/** * Creates a Matrix with ones (1) on the diagonal, and zeros (0) elsewhere * @param numRowsAndColumns number of rows and columns in the Matrix * @return Identity Matrix with ones on the diagonal and zeros elsewhere */ public MatrixType createIdentity( int numRowsAndColumns ) { return this.createIdentity( numRowsAndColumns, numRowsAndColumns ); }
/** * Creates a Matrix with ones (1) on the diagonal, and zeros (0) elsewhere * @param numRowsAndColumns number of rows and columns in the Matrix * @return Identity Matrix with ones on the diagonal and zeros elsewhere */ public MatrixType createIdentity( int numRowsAndColumns ) { return this.createIdentity( numRowsAndColumns, numRowsAndColumns ); }
/** * Creates a new instance of InverseWishartDistribution * @param dimensionality * Dimensionality of the inverse-Wishart distribution, which sets * the degrees of freedom to two plus the dimensionality. */ public InverseWishartDistribution( final int dimensionality ) { this( MatrixFactory.getDefault().createIdentity(dimensionality, dimensionality ), dimensionality + 2 ); }
/** * Creates a new instance of MultivariateGaussianMeanBayesianEstimator * @param dimensionality * Dimensionality of the Vectors */ public MultivariateGaussianMeanBayesianEstimator( int dimensionality ) { this( MatrixFactory.getDefault().createIdentity(dimensionality,dimensionality) ); }
/** * Creates a new instance of MultivariateGaussianMeanBayesianEstimator * @param dimensionality * Dimensionality of the Vectors */ public MultivariateGaussianMeanBayesianEstimator( int dimensionality ) { this( MatrixFactory.getDefault().createIdentity(dimensionality,dimensionality) ); }
/** * Creates a new instance of InverseWishartDistribution * @param dimensionality * Dimensionality of the inverse-Wishart distribution, which sets * the degrees of freedom to two plus the dimensionality. */ public InverseWishartDistribution( final int dimensionality ) { this( MatrixFactory.getDefault().createIdentity(dimensionality, dimensionality ), dimensionality + 2 ); }
/** * Creates a new instance of MultivariateGaussianMeanBayesianEstimator * @param dimensionality * Dimensionality of the Vectors */ public MultivariateGaussianMeanBayesianEstimator( int dimensionality ) { this( MatrixFactory.getDefault().createIdentity(dimensionality,dimensionality) ); }
/** * Creates a new instance of InverseWishartDistribution * @param dimensionality * Dimensionality of the inverse-Wishart distribution, which sets * the degrees of freedom to two plus the dimensionality. */ public InverseWishartDistribution( final int dimensionality ) { this( MatrixFactory.getDefault().createIdentity(dimensionality, dimensionality ), dimensionality + 2 ); }
/** * Creates a new instance of MultivariateGaussian. * * @param dimensionality Dimensionality of the Gaussian to create. */ public MultivariateGaussian( int dimensionality) { this(VectorFactory.getDefault().createVector(dimensionality), MatrixFactory.getDefault().createIdentity(dimensionality, dimensionality)); }
/** * Creates a distribution with the given dimensionality. * @param dimensionality * Dimensionality of the distribution. */ public MultivariateStudentTDistribution( int dimensionality ) { this( DEFAULT_DEGREES_OF_FREEDOM, VectorFactory.getDefault().createVector(dimensionality), MatrixFactory.getDefault().createIdentity(dimensionality,dimensionality) ); }
/** * Creates a new instance of MultivariateGaussian. * * @param dimensionality Dimensionality of the Gaussian to create. */ public MultivariateGaussian( int dimensionality) { this(VectorFactory.getDefault().createVector(dimensionality), MatrixFactory.getDefault().createIdentity(dimensionality, dimensionality)); }
/** * Creates a distribution with the given dimensionality. * @param dimensionality * Dimensionality of the distribution. */ public MultivariateStudentTDistribution( int dimensionality ) { this( DEFAULT_DEGREES_OF_FREEDOM, VectorFactory.getDefault().createVector(dimensionality), MatrixFactory.getDefault().createIdentity(dimensionality,dimensionality) ); }
/** * Creates a distribution with the given dimensionality. * @param dimensionality * Dimensionality of the distribution. */ public MultivariateStudentTDistribution( int dimensionality ) { this( DEFAULT_DEGREES_OF_FREEDOM, VectorFactory.getDefault().createVector(dimensionality), MatrixFactory.getDefault().createIdentity(dimensionality,dimensionality) ); }
/** * Creates a new instance of MultivariateGaussian. * * @param dimensionality Dimensionality of the Gaussian to create. */ public MultivariateGaussian( int dimensionality) { this(VectorFactory.getDefault().createVector(dimensionality), MatrixFactory.getDefault().createIdentity(dimensionality, dimensionality)); }
public Matrix differentiate( Vector input) { int M = input.getDimensionality(); Matrix dydx = MatrixFactory.getDefault().createIdentity(M,M).scale( this.getScaleFactor() ); return dydx; }
public Matrix differentiate( Vector input) { int M = input.getDimensionality(); Matrix dydx = MatrixFactory.getDefault().createIdentity(M,M).scale( this.getScaleFactor() ); return dydx; }
public Matrix differentiate( Vector input) { int M = input.getDimensionality(); Matrix dydx = MatrixFactory.getDefault().createIdentity(M,M).scale( this.getScaleFactor() ); return dydx; }
/** * Creates a new instance of MultivariateGaussianMeanBayesianEstimator * @param knownCovarianceInverse * Known covariance matrix of the estimated mean. */ public MultivariateGaussianMeanBayesianEstimator( Matrix knownCovarianceInverse ) { this( knownCovarianceInverse, new MultivariateGaussian( VectorFactory.getDefault().createVector(knownCovarianceInverse.getNumRows()), MatrixFactory.getDefault().createIdentity(knownCovarianceInverse.getNumRows(), knownCovarianceInverse.getNumColumns()) ) ); }
/** * Creates a new instance of MultivariateGaussianMeanBayesianEstimator * @param knownCovarianceInverse * Known covariance matrix of the estimated mean. */ public MultivariateGaussianMeanBayesianEstimator( Matrix knownCovarianceInverse ) { this( knownCovarianceInverse, new MultivariateGaussian( VectorFactory.getDefault().createVector(knownCovarianceInverse.getNumRows()), MatrixFactory.getDefault().createIdentity(knownCovarianceInverse.getNumRows(), knownCovarianceInverse.getNumColumns()) ) ); }
/** * Creates a new instance of MultivariateGaussianMeanBayesianEstimator * @param knownCovarianceInverse * Known covariance matrix of the estimated mean. */ public MultivariateGaussianMeanBayesianEstimator( Matrix knownCovarianceInverse ) { this( knownCovarianceInverse, new MultivariateGaussian( VectorFactory.getDefault().createVector(knownCovarianceInverse.getNumRows()), MatrixFactory.getDefault().createIdentity(knownCovarianceInverse.getNumRows(), knownCovarianceInverse.getNumColumns()) ) ); }