}
public override ConcatVector[][][] Generate(SourceOfRandomness sourceOfRandomness, IGenerationStatus generationStatus)
{
int l = sourceOfRandomness.NextInt(10) + 1;
int m = sourceOfRandomness.NextInt(10) + 1;
int n = sourceOfRandomness.NextInt(10) + 1;
ConcatVector[][][] factor3 = new ConcatVector[l][][];
for (int i = 0; i < factor3.Length; i++)
{
for (int j = 0; j < factor3[0].Length; j++)
{
for (int k = 0; k < factor3[0][0].Length; k++)
{
int numComponents = sourceOfRandomness.NextInt(7);
ConcatVector v = new ConcatVector(numComponents);
for (int x = 0; x < numComponents; x++)
{
if (sourceOfRandomness.NextBoolean())
{
v.SetSparseComponent(x, sourceOfRandomness.NextInt(32), sourceOfRandomness.NextDouble());
}
else
{
double[] val = new double[sourceOfRandomness.NextInt(12)];
for (int y = 0; y < val.Length; y++)
{
val[y] = sourceOfRandomness.NextDouble();
}
v.SetDenseComponent(x, val);
}
}
factor3[i][j][k] = v;
}
}
}
return(factor3);
}
/// <summary>
/// This adds a dense feature to a vector, setting the appropriate component of the given vector to the passed in
/// value.
/// </summary>
/// <param name="vector">the vector</param>
/// <param name="featureName">the feature whose value to set</param>
/// <param name="value">the value we want to set this vector to</param>
public virtual void SetDenseFeature(ConcatVector vector, string featureName, double[] value)
{
vector.SetDenseComponent(EnsureFeature(featureName), value);
}