def gram_block_circulant(ds, n_times_valid, method='full',
sample_weights=None):
"""Returns ...
Parameters
----------
ds : array, shape (n_atoms, n_times_atom)
The atoms
n_times_valid : int
n_times - n_times_atom + 1
method : string
If 'full', returns full circulant matrix.
If 'scipy', returns scipy linear operator.
If 'custom', returns custom linear operator.
sample_weights : array, shape (n_times, )
The sample weights for one trial
"""
from scipy.sparse.linalg import LinearOperator
from functools import partial
n_atoms, n_times_atom = ds.shape
n_times = n_times_valid + n_times_atom - 1
if method == 'full':
D = np.zeros((n_times, n_atoms * n_times_valid))
for k_idx in range(n_atoms):
d_padded = np.zeros((n_times, ))
d_padded[:n_times_atom] = ds[k_idx]
start = k_idx * n_times_valid
stop = start + n_times_valid
D[:, start:stop] = linalg.circulant((d_padded))[:, :n_times_valid]
if sample_weights is not None:
wD = sample_weights[:, None] * D
return np.dot(D.T, wD)
else:
return np.dot(D.T, D)
elif method == 'scipy':
def matvec(v, ds):
assert v.shape[0] % ds.shape[0] == 0
return _fprime(ds, v, Xi=None, reg=None,
sample_weights=sample_weights)
D = LinearOperator((n_atoms * n_times_valid, n_atoms * n_times_valid),
matvec=partial(matvec, ds=ds))
elif method == 'custom':
return CustomLinearOperator(ds, n_times_valid, sample_weights)
else:
raise ValueError('Unkown method %s.' % method)
return D
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