@Override public Matrix init(int rows, int cols) { final Matrix ret = smf.createMatrix(rows, cols); for (int i = 0; i < rows; i++) { for (int j = 0; j < cols; j++) { if (this.random.nextDouble() > sparcity) ret.setElement(i, j, 1d); } } return ret; }
@Override public Matrix init(int rows, int cols) { return MatrixFactory.getSparseDefault().createMatrix(rows, cols); }
@Override public Matrix init(int rows, int cols) { final Matrix ret = smf.createMatrix(rows, cols); final Vector oneRow = CFMatrixUtils.plusInplace(smf.createMatrix(1, cols), 1).getRow(0); for (int i = 0; i < rows; i++) { if (this.random.nextDouble() > sparcity) { ret.setRow(i, oneRow.clone()); } } return ret; }
@Override public Matrix init(int rows, int cols) { final SparseMatrix rand = (SparseMatrix) smf.createUniformRandom(rows, cols, min, max, random); final Matrix ret = smf.createMatrix(rows, cols); for (int i = 0; i < rows; i++) { if (this.random.nextDouble() > sparcity) { ret.setRow(i, rand.getRow(i)); } } return ret; } }
@Override public Matrix init(int rows, int cols) { final SparseMatrix rand = (SparseMatrix) smf.createUniformRandom(rows, cols, min, max, random); final Matrix ret = smf.createMatrix(rows, cols); for (int i = 0; i < rows; i++) { for (int j = 0; j < cols; j++) { if (this.random.nextDouble() > sparcity) ret.setElement(i, j, rand.getElement(i, j)); } } return ret; }
private Matrix prepareMatrix(double[] y) { final Matrix Y = DMF.createMatrix(1, y.length + 1); Y.setElement(0, 0, 1); Y.setSubMatrix(0, 1, DMF.copyArray(new double[][] { y })); return Y.transpose(); }
private Matrix prepareMatrix(double[] y) { final Matrix Y = DMF.createMatrix(1, y.length + 1); Y.setElement(0, 0, 1); Y.setSubMatrix(0, 1, DMF.copyArray(new double[][] { y })); return Y.transpose(); }
private Matrix repmat(Matrix dL1, int nRows, int nCols) { final Matrix out = DMF.createMatrix(nRows * dL1.getNumRows(), nCols * dL1.getNumColumns()); for (int i = 0; i < nRows; i++) { for (int j = 0; j < nCols; j++) { out.setSubMatrix(i * dL1.getNumRows(), j * dL1.getNumColumns(), dL1); } } return out; }
private Matrix repmat(Matrix dL1, int nRows, int nCols) { final Matrix out = DMF.createMatrix(nRows * dL1.getNumRows(), nCols * dL1.getNumColumns()); for (int i = 0; i < nRows; i++) { for (int j = 0; j < nCols; j++) { out.setSubMatrix(i * dL1.getNumRows(), j * dL1.getNumColumns(), dL1); } } return out; }
private Matrix prepareMatrix(Vector y) { final Matrix Y = DMF.createMatrix(1, y.getDimensionality() + 1); Y.setElement(0, 0, 1); Y.setSubMatrix(0, 1, DMF.copyRowVectors(y)); return Y.transpose(); }
private Matrix prepareMatrix(Vector y) { final Matrix Y = DMF.createMatrix(1, y.getDimensionality() + 1); Y.setElement(0, 0, 1); Y.setSubMatrix(0, 1, DMF.copyRowVectors(y)); return Y.transpose(); }
public Matrix getValue() { final int dim = this.conditionalDistribution.getInputDimensionality(); Matrix parameter = MatrixFactory.getDefault().createMatrix(dim, dim+1); parameter.setColumn(0, this.conditionalDistribution.getMean() ); parameter.setSubMatrix(0,1, this.conditionalDistribution.getCovariance() ); return parameter; }
public Matrix getMean() { Matrix C = this.inverseWishart.getMean(); Vector mean = this.gaussian.getMean(); final int d = this.getInputDimensionality(); Matrix R = MatrixFactory.getDefault().createMatrix(d, d+1); R.setColumn(0, mean); R.setSubMatrix(0, 1, C); return R; }
public Matrix getValue() { final int dim = this.conditionalDistribution.getInputDimensionality(); Matrix parameter = MatrixFactory.getDefault().createMatrix(dim, dim+1); parameter.setColumn(0, this.conditionalDistribution.getMean() ); parameter.setSubMatrix(0,1, this.conditionalDistribution.getCovariance() ); return parameter; }
public Matrix getMean() { Matrix C = this.inverseWishart.getMean(); Vector mean = this.gaussian.getMean(); final int d = this.getInputDimensionality(); Matrix R = MatrixFactory.getDefault().createMatrix(d, d+1); R.setColumn(0, mean); R.setSubMatrix(0, 1, C); return R; }
public Matrix getMean() { Matrix C = this.inverseWishart.getMean(); Vector mean = this.gaussian.getMean(); final int d = this.getInputDimensionality(); Matrix R = MatrixFactory.getDefault().createMatrix(d, d+1); R.setColumn(0, mean); R.setSubMatrix(0, 1, C); return R; }
public Matrix getValue() { final int dim = this.conditionalDistribution.getInputDimensionality(); Matrix parameter = MatrixFactory.getDefault().createMatrix(dim, dim+1); parameter.setColumn(0, this.conditionalDistribution.getMean() ); parameter.setSubMatrix(0,1, this.conditionalDistribution.getCovariance() ); return parameter; }
public Matrix differentiate( Vector input) { int M = input.getDimensionality(); Matrix dydx = MatrixFactory.getDefault().createMatrix(M, M); for (int i = 0; i < M; i++) { dydx.setElement(i, i, this.getScalarFunction().differentiate(input.getElement(i))); } return dydx; }
public Matrix differentiate( Vector input) { int M = input.getDimensionality(); Matrix dydx = MatrixFactory.getDefault().createMatrix(M, M); for (int i = 0; i < M; i++) { dydx.setElement(i, i, this.getScalarFunction().differentiate(input.getElement(i))); } return dydx; }
public Matrix differentiate( Vector input) { int M = input.getDimensionality(); Matrix dydx = MatrixFactory.getDefault().createMatrix(M, M); for (int i = 0; i < M; i++) { dydx.setElement(i, i, this.getScalarFunction().differentiate(input.getElement(i))); } return dydx; }