def __init__(self, D, S, lmbda=None, W=None, opt=None):
"""
Parameters
----------
D : array_like
Dictionary array (2d)
S : array_like
Signal array (1d or 2d)
lmbda : float
Regularisation parameter
W : array_like
Weight array (1d or 2d)
opt : :class:`WeightedBPDN.Options` object
Algorithm options
"""
super(WeightedBPDN, self).__init__(D, S, lmbda, opt)
if W is None:
W = np.array([1.0], dtype=self.dtype)
if W.ndim > 0:
W = atleast_nd(2, W)
self.W = np.asarray(W, dtype=self.dtype)
def __init__(self, Z, S, W=None, dsz=None, opt=None):
"""
Parameters
----------
Z : array_like, shape (M, K)
Sparse representation coefficient matrix
S : array_like, shape (N, K)
Signal matrix
W : array_like, shape (N, K)
Weight matrix
dsz : tuple
Dictionary size
opt : :class:`WeightedCnstrMOD.Options` object
Algorithm options
"""
super(WeightedCnstrMOD, self).__init__(Z, S, dsz, opt)
if W is None:
W = np.array([1.0], dtype=self.dtype)
if W.ndim > 0:
W = atleast_nd(2, W)
self.W = np.asarray(W, dtype=self.dtype)
def __init__(self, A, S, lmbda, opt=None, axes=(0, 1), caxis=None):
"""
|
**Call graph**
.. image:: ../_static/jonga/tvl1dcn_init.svg
:width: 20%
:target: ../_static/jonga/tvl1dcn_init.svg
|
Parameters
----------
A : array_like
Filter kernel corresponding to operator :math:`H` above
S : array_like
Signal vector or matrix
lmbda : float
Regularisation parameter
opt : TVL1Deconv.Options object
Algorithm options
axes : tuple, optional (default (0,1))
Axes on which TV regularisation is to be applied
caxis : int or None, optional (default None)
Axis on which channels of a multi-channel image are stacked.
If None, TV regularisation is applied indepdendently to each
channel, otherwise Vector TV :cite:`blomgren-1998-color`
regularisation is applied jointly to all channels.
"""
if opt is None:
opt = TVL1Deconv.Options()
# Set dtype attribute based on S.dtype and opt['DataType']
self.set_dtype(opt, S.dtype)
self.axes = axes
self.axsz = tuple(np.asarray(S.shape)[list(axes)])
if caxis is None:
self.saxes = (-1, )
else:
self.saxes = (caxis, -1)
self.lmbda = self.dtype.type(lmbda)
# Set penalty parameter
self.set_attr('rho',
opt['rho'],
dval=(2.0 * self.lmbda + 0.1),
dtype=self.dtype)
yshape = S.shape + (len(axes) + 1, )
self.S = np.asarray(S, dtype=self.dtype)
super(TVL1Deconv, self).__init__(S.size, yshape, yshape, S.dtype, opt)
self.axshp = tuple([S.shape[k] for k in axes])
self.A = atleast_nd(S.ndim, A.astype(self.dtype))
self.Af = rfftn(self.A, self.axshp, axes=axes)
self.Sf = rfftn(self.S, axes=axes)
self.AHAf = np.conj(self.Af) * self.Af
self.AHSf = np.conj(self.Af) * self.Sf
self.Wdf = np.asarray(self.opt['DFidWeight'], dtype=self.dtype)
self.Wtv = np.asarray(self.opt['TVWeight'], dtype=self.dtype)
if hasattr(self.Wtv, 'ndim') and self.Wtv.ndim == S.ndim:
self.Wtvna = self.Wtv[..., np.newaxis]
else:
self.Wtvna = self.Wtv
self.Gf, self.GHGf = gradient_filters(S.ndim,
axes,
self.axshp,
dtype=self.dtype)
self.GAf = np.concatenate((self.Gf, self.Af[..., np.newaxis]),
axis=self.Gf.ndim - 1)
def __init__(self, A, S, lmbda, opt=None, axes=(0, 1), caxis=None):
"""
|
**Call graph**
.. image:: ../_static/jonga/tvl2dcn_init.svg
:width: 20%
:target: ../_static/jonga/tvl2dcn_init.svg
|
Parameters
----------
A : array_like
Filter kernel corresponding to operator :math:`H` above
S : array_like
Signal vector or matrix
lmbda : float
Regularisation parameter
opt : TVL2Deconv.Options object
Algorithm options
axes : tuple, optional (default (0, 1))
Axes on which TV regularisation is to be applied
caxis : int or None, optional (default None)
Axis on which channels of a multi-channel image are stacked.
If None, TV regularisation is applied indepdendently to each
channel, otherwise Vector TV :cite:`blomgren-1998-color`
regularisation is applied jointly to all channels.
"""
if opt is None:
opt = TVL2Deconv.Options()
# Set flag indicating whether problem involves real or complex
# values, and get appropriate versions of functions from fft
# module
self.real_dtype = np.isrealobj(S)
self.fftn = fftn_func(self.real_dtype)
self.ifftn = ifftn_func(self.real_dtype)
self.fl2norm2 = fl2norm2_func(self.real_dtype)
# Set dtype attribute based on S.dtype and opt['DataType']
self.set_dtype(opt, S.dtype)
self.S = np.asarray(S, dtype=self.dtype)
self.axes = axes
self.axsz = tuple([S.shape[i] for i in axes])
if caxis is None:
self.saxes = (-1,)
else:
self.saxes = (caxis, -1)
self.lmbda = real_dtype(self.dtype).type(lmbda)
# Set penalty parameter
self.set_attr('rho', opt['rho'], dval=(2.0*self.lmbda + 0.1),
dtype=real_dtype(self.dtype))
yshape = S.shape + (len(axes),)
super(TVL2Deconv, self).__init__(S.size, yshape, yshape, S.dtype, opt)
self.axshp = tuple([S.shape[k] for k in axes])
self.A = atleast_nd(S.ndim, A.astype(self.dtype))
self.Af = self.fftn(self.A, self.axshp, axes=axes)
self.Sf = self.fftn(self.S, axes=axes)
self.AHAf = np.conj(self.Af)*self.Af
self.AHSf = np.conj(self.Af)*self.Sf
self.Wtv = np.asarray(self.opt['TVWeight'],
dtype=real_dtype(self.dtype))
if hasattr(self.Wtv, 'ndim') and self.Wtv.ndim == S.ndim:
self.Wtvna = self.Wtv[..., np.newaxis]
else:
self.Wtvna = self.Wtv
# Construct gradient operators in frequency domain
self.Gf, self.GHGf = gradient_filters(S.ndim, axes, self.axshp,
dtype=self.dtype)
def __init__(self, Z, S, W, dsz, opt=None, dimK=None, dimN=2):
"""
|
**Call graph**
.. image:: ../_static/jonga/ccmodmdfista_init.svg
:width: 20%
:target: ../_static/jonga/ccmodmdfista_init.svg
|
Parameters
----------
Z : array_like
Coefficient map array
S : array_like
Signal array
W : array_like
Mask array. The array shape must be such that the array is
compatible for multiplication with the *internal* shape of
input array S (see :class:`.cnvrep.CDU_ConvRepIndexing` for a
discussion of the distinction between *external* and
*internal* data layouts).
dsz : tuple
Filter support size(s)
opt : :class:`ConvCnstrMODMask.Options` object
Algorithm options
dimK : 0, 1, or None, optional (default None)
Number of dimensions in input signal corresponding to multiple
independent signals
dimN : int, optional (default 2)
Number of spatial dimensions
"""
# Set default options if none specified
if opt is None:
opt = ConvCnstrMODMask.Options()
# Infer problem dimensions and set relevant attributes of self
self.cri = CDU_ConvRepIndexing(dsz, S, dimK=dimK, dimN=dimN)
# Append singleton dimensions to W if necessary
if hasattr(W, 'ndim'):
W = atleast_nd(self.cri.dimN + 3, W)
# Reshape W if necessary (see discussion of reshape of S in
# ccmod base class)
if self.cri.Cd == 1 and self.cri.C > 1 and hasattr(W, 'ndim'):
# In most cases broadcasting rules make it possible for W
# to have a singleton dimension corresponding to a
# non-singleton dimension in S. However, when S is
# reshaped to interleave axisC and axisK on the same axis,
# broadcasting is no longer sufficient unless axisC and
# axisK of W are either both singleton or both of the same
# size as the corresponding axes of S. If neither of these
# cases holds, it is necessary to replicate the axis of W
# (axisC or axisK) that does not have the same size as the
# corresponding axis of S.
shpw = list(W.shape)
swck = shpw[self.cri.axisC] * shpw[self.cri.axisK]
if swck > 1 and swck < self.cri.C * self.cri.K:
if W.shape[self.cri.axisK] == 1 and self.cri.K > 1:
shpw[self.cri.axisK] = self.cri.K
else:
shpw[self.cri.axisC] = self.cri.C
W = np.broadcast_to(W, shpw)
self.W = W.reshape(W.shape[0:self.cri.dimN] +
(1, W.shape[self.cri.axisC] *
W.shape[self.cri.axisK], 1))
else:
self.W = W
super(ConvCnstrMODMask, self).__init__(Z, S, dsz, opt, dimK, dimN)
# Create byte aligned arrays for FFT calls
self.WRy = empty_aligned(self.S.shape, dtype=self.dtype)
self.Ryf = rfftn_empty_aligned(self.S.shape, self.cri.axisN,
self.dtype)
def __init__(self, D0, S, lmbda=None, W=None, opt=None):
"""
Parameters
----------
D0 : array_like, shape (N, M)
Initial dictionary matrix
S : array_like, shape (N, K)
Signal vector or matrix
lmbda : float
Regularisation parameter
W : array_like, shape (N, K)
Weight matrix
opt : :class:`WeightedBPDNDictLearn.Options` object
Algorithm options
"""
if opt is None:
opt = WeightedBPDNDictLearn.Options()
self.opt = opt
# Normalise dictionary according to D update options
D0 = cmod.getPcn(opt['CMOD', 'ZeroMean'],
opt['CMOD', 'NonNegCoef'])(D0)
# Modify D update options to include initial values for Y and U
Nc = D0.shape[1]
opt['CMOD'].update({'X0': D0})
# Create X update object
xstep = bpdn.WeightedBPDN(D0, S, lmbda, W=W, opt=opt['BPDN'])
# Create D update object
Nm = S.shape[1]
dstep = cmod.WeightedCnstrMOD(xstep.Y, S, W=W, dsz=(Nc, Nm),
opt=opt['CMOD'])
if W is None:
W = np.array([1.0], dtype=xstep.dtype)
if W.ndim > 0:
W = atleast_nd(2, W)
self.W = np.asarray(W, dtype=xstep.dtype)
# Configure iteration statistics reporting
if self.opt['AccurateDFid']:
isxmap = {'XRsdl': 'Rsdl', 'XL': 'L'}
evlmap = {'ObjFun': 'ObjFun', 'DFid': 'DFid', 'RegL1': 'RegL1'}
else:
isxmap = {'ObjFun': 'ObjFun', 'DFid': 'DFid', 'RegL1': 'RegL1',
'XRsdl': 'Rsdl', 'XL': 'L'}
evlmap = {}
isc = dictlrn.IterStatsConfig(
isfld=['Iter', 'ObjFun', 'DFid', 'RegL1', 'Cnstr', 'XRsdl',
'XL', 'DRsdl', 'DL', 'Time'],
isxmap=isxmap,
isdmap={'Cnstr': 'Cnstr', 'DRsdl': 'Rsdl', 'DL': 'L'},
evlmap=evlmap,
hdrtxt=['Itn', 'Fnc', 'DFid', u('ℓ1'), 'Cnstr', 'X_Rsdl',
'X_L', 'D_Rsdl', 'D_L'],
hdrmap={'Itn': 'Iter', 'Fnc': 'ObjFun', 'DFid': 'DFid',
u('ℓ1'): 'RegL1', 'Cnstr': 'Cnstr', 'X_Rsdl': 'XRsdl',
'X_L': 'XL', 'D_Rsdl': 'DRsdl', 'D_L': 'DL'}
)
# Call parent constructor
super(WeightedBPDNDictLearn, self).__init__(xstep, dstep, opt, isc)
