/** * Creates a new, empty {@link FactorizationMachine} of the given * input dimensionality (d) and factor count (k). It initializes the * internal d-dimensional weight vector and k-by-d factor matrix based on * these sizes. All values are initialized to 0. * * @param dimensionality * The input dimensionality (d). Cannot be negative. * @param factorCount * The number of factors for pairwise interactions (k). Cannot be * negative. */ public FactorizationMachine( final int dimensionality, final int factorCount) { this(0.0, VectorFactory.getDenseDefault().createVector(dimensionality), MatrixFactory.getDenseDefault().createMatrix( factorCount, dimensionality)); }
/** * Creates a new, empty {@link FactorizationMachine} of the given * input dimensionality (d) and factor count (k). It initializes the * internal d-dimensional weight vector and k-by-d factor matrix based on * these sizes. All values are initialized to 0. * * @param dimensionality * The input dimensionality (d). Cannot be negative. * @param factorCount * The number of factors for pairwise interactions (k). Cannot be * negative. */ public FactorizationMachine( final int dimensionality, final int factorCount) { this(0.0, VectorFactory.getDenseDefault().createVector(dimensionality), MatrixFactory.getDenseDefault().createMatrix( factorCount, dimensionality)); }
/** * Creates a new, empty {@link FactorizationMachine} of the given * input dimensionality (d) and factor count (k). It initializes the * internal d-dimensional weight vector and k-by-d factor matrix based on * these sizes. All values are initialized to 0. * * @param dimensionality * The input dimensionality (d). Cannot be negative. * @param factorCount * The number of factors for pairwise interactions (k). Cannot be * negative. */ public FactorizationMachine( final int dimensionality, final int factorCount) { this(0.0, VectorFactory.getDenseDefault().createVector(dimensionality), MatrixFactory.getDenseDefault().createMatrix( factorCount, dimensionality)); }
factors = MatrixFactory.getDenseDefault().createMatrix( this.factorCount, this.dimensionality); for (int i = 0; i < this.dimensionality; i++)
factors = MatrixFactory.getDenseDefault().createMatrix( this.factorCount, this.dimensionality); for (int i = 0; i < this.dimensionality; i++)
factors = MatrixFactory.getDenseDefault().createMatrix( this.factorCount, this.dimensionality); for (int i = 0; i < this.dimensionality; i++)
Matrix u = learner.getU(); Matrix w = learner.getW(); Matrix bias = MatrixFactory.getDenseDefault().copyMatrix(learner.getBias()); BilinearEvaluator eval = new RootMeanSumLossEvaluator(); eval.setLearner(learner);
final Matrix u = learner.getU(); final Matrix w = learner.getW(); final Matrix bias = MatrixFactory.getDenseDefault().copyMatrix(learner.getBias()); final BilinearEvaluator eval = new RootMeanSumLossEvaluator(); eval.setLearner(learner);
final Matrix bias = MatrixFactory.getDenseDefault().copyMatrix(learner.getBias()); final BilinearEvaluator eval = new RootMeanSumLossEvaluator(); eval.setLearner(learner);
final Matrix components = MatrixFactory.getDenseDefault().createMatrix( realComponentCount, dataSize); for (int i = 0; i < realComponentCount; i++)
final Matrix components = MatrixFactory.getDenseDefault().createMatrix( realComponentCount, dataSize); for (int i = 0; i < realComponentCount; i++)
final Matrix components = MatrixFactory.getDenseDefault().createMatrix( realComponentCount, dataSize); for (int i = 0; i < realComponentCount; i++)
final Matrix K = MatrixFactory.getDenseDefault().createMatrix(size, size); final Vector k = VectorFactory.getDenseDefault().createVector(size);
final Matrix K = MatrixFactory.getDenseDefault().createMatrix(size, size); final Vector k = VectorFactory.getDenseDefault().createVector(size);
final Matrix K = MatrixFactory.getDenseDefault().createMatrix(size, size); final Vector k = VectorFactory.getDenseDefault().createVector(size);
covariance = MatrixFactory.getDenseDefault().createIdentity( dimensionality, dimensionality);
covariance = MatrixFactory.getDenseDefault().createIdentity( dimensionality, dimensionality);
covariance = MatrixFactory.getDenseDefault().createIdentity( dimensionality, dimensionality);