def svd(matrix, k=50):
"""
Calculate the truncated singular value decomposition
:math:`A = U * Sigma * V^T` using SVDLIBC.
Returns a triple of:
- U as a dense labeled matrix
- S, a dense vector representing the diagonal of Sigma
- V as a dense labeled matrix
This matrix must not contain any empty rows or columns. If it does,
use the .squish() method first.
"""
assert matrix.ndim == 2
if isinstance(matrix, DenseMatrix):
# FIXME: we can probably feed this into SVDLIBC directly.
matrix = matrix.to_sparse()
if matrix.shape[1] >= matrix.shape[0] * 1.2:
# transpose the matrix for speed
V, S, U = matrix.T.svd(k)
return U, S, V
Ut, S, Vt = svd_llmat(matrix.llmatrix, k)
U = DenseMatrix(Ut.T, matrix.row_labels, None)
V = DenseMatrix(Vt.T, matrix.col_labels, None)
return (U, S, V)
def svd(matrix, k=50):
"""
Calculate the truncated singular value decomposition
:math:`A = U * Sigma * V^T` using SVDLIBC.
Returns a triple of:
- U as a dense labeled matrix
- S, a dense vector representing the diagonal of Sigma
- V as a dense labeled matrix
This matrix must not contain any empty rows or columns. If it does,
use the .squish() method first.
"""
assert matrix.ndim == 2
if isinstance(matrix, DenseMatrix):
Ut, S, Vt = svd_ndarray(matrix, k)
elif isinstance(matrix, SparseMatrix):
if matrix.nnz == 0:
# don't let svdlib touch a matrix of all zeros. It explodes and
# corrupts its state. Just return a zero result instead.
U = DenseMatrix((matrix.shape[0], k))
S = np.zeros((k,))
V = DenseMatrix((matrix.shape[1], k))
return U, S, V
if matrix.shape[1] >= matrix.shape[0] * 1.2:
# transpose the matrix for speed
V, S, U = matrix.T.svd(k)
return U, S, V
Ut, S, Vt = svd_llmat(matrix.llmatrix, k)
else:
raise TypeError("Don't know how to SVD a %r", type(matrix))
U = DenseMatrix(Ut.T, matrix.row_labels, None)
V = DenseMatrix(Vt.T, matrix.col_labels, None)
return U, S, V
def svd(matrix, k=50):
"""
Calculate the truncated singular value decomposition
:math:`A = U * Sigma * V^T` using SVDLIBC.
Returns a triple of:
- U as a dense labeled matrix
- S, a dense vector representing the diagonal of Sigma
- V as a dense labeled matrix
This matrix must not contain any empty rows or columns. If it does,
use the .squish() method first.
"""
assert matrix.ndim == 2
if isinstance(matrix, DenseMatrix):
Ut, S, Vt = svd_ndarray(matrix, k)
elif isinstance(matrix, SparseMatrix):
if matrix.nnz == 0:
# don't let svdlib touch a matrix of all zeros. It explodes and
# corrupts its state. Just return a zero result instead.
U = DenseMatrix((matrix.shape[0], k))
S = np.zeros((k, ))
V = DenseMatrix((matrix.shape[1], k))
return U, S, V
if matrix.shape[1] >= matrix.shape[0] * 1.2:
# transpose the matrix for speed
V, S, U = matrix.T.svd(k)
return U, S, V
Ut, S, Vt = svd_llmat(matrix.llmatrix, k)
else:
raise TypeError("Don't know how to SVD a %r", type(matrix))
U = DenseMatrix(Ut.T, matrix.row_labels, None)
V = DenseMatrix(Vt.T, matrix.col_labels, None)
return U, S, V