public Model(SharedVariableArray<Vector> w, Range c, int numChunks)
{
// Items.
numItems = Variable.New<int>().Named("numItems");
i = new Range(numItems).Named("i");
i.AddAttribute(new Sequential());
// The model identifier for the shared variables.
model = new MicrosoftResearch.Infer.Models.Model(numChunks).Named("model");
// The weight vector for each submodel.
wModel = w.GetCopyFor(model).Named("wModel");
noisePrecision = Variable.New<double>().Named("noisePrecision");
// Arrays of <see cref="Vector"/>-valued items (feature vectors) and integer labels.
x = Variable.Array<Vector>(i).Named("x");
y = Variable.Array<int>(i).Named("y");
// For all items...
using (Variable.ForEach(i))
{
// ...compute the score of this item across all classes...
score = BPMUtils.ComputeClassScores(wModel, x[i], noisePrecision);
y[i] = Variable.DiscreteUniform(c);
// ... and constrain the output.
BPMUtils.ConstrainMaximum(y[i], score);
}
// Inference engine settings (EP).
engine.Compiler.UseSerialSchedules = true;
engine.ShowProgress = false;
}
public Model(ISharedVariableArray<VariableArray<double>, double[][]> w, Range c, int numChunks)
{
// Items.
numItems = Variable.New<int>().Named("numItems");
i = new Range(numItems).Named("i");
i.AddAttribute(new Sequential());
// Features per item.
numFeaturesPerItem = Variable.Array<int>(i).Named("numFeaturesPerItem");
fItem = new Range(numFeaturesPerItem[i]).Named("fItem");
// The model identifier for the shared variables.
model = new MicrosoftResearch.Infer.Models.Model(numChunks).Named("model");
// The weight vector for each submodel.
wModel = w.GetCopyFor(model).Named("wModel");
noisePrecision = Variable.New<double>().Named("noisePrecision");
// Jagged array of feature values - each item is an array of data values
// whose indices are given by the corresponding indices[i].
values = Variable.Array(Variable.Array<double>(fItem), i).Named("values");
// Jagged array of indices for the items.
indices = Variable.Array(Variable.Array<int>(fItem), i).Named("indices");
// Labels.
y = Variable.Array<int>(i).Named("y");
// For all items...
using (Variable.ForEach(i))
{
// ...compute the score of this item across all classes...
score = BPMUtils.ComputeClassScores(wModel, values[i], indices[i], fItem, noisePrecision);
y[i] = Variable.DiscreteUniform(c);
// ... and constrain the output.
BPMUtils.ConstrainMaximum(y[i], score);
}
// Inference engine settings (EP).
engine.Compiler.UseSerialSchedules = true;
engine.ShowProgress = false;
}
