def __init__(self,
D0,
S,
lmbda=None,
opt=None,
xmethod=None,
dmethod=None,
dimK=1,
dimN=2):
"""
|
**Call graph**
.. image:: ../_static/jonga/cbpdndl_init.svg
:width: 20%
:target: ../_static/jonga/cbpdndl_init.svg
|
Parameters
----------
D0 : array_like
Initial dictionary array
S : array_like
Signal array
lmbda : float
Regularisation parameter
opt : :class:`ConvBPDNDictLearn.Options` object
Algorithm options
xmethod : string, optional (default 'admm')
String selecting sparse coding solver. Valid values are
documented in function :func:`.ConvBPDN`.
dmethod : string, optional (default 'fista')
String selecting dictionary update solver. Valid values are
documented in function :func:`.ConvCnstrMOD`.
dimK : int, optional (default 1)
Number of signal dimensions. If there is only a single input
signal (e.g. if `S` is a 2D array representing a single image)
`dimK` must be set to 0.
dimN : int, optional (default 2)
Number of spatial/temporal dimensions
"""
if opt is None:
opt = ConvBPDNDictLearn.Options(xmethod=xmethod, dmethod=dmethod)
if xmethod is None:
xmethod = opt.xmethod
if dmethod is None:
dmethod = opt.dmethod
if opt.xmethod != xmethod or opt.dmethod != dmethod:
raise ValueError('Parameters xmethod and dmethod must have the '
'same values used to initialise the Options '
'object')
self.opt = opt
self.xmethod = xmethod
self.dmethod = dmethod
# Get dictionary size
if self.opt['DictSize'] is None:
dsz = D0.shape
else:
dsz = self.opt['DictSize']
# Construct object representing problem dimensions
cri = cr.CDU_ConvRepIndexing(dsz, S, dimK, dimN)
# Normalise dictionary
D0 = cr.Pcn(D0,
dsz,
cri.Nv,
dimN,
cri.dimCd,
crp=True,
zm=opt['CCMOD', 'ZeroMean'])
# Modify D update options to include initial value for Y
optname = 'X0' if dmethod == 'fista' else 'Y0'
opt['CCMOD'].update(
{optname: cr.zpad(cr.stdformD(D0, cri.Cd, cri.M, dimN), cri.Nv)})
# Create X update object
xstep = ConvBPDN(D0,
S,
lmbda,
opt['CBPDN'],
method=xmethod,
dimK=dimK,
dimN=dimN)
# Create D update object
dstep = ConvCnstrMOD(None,
S,
dsz,
opt['CCMOD'],
method=dmethod,
dimK=dimK,
dimN=dimN)
# Configure iteration statistics reporting
isc = dictlrn.IterStatsConfig(isfld=dc.isfld(xmethod, dmethod, opt),
isxmap=dc.isxmap(xmethod, opt),
isdmap=dc.isdmap(dmethod),
evlmap=dc.evlmap(opt['AccurateDFid']),
hdrtxt=dc.hdrtxt(xmethod, dmethod, opt),
hdrmap=dc.hdrmap(xmethod, dmethod, opt),
fmtmap={
'It_X': '%4d',
'It_D': '%4d'
})
# Call parent constructor
super(ConvBPDNDictLearn, self).__init__(xstep, dstep, opt, isc)
def __init__(self,
D0,
S,
lmbda,
W,
opt=None,
xmethod=None,
dmethod=None,
dimK=1,
dimN=2):
"""
|
**Call graph**
.. image:: ../_static/jonga/cbpdnmddl_init.svg
:width: 20%
:target: ../_static/jonga/cbpdnmddl_init.svg
|
Parameters
----------
D0 : array_like
Initial dictionary array
S : array_like
Signal array
lmbda : float
Regularisation parameter
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) after reshaping to the shape determined by
:func:`.cnvrep.mskWshape`.
opt : :class:`ConvBPDNMaskDictLearn.Options` object
Algorithm options
xmethod : string, optional (default 'admm')
String selecting sparse coding solver. Valid values are
documented in function :func:`.ConvBPDNMask`.
dmethod : string, optional (default 'pgm')
String selecting dictionary update solver. Valid values are
documented in function :func:`.ConvCnstrMODMask`.
dimK : int, optional (default 1)
Number of signal dimensions. If there is only a single input
signal (e.g. if `S` is a 2D array representing a single image)
`dimK` must be set to 0.
dimN : int, optional (default 2)
Number of spatial/temporal dimensions
"""
if opt is None:
opt = ConvBPDNMaskDictLearn.Options(xmethod=xmethod,
dmethod=dmethod)
if xmethod is None:
xmethod = opt.xmethod
if dmethod is None:
dmethod = opt.dmethod
if opt.xmethod != xmethod or opt.dmethod != dmethod:
raise ValueError('Parameters xmethod and dmethod must have the '
'same values used to initialise the Options '
'object')
self.opt = opt
self.xmethod = xmethod
self.dmethod = dmethod
# Get dictionary size
if self.opt['DictSize'] is None:
dsz = D0.shape
else:
dsz = self.opt['DictSize']
# Construct object representing problem dimensions
cri = cr.CDU_ConvRepIndexing(dsz, S, dimK, dimN)
# Normalise dictionary
D0 = cr.Pcn(D0,
dsz,
cri.Nv,
dimN,
cri.dimCd,
crp=True,
zm=opt['CCMOD', 'ZeroMean'])
# Modify D update options to include initial values for Y
if cri.C == cri.Cd:
Y0b0 = np.zeros(cri.Nv + (cri.C, 1, cri.K))
else:
Y0b0 = np.zeros(cri.Nv + (1, 1, cri.C * cri.K))
Y0b1 = cr.zpad(cr.stdformD(D0, cri.Cd, cri.M, dimN), cri.Nv)
if dmethod == 'pgm':
opt['CCMOD'].update({'X0': Y0b1})
else:
if dmethod == 'cns':
Y0 = Y0b1
else:
Y0 = np.concatenate((Y0b0, Y0b1), axis=cri.axisM)
opt['CCMOD'].update({'Y0': Y0})
# Create X update object
xstep = ConvBPDNMask(D0,
S,
lmbda,
W,
opt['CBPDN'],
method=xmethod,
dimK=dimK,
dimN=dimN)
# Create D update object
dstep = ConvCnstrMODMask(None,
S,
W,
dsz,
opt['CCMOD'],
method=dmethod,
dimK=dimK,
dimN=dimN)
# Configure iteration statistics reporting
isc = dictlrn.IterStatsConfig(isfld=dc.isfld(xmethod, dmethod, opt),
isxmap=dc.isxmap(xmethod, opt),
isdmap=dc.isdmap(dmethod),
evlmap=dc.evlmap(opt['AccurateDFid']),
hdrtxt=dc.hdrtxt(xmethod, dmethod, opt),
hdrmap=dc.hdrmap(xmethod, dmethod, opt),
fmtmap={
'It_X': '%4d',
'It_D': '%4d'
})
# Call parent constructor
super(ConvBPDNMaskDictLearn, self).__init__(xstep, dstep, opt, isc)
def __init__(self, D0, S, lmbda=None, opt=None, xmethod=None,
dmethod=None, dimK=1, dimN=2):
"""
|
**Call graph**
.. image:: ../_static/jonga/cbpdndl_init.svg
:width: 20%
:target: ../_static/jonga/cbpdndl_init.svg
|
Parameters
----------
D0 : array_like
Initial dictionary array
S : array_like
Signal array
lmbda : float
Regularisation parameter
opt : :class:`ConvBPDNDictLearn.Options` object
Algorithm options
xmethod : string, optional (default 'admm')
String selecting sparse coding solver. Valid values are
documented in function :func:`.ConvBPDN`.
dmethod : string, optional (default 'fista')
String selecting dictionary update solver. Valid values are
documented in function :func:`.ConvCnstrMOD`.
dimK : int, optional (default 1)
Number of signal dimensions. If there is only a single input
signal (e.g. if `S` is a 2D array representing a single image)
`dimK` must be set to 0.
dimN : int, optional (default 2)
Number of spatial/temporal dimensions
"""
if opt is None:
opt = ConvBPDNDictLearn.Options(xmethod=xmethod, dmethod=dmethod)
if xmethod is None:
xmethod = opt.xmethod
if dmethod is None:
dmethod = opt.dmethod
if opt.xmethod != xmethod or opt.dmethod != dmethod:
raise ValueError('Parameters xmethod and dmethod must have the '
'same values used to initialise the Options '
'object')
self.opt = opt
self.xmethod = xmethod
self.dmethod = dmethod
# Get dictionary size
if self.opt['DictSize'] is None:
dsz = D0.shape
else:
dsz = self.opt['DictSize']
# Construct object representing problem dimensions
cri = cr.CDU_ConvRepIndexing(dsz, S, dimK, dimN)
# Normalise dictionary
D0 = cr.Pcn(D0, dsz, cri.Nv, dimN, cri.dimCd, crp=True,
zm=opt['CCMOD', 'ZeroMean'])
# Modify D update options to include initial value for Y
optname = 'X0' if dmethod == 'fista' else 'Y0'
opt['CCMOD'].update({optname: cr.zpad(
cr.stdformD(D0, cri.Cd, cri.M, dimN), cri.Nv)})
# Create X update object
xstep = ConvBPDN(D0, S, lmbda, opt['CBPDN'], method=xmethod,
dimK=dimK, dimN=dimN)
# Create D update object
dstep = ConvCnstrMOD(None, S, dsz, opt['CCMOD'], method=dmethod,
dimK=dimK, dimN=dimN)
# Configure iteration statistics reporting
isc = dictlrn.IterStatsConfig(
isfld=dc.isfld(xmethod, dmethod, opt),
isxmap=dc.isxmap(xmethod, opt), isdmap=dc.isdmap(dmethod),
evlmap=dc.evlmap(opt['AccurateDFid']),
hdrtxt=dc.hdrtxt(xmethod, dmethod, opt),
hdrmap=dc.hdrmap(xmethod, dmethod, opt),
fmtmap={'It_X': '%4d', 'It_D': '%4d'})
# Call parent constructor
super(ConvBPDNDictLearn, self).__init__(xstep, dstep, opt, isc)