def __init__(self, D0, S, lmbda=None, opt=None):
"""
Initialise a BPDNDictLearn object with problem size and options.
Parameters
----------
D0 : array_like, shape (N, M)
Initial dictionary matrix
S : array_like, shape (N, K)
Signal vector or matrix
lmbda : float
Regularisation parameter
opt : :class:`BPDNDictLearn.Options` object
Algorithm options
"""
if opt is None:
opt = BPDNDictLearn.Options()
self.opt = opt
# Normalise dictionary according to D update options
D0 = cmod.getPcn(opt['CMOD', 'ZeroMean'])(D0)
# Modify D update options to include initial values for Y and U
Nc = D0.shape[1]
opt['CMOD'].update({'Y0' : D0, 'U0' : np.zeros((S.shape[0], Nc))})
# Create X update object
xstep = bpdn.BPDN(D0, S, lmbda, opt['BPDN'])
# Create D update object
Nm = S.shape[1]
dstep = cmod.CnstrMOD(xstep.Y, S, (Nc, Nm), opt['CMOD'])
# Configure iteration statistics reporting
isc = dictlrn.IterStatsConfig(
isfld = ['Iter', 'ObjFun', 'DFid', 'RegL1', 'Cnstr', 'XPrRsdl',
'XDlRsdl', 'XRho', 'DPrRsdl', 'DDlRsdl', 'DRho', 'Time'],
isxmap = {'ObjFun' : 'ObjFun', 'DFid' : 'DFid', 'RegL1' : 'RegL1',
'XPrRsdl' : 'PrimalRsdl', 'XDlRsdl' : 'DualRsdl',
'XRho' : 'Rho'},
isdmap = {'Cnstr' : 'Cnstr', 'DPrRsdl' : 'PrimalRsdl',
'DDlRsdl' : 'DualRsdl', 'DRho' : 'Rho'},
evlmap = {},
hdrtxt = ['Itn', 'Fnc', 'DFid', u('ℓ1'), 'Cnstr', 'r_X', 's_X',
u('ρ_X'), 'r_D', 's_D', u('ρ_D')],
hdrmap = {'Itn' : 'Iter', 'Fnc' : 'ObjFun', 'DFid' : 'DFid',
u('ℓ1') : 'RegL1', 'Cnstr' : 'Cnstr', 'r_X' : 'XPrRsdl',
's_X' : 'XDlRsdl', u('ρ_X') : 'XRho', 'r_D' : 'DPrRsdl',
's_D' : 'DDlRsdl', u('ρ_D') : 'DRho'}
)
# Call parent constructor
super(BPDNDictLearn, self).__init__(xstep, dstep, opt, isc)
def __init__(self, D0, S, lmbda=None, opt=None):
"""
|
**Call graph**
.. image:: ../_static/jonga/bpdndl_init.svg
:width: 20%
:target: ../_static/jonga/bpdndl_init.svg
|
Parameters
----------
D0 : array_like, shape (N, M)
Initial dictionary matrix
S : array_like, shape (N, K)
Signal vector or matrix
lmbda : float
Regularisation parameter
opt : :class:`BPDNDictLearn.Options` object
Algorithm options
"""
if opt is None:
opt = BPDNDictLearn.Options()
self.opt = opt
# Normalise dictionary according to D update options
D0 = cmod.getPcn(opt['CMOD', 'ZeroMean'])(D0)
# Modify D update options to include initial values for Y and U
Nc = D0.shape[1]
opt['CMOD'].update({'Y0': D0, 'U0': np.zeros((S.shape[0], Nc))})
# Create X update object
xstep = bpdn.BPDN(D0, S, lmbda, opt['BPDN'])
# Create D update object
Nm = S.shape[1]
dstep = cmod.CnstrMOD(xstep.Y, S, (Nc, Nm), opt['CMOD'])
# Configure iteration statistics reporting
if self.opt['AccurateDFid']:
isxmap = {'XPrRsdl': 'PrimalRsdl', 'XDlRsdl': 'DualRsdl',
'XRho': 'Rho'}
evlmap = {'ObjFun': 'ObjFun', 'DFid': 'DFid', 'RegL1': 'RegL1'}
else:
isxmap = {'ObjFun': 'ObjFun', 'DFid': 'DFid', 'RegL1': 'RegL1',
'XPrRsdl': 'PrimalRsdl', 'XDlRsdl': 'DualRsdl',
'XRho': 'Rho'}
evlmap = {}
isc = dictlrn.IterStatsConfig(
isfld=['Iter', 'ObjFun', 'DFid', 'RegL1', 'Cnstr', 'XPrRsdl',
'XDlRsdl', 'XRho', 'DPrRsdl', 'DDlRsdl', 'DRho', 'Time'],
isxmap=isxmap,
isdmap={'Cnstr': 'Cnstr', 'DPrRsdl': 'PrimalRsdl',
'DDlRsdl': 'DualRsdl', 'DRho': 'Rho'},
evlmap=evlmap,
hdrtxt=['Itn', 'Fnc', 'DFid', u('ℓ1'), 'Cnstr', 'r_X', 's_X',
u('ρ_X'), 'r_D', 's_D', u('ρ_D')],
hdrmap={'Itn': 'Iter', 'Fnc': 'ObjFun', 'DFid': 'DFid',
u('ℓ1'): 'RegL1', 'Cnstr': 'Cnstr', 'r_X': 'XPrRsdl',
's_X': 'XDlRsdl', u('ρ_X'): 'XRho', 'r_D': 'DPrRsdl',
's_D': 'DDlRsdl', u('ρ_D'): 'DRho'}
)
# Call parent constructor
super(BPDNDictLearn, self).__init__(xstep, dstep, opt, isc)
# X and D update options
lmbda = 0.1
optx = bpdn.BPDN.Options({
'Verbose': False,
'MaxMainIter': 1,
'rho': 50.0 * lmbda + 0.5
})
optd = cmod.CnstrMOD.Options({
'Verbose': False,
'MaxMainIter': 1,
'rho': S.shape[1] / 200.0
})
# Normalise dictionary according to D update options
D0 = cmod.getPcn(optd['ZeroMean'])(D0)
# Update D update options to include initial values for Y and U
optd.update({'Y0': D0, 'U0': np.zeros((S.shape[0], D0.shape[1]))})
# Create X update object
xstep = bpdn.BPDN(D0, S, lmbda, optx)
# Create D update object
dstep = cmod.CnstrMOD(None, S, (D0.shape[1], S.shape[1]), optd)
# Create DictLearn object
opt = dictlrn.DictLearn.Options({'Verbose': True, 'MaxMainIter': 100})
d = dictlrn.DictLearn(xstep, dstep, opt)
Dmx = d.solve()
print("DictLearn solve time: %.2fs" % d.timer.elapsed('solve'))