Best code snippets using no.uib.cipr.matrix.Matrices(Showing top 15 results out of 315)

/**
 * Return the correlation/covariance matrix
 *
 * @return the correlation or covariance matrix
 */
public double[][] getCorrelationMatrix() {
 return Matrices.getArray(m_correlation);
}

/**
 * Creates a random vector. Numbers are drawn from a uniform distribution
 * between 0 and 1
 * 
 * @param size
 *            Size of the vector
 */
public static Vector random(int size) {
  return random(new DenseVector(size));
}

public Matrix mult(Matrix B, DenseMatrix C) {
  if (pivots == null)
    return super.mult(B, C);
  checkMultAdd(B, C);
  C.set(B);
  LAPACK.getInstance().dlaswp(C.numColumns(), C.getData(),
      Matrices.ld(C.numRows()), 1, pivots.length, pivots,
      transposed ? -1 : 1);
  return C;
}

public Model(int nvisible, int nhidden) {
  w1 = (DenseMatrix) Matrices.random(nhidden, nhidden);
  w2 = (DenseMatrix) Matrices.random(nvisible, nhidden);
  b1 = (DenseVector) Matrices.random(nhidden);
  b2 = (DenseVector) Matrices.random(nvisible);
}

public double getDouble(long... coordinates) {
  if (inv == null) {
    DenseMatrix A = new MTJDenseDoubleMatrix2D(getSource()).getWrappedObject();
    DenseMatrix I = Matrices.identity((int) getSource().getColumnCount());
    DenseMatrix AI = I.copy();
    inv = new MTJDenseDoubleMatrix2D((DenseMatrix) A.solve(I, AI));
  }
  return inv.getAsDouble(coordinates);
}

/**
 * Creates a random matrix. Numbers are drawn from a uniform distribution
 * between 0 and 1
 * 
 * @param numRows
 *            Number of rows
 * @param numColumns
 *            Number of columns
 */
public static Matrix random(int numRows, int numColumns) {
  return random(new DenseMatrix(numRows, numColumns));
}

@Override
public Matrix copy() {
  return Matrices.synchronizedMatrixByColumns(A.copy());
}

@Override
public Vector copy() {
  return Matrices.synchronizedVector(x.copy());
}

@Override
public Matrix copy() {
  return Matrices.synchronizedMatrixByRows(A.copy());
}

public Matrix transAmult(Matrix B, DenseMatrix C) {
  if (pivots == null)
    return super.transAmult(B, C);
  checkTransAmultAdd(B, C);
  C.set(B);
  LAPACK.getInstance().dlaswp(C.numColumns(), C.getData(),
      Matrices.ld(C.numRows()), 1, pivots.length, pivots,
      transposed ? 1 : -1);
  return C;
}

public Matrix mult(Matrix B, DenseMatrix C) {
  if (pivots == null)
    return super.mult(B, C);
  checkMultAdd(B, C);
  C.set(B);
  LAPACK.getInstance().dlaswp(C.numColumns(), C.getData(),
      Matrices.ld(C.numRows()), 1, pivots.length, pivots,
      transposed ? -1 : 1);
  return C;
}

/**
 * Creates a random vector. Numbers are drawn from a uniform distribution
 * between 0 and 1
 * 
 * @param size
 *            Size of the vector
 */
public static Vector random(int size) {
  return random(new DenseVector(size));
}

public Matrix transAmult(Matrix B, DenseMatrix C) {
  if (pivots == null)
    return super.transAmult(B, C);
  checkTransAmultAdd(B, C);
  C.set(B);
  LAPACK.getInstance().dlaswp(C.numColumns(), C.getData(),
      Matrices.ld(C.numRows()), 1, pivots.length, pivots,
      transposed ? 1 : -1);
  return C;
}

/**
 * Creates a random matrix. Numbers are drawn from a uniform distribution
 * between 0 and 1
 * 
 * @param numRows
 *            Number of rows
 * @param numColumns
 *            Number of columns
 */
public static Matrix random(int numRows, int numColumns) {
  return random(new DenseMatrix(numRows, numColumns));
}

/**
 * Return the correlation/covariance matrix
 *
 * @return the correlation or covariance matrix
 */
public double[][] getCorrelationMatrix() {
 return Matrices.getArray(m_correlation);
}