예제 #1
0
			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;
			}
예제 #2
0
			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;
			}