def compute_multiple(data, ncols, alg, compress=False, n_power_iter=0, step=1):
"""
Compute separable NMF of the input matrix with k columns, for
k = 1 to ncols.
:param data: Input matrix
:type data: numpy.ndarray
:param ncols: Maximum number of columns to select
:type ncols: int
:param alg: Choice of algorithm for computing the columns.
One of 'SPA' or 'XRAY'.
:type alg: basestring
:param compress: Whether to use compression or not
:type compress: bool
:param n_power_iter: Number of power iterations for compression
:type n_power_iter: int
:param step: Step size for k
:type step: int
:return:
The selected columns, the right factors of the separable NMF
decomposition, and the relative errors.
:rtype: list of tuples of:
- list of ints
- right factor matrix
- relative error of the approximation
"""
if compress:
data_comp, _ = csnmf.compression.compress(data, ncols, n_power_iter)
else:
if isinstance(data, da.Array):
_, data_comp = csnmf.tsqr.qr(data)
elif isinstance(data, np.ndarray):
_, data_comp = np.linalg.qr(data)
else:
raise TypeError('Cannot compute QR decomposition of matrices '
'of type ' + type(data).__name__)
colnorms = _compute_colnorms(data_comp)
try:
data_comp = np.array(data_comp)
colnorms = np.array(colnorms)
except:
raise
results = []
for k in range(step, ncols, step):
cols = mrnmf.select_columns(data_comp, colnorms, alg, k)
mat_h, error = mrnmf.nnls_frob(data_comp, cols)
results.append((cols, mat_h, error))
return results
def compute(data, ncols, alg, compress=False, n_power_iter=0):
"""
Compute separable NMF of the input matrix.
:param data: Input matrix
:type data: numpy.ndarray
:param ncols: Number of columns to select
:type ncols: int
:param alg: Choice of algorithm for computing the columns.
One of 'SPA' or 'XRAY'.
:type alg: basestring
:param compress: Whether to use compression or not
:type compress: bool
:param n_power_iter: Number of power iterations for compression
:type n_power_iter: int
:return:
The selected columns, the right factor of the separable NMF
decomposition, and the relative error.
:rtype: tuple of:
- list of ints
- right factor matrix
- relative error of the approximation
"""
if compress:
#data_comp, _ = csnmf.compression.compress(data, ncols, n_power_iter, our=False)
data_comp, _ = csnmf.compression.compress(data, ncols, n_power_iter, our=True)
else:
if isinstance(data, da.Array):
_, data_comp = csnmf.tsqr.qr(data)
elif isinstance(data, np.ndarray):
_, data_comp = np.linalg.qr(data)
else:
raise TypeError('Cannot compute QR decomposition of matrices '
'of type ' + type(data).__name__)
data_comp = np.array(data_comp)
if alg == 'SPA':
colnorms = _compute_colnorms(data_comp)
colnorms = np.array(colnorms)
else:
colnorms = None
cols = mrnmf.select_columns(data_comp, alg, ncols, colnorms=colnorms)
mat_h, error = mrnmf.nnls_frob(data_comp, cols)
return cols, mat_h, error
def compute(data, ncols, alg, compress=False, n_power_iter=0):
"""
Compute separable NMF of the input matrix.
:param data: Input matrix
:type data: numpy.ndarray
:param ncols: Number of columns to select
:type ncols: int
:param alg: Choice of algorithm for computing the columns.
One of 'SPA' or 'XRAY'.
:type alg: basestring
:param compress: Whether to use compression or not
:type compress: bool
:param n_power_iter: Number of power iterations for compression
:type n_power_iter: int
:return:
The selected columns, the right factor of the separable NMF
decomposition, and the relative error.
:rtype: tuple of:
- list of ints
- right factor matrix
- relative error of the approximation
"""
if compress:
data_comp, _ = csnmf.compression.compress(data, ncols, n_power_iter)
else:
if isinstance(data, da.Array):
_, data_comp = csnmf.tsqr.qr(data)
elif isinstance(data, np.ndarray):
_, data_comp = np.linalg.qr(data)
else:
raise TypeError('Cannot compute QR decomposition of matrices '
'of type ' + type(data).__name__)
data_comp = np.array(data_comp)
if alg == 'SPA':
colnorms = _compute_colnorms(data_comp)
colnorms = np.array(colnorms)
else:
colnorms = None
cols = mrnmf.select_columns(data_comp, alg, ncols, colnorms=colnorms)
mat_h, error = mrnmf.nnls_frob(data_comp, cols)
return cols, mat_h, error