def random(shape,
density=1.0,
rng=utils.RandomUniform(0, 1),
sort=False,
normalise=False):
"""Generates a random p-by-1 vector.
shape : tuple
The shape of the underlying data. E.g., beta may represent an
underlying 2-by-3-by-4 image, and will in that case be 24-by-1.
density : float in (0, 1], optional
The density of the returned regression vector (fraction of non-zero
elements). Zero-elements will be randomly distributed in the vector.
Default is 1.0.
rng : function or callable, optional
The random number generator. Must be a function that takes *shape as
input. Default is utils.RandomUniform in the interval [0, 1).
sort : boolean, optional
Whether or not to sort the vector. The vector is sorted along the
dimensions in order from the first. Default is False.
normalise : boolean, boolean
Whether or not to normalise the vector. Default is False.
"""
if not isinstance(shape, (list, tuple)):
shape = (shape, )
density = max(0.0, min(density, 1.0))
p = np.prod(shape)
ps = int(density * p + 0.5)
beta = rng(p)
beta[ps:] = 0.0
# Use and update the random number generator for permuting beta
random_state = np.random.RandomState()
random_state.set_state(rng.get_state())
beta = random_state.permutation(beta)
rng.set_state(random_state.get_state())
# beta = np.random.permutation(beta)
if sort:
beta = np.reshape(beta, shape)
for i in xrange(len(shape)):
beta = np.sort(beta, axis=i)
beta = np.reshape(beta, (p, 1))
if normalise:
beta /= np.linalg.norm(beta)
return beta
def __init__(self,
l,
A,
mu=utils.TOLERANCE,
rng=utils.RandomUniform(-1, 1),
norm=L2.grad,
**kwargs):
super(NesterovFunction, self).__init__(l, rng=rng, norm=norm, **kwargs)
self.A = A
self.mu = mu
def random(shape,
density=1.0,
rng=utils.RandomUniform(0, 1).rand,
sort=False,
normalise=False):
"""Generates a random p-by-1 vector.
shape : A tuple. The shape of the underlying data. E.g., beta may represent
an underlying 2-by-3-by-4 image, and will in that case be 24-by-1.
density : A scalar in (0, 1]. The density of the returned regression vector
(fraction of non-zero elements). Zero-elements will be randomly
distributed in the vector. Default is 1.0.
rng : The random number generator. Must be a function that takes *shape as
input. Default is utils.RandomUniform in the interval [0, 1).
sort : A boolean. Whether or not to sort the vector. The vector is sorted
along the dimensions in order from the first. Default is False.
normalise : A boolean. Whether or not to normalise the vector. Default is
False.
"""
if not isinstance(shape, (list, tuple)):
shape = (shape, )
density = max(0.0, min(density, 1.0))
p = np.prod(shape)
ps = int(density * p + 0.5)
beta = rng(p)
beta[ps:] = 0.0
beta = np.random.permutation(beta)
if sort:
beta = np.reshape(beta, shape)
for i in xrange(len(shape)):
beta = np.sort(beta, axis=i)
beta = np.reshape(beta, (p, 1))
if normalise:
beta *= 1.0 / utils.norm2(beta)
return beta
def __init__(self, l, A, rng=utils.RandomUniform(-1, 1), **kwargs):
super(GroupLasso, self).__init__(l, A=A, rng=rng, **kwargs)
def __init__(self, l, rng=utils.RandomUniform(-1, 1)):
super(L1, self).__init__(l, rng=rng)
def __init__(self, l, A, rng=utils.RandomUniform(0, 1), **kwargs):
super(TotalVariation, self).__init__(l, A=A, rng=rng, **kwargs)