/// <summary> /// Constructs a multi-component Bayes Point Machine using shared variables for chunking data /// </summary> /// <param name="nClass">Number of components (classes)</param> /// <param name="nFeatures">Number of features</param> /// <param name="noisePrec">Noise precision</param> /// <param name="trainChunkSize">Chunk size for training</param> /// <param name="testChunkSize">Chunk size for testing</param> public BPM_Shared(int nClass, int nFeatures, double noisePrec, int trainChunkSize, int testChunkSize) { this.nClass = nClass; this.nFeatures = nFeatures; this.trainChunkSize = trainChunkSize; this.testChunkSize = testChunkSize; NoisePrec = noisePrec; feature = new Range(nFeatures).Named("feature"); // The set of weight vectors (one for each component) are shared between all data chunks w = new SharedVariable<Vector>[nClass]; VectorGaussian wPrior0 = VectorGaussian.PointMass(Vector.Zero(nFeatures)); VectorGaussian wPrior = VectorGaussian.FromMeanAndPrecision(Vector.Zero(nFeatures), PositiveDefiniteMatrix.Identity(nFeatures)); for (int c = 0; c < nClass; c++) { w[c] = (c == 0) ? SharedVariable<Vector>.Random(VectorGaussian.PointMass(Vector.Zero(nFeatures))).Named("w_" + c) : SharedVariable<Vector>.Random(wPrior).Named("w_" + c); } trainModel = SpecifyTrainModel("_train", trainChunkSize); testModel = SpecifyTestModel("_test", testChunkSize); }
/// <summary> /// Specify the training model /// </summary> /// <param name="s">The name of the test model</param> /// <param name="nChunks">The number of chunks</param> /// <returns>A <see cref="BPMVarsModelForTest"/> instance</returns> private BPMVarsModelForTest SpecifyTestModel(string s, int nChunks) { // The number of test items - this will be set by the calling program Variable<int> nItem = Variable.New<int>().Named("nItem" + s); // A range over the items Range item = new Range(nItem).Named("item" + s); // An array of feature vectors - their observed values will be // set by the calling program VariableArray<Vector> xValues = Variable.Array<Vector>(item); // The model identifier for the shared variables Model model = new Model(nChunks).Named("model" + s); // The weight vector for each submodel Variable<Vector>[] wModel = new Variable<Vector>[nClass]; for (int c = 0; c < nClass; c++) { // Get a copy of the shared weight vector variable for the submodel wModel[c] = w[c].GetCopyFor(model).Named("wModel_" + c + s); } // Loop over data VariableArray<int> ytest = Variable.Array<int>(item).Named("ytest" + s); using (Variable.ForEach(item)) { // The score for this item across all components Variable<double>[] score = BPMUtils.ComputeClassScores(wModel, xValues[item], NoisePrec); // The constraints on the output variable ytest[item] = Variable.DiscreteUniform(nClass); BPMUtils.ConstrainMaximum(ytest[item], score, nClass); } // Store the variables BPMVarsModelForTest bpmVar = new BPMVarsModelForTest(); bpmVar.ie = new InferenceEngine(); bpmVar.xValues = xValues; bpmVar.y = ytest; bpmVar.nItems = nItem; bpmVar.model = model; return bpmVar; }