'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'))
# Display dictionaries
D1 = Dmx.reshape((8, 8, D0.shape[1]))
D0 = D0.reshape(8, 8, D0.shape[-1])
fig1 = plot.figure(1, figsize=(14, 7))
plot.subplot(1, 2, 1)
plot.imview(util.tiledict(D0), fgrf=fig1, title='D0')
plot.subplot(1, 2, 2)
plot.imview(util.tiledict(D1), fgrf=fig1, title='D1')
"""
lmbda = 0.1
opt = bpdn.BPDN.Options({'Verbose': True, 'MaxMainIter': 200,
'RelStopTol': 1e-3})
b = bpdn.BPDN(D0, S, lmbda, opt)
X = b.solve()
"""
Update dictionary for training image set.
"""
opt = cmod.CnstrMOD.Options({'Verbose': True, 'MaxMainIter': 100,
'RelStopTol': 1e-3, 'rho': 4e2})
c = cmod.CnstrMOD(X, S, None, opt)
D1 = c.solve()
print("CMOD solve time: %.2fs" % c.timer.elapsed('solve'))
"""
Display initial and final dictionaries.
"""
D0 = D0.reshape((8, 8, D0.shape[-1]))
D1 = D1.reshape((8, 8, D1.shape[-1]))
fig = plot.figure(figsize=(14, 7))
plot.subplot(1, 2, 1)
plot.imview(util.tiledict(D0), fig=fig, title='D0')
plot.subplot(1, 2, 2)
plot.imview(util.tiledict(D1), fig=fig, title='D1')
def gengraphs(pth, nopyfftw):
"""
Generate call graph images when necessary. Parameter pth is the path
to the directory in which images are to be created. Parameter nopyfftw
is a flag indicating whether it is necessary to avoid using pyfftw.
"""
srcmodflt = '^sporco.admm'
srcqnmflt = r'^((?!<locals>|__new|_name_nested).)*$'
dstqnmflt = r'^((?!<locals>|__new|_name_nested).)*$'
fnmsub = ('^sporco.admm.', '')
grpflt = r'^[^\.]*.[^\.]*'
lnksub = (r'^([^\.]*).(.*)',
r'../../sporco.admm.\1.html#sporco.admm.\1.\2')
fntsz = 9
fntfm = 'Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans'
kwargs = {'fntsz': fntsz, 'fntfm': fntfm, 'rmsz': True}
ct = jonga.CallTracer(srcmodflt=srcmodflt,
srcqnmflt=srcqnmflt,
dstqnmflt=dstqnmflt,
fnmsub=fnmsub,
grpflt=grpflt,
lnksub=lnksub)
# Make destination directory if it doesn't exist
if not os.path.exists(pth):
os.makedirs(pth, exist_ok=True)
# Handle environment in which pyfftw is unavailable
if nopyfftw:
import numpy.fft as npfft
import sporco.linalg as spl
def empty(shape, dtype, order='C', n=None):
return np.zeros(shape, dtype=dtype)
spl.pyfftw_empty_aligned = empty
spl.fftn = npfft.fftn
spl.ifftn = npfft.ifftn
spl.rfftn = npfft.rfftn
spl.irfftn = npfft.irfftn
import numpy as np
np.random.seed(12345)
#### bpdn module
from sporco.admm import bpdn
mdnm = 'sporco.admm.bpdn'
D = np.random.randn(8, 16)
s = np.random.randn(8, 1)
lmbda = 0.1
## BPDN class
opt = bpdn.BPDN.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'bpdn_init.svg', **kwargs):
b = bpdn.BPDN(D, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'bpdn_solve.svg', **kwargs):
b.solve()
## BPDNJoint class
opt = bpdn.BPDNJoint.Options({'Verbose': False, 'MaxMainIter': 1})
mu = 0.01
with CallGraph(ct, mdnm, pth, 'bpdnjnt_init.svg', **kwargs):
b = bpdn.BPDNJoint(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'bpdnjnt_solve.svg', **kwargs):
b.solve()
## ElasticNet class
opt = bpdn.ElasticNet.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'elnet_init.svg', **kwargs):
b = bpdn.ElasticNet(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'elnet_solve.svg', **kwargs):
b.solve()
# BPDNProjL1 class
opt = bpdn.BPDNProjL1.Options({'Verbose': False, 'MaxMainIter': 1})
gamma = 2.0
with CallGraph(ct, mdnm, pth, 'bpdnprjl1_init.svg', **kwargs):
b = bpdn.BPDNProjL1(D, s, gamma, opt)
with CallGraph(ct, mdnm, pth, 'bpdnprjl1_solve.svg', **kwargs):
b.solve()
## MinL1InL2Ball class
opt = bpdn.MinL1InL2Ball.Options({'Verbose': False, 'MaxMainIter': 1})
epsilon = 1.0
with CallGraph(ct, mdnm, pth, 'bpdnml1l2_init.svg', **kwargs):
b = bpdn.MinL1InL2Ball(D, s, epsilon, opt)
with CallGraph(ct, mdnm, pth, 'bpdnml1l2_solve.svg', **kwargs):
b.solve()
#### cbpdn module
from sporco.admm import cbpdn
mdnm = 'sporco.admm.cbpdn'
D = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8)
lmbda = 0.1
## ConvBPDN class
opt = cbpdn.ConvBPDN.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cbpdn_init.svg', **kwargs):
b = cbpdn.ConvBPDN(D, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'cbpdn_solve.svg', **kwargs):
b.solve()
## ConvBPDNJoint class
opt = cbpdn.ConvBPDNJoint.Options({'Verbose': False, 'MaxMainIter': 1})
mu = 0.01
with CallGraph(ct, mdnm, pth, 'cbpdnjnt_init.svg', **kwargs):
b = cbpdn.ConvBPDNJoint(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnjnt_solve.svg', **kwargs):
b.solve()
## ConvElasticNet class
opt = cbpdn.ConvElasticNet.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'celnet_init.svg', **kwargs):
b = cbpdn.ConvElasticNet(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'celnet_solve.svg', **kwargs):
b.solve()
## ConvBPDNGradReg class
opt = cbpdn.ConvBPDNGradReg.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cbpdngrd_init.svg', **kwargs):
b = cbpdn.ConvBPDNGradReg(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdngrd_solve.svg', **kwargs):
b.solve()
## ConvBPDNProjL1 class
opt = cbpdn.ConvBPDNProjL1.Options({'Verbose': False, 'MaxMainIter': 1})
gamma = 0.5
with CallGraph(ct, mdnm, pth, 'cbpdnprjl1_init.svg', **kwargs):
b = cbpdn.ConvBPDNProjL1(D, s, gamma, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnprjl1_solve.svg', **kwargs):
b.solve()
## ConvMinL1InL2Ball class
opt = cbpdn.ConvMinL1InL2Ball.Options({'Verbose': False, 'MaxMainIter': 1})
epsilon = 0.5
with CallGraph(ct, mdnm, pth, 'cbpdnml1l2_init.svg', **kwargs):
b = cbpdn.ConvMinL1InL2Ball(D, s, epsilon, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnml1l2_solve.svg', **kwargs):
b.solve()
## ConvBPDNMaskDcpl class
opt = cbpdn.ConvBPDNMaskDcpl.Options({'Verbose': False, 'MaxMainIter': 1})
W = np.ones(s.shape)
with CallGraph(ct, mdnm, pth, 'cbpdnmd_init.svg', **kwargs):
b = cbpdn.ConvBPDNMaskDcpl(D, s, lmbda, W, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnmd_solve.svg', **kwargs):
b.solve()
#### cbpdntv module
from sporco.admm import cbpdntv
mdnm = 'sporco.admm.cbpdntv'
D = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8)
lmbda = 0.1
mu = 0.01
## ConvBPDNScalarTV class
opt = cbpdntv.ConvBPDNScalarTV.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'cbpdnstv_init.svg', **kwargs):
b = cbpdntv.ConvBPDNScalarTV(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnstv_solve.svg', **kwargs):
b.solve()
## ConvBPDNVectorTV class
opt = cbpdntv.ConvBPDNVectorTV.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'cbpdnvtv_init.svg', **kwargs):
b = cbpdntv.ConvBPDNVectorTV(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnvtv_solve.svg', **kwargs):
b.solve()
## ConvBPDNRecTV class
opt = cbpdntv.ConvBPDNRecTV.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cbpdnrtv_init.svg', **kwargs):
b = cbpdntv.ConvBPDNRecTV(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnrtv_solve.svg', **kwargs):
b.solve()
#### cmod module
from sporco.admm import cmod
mdnm = 'sporco.admm.cmod'
X = np.random.randn(8, 16)
S = np.random.randn(8, 16)
## CnstrMOD class
opt = cmod.CnstrMOD.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cmod_init.svg', **kwargs):
b = cmod.CnstrMOD(X, S, opt=opt)
with CallGraph(ct, mdnm, pth, 'cmod_solve.svg', **kwargs):
b.solve()
#### ccmod module
from sporco.admm import ccmod
mdnm = 'sporco.admm.ccmod'
X = np.random.randn(8, 8, 1, 2, 1)
S = np.random.randn(8, 8, 2)
dsz = (4, 4, 1)
## ConvCnstrMOD_IterSM class
opt = ccmod.ConvCnstrMOD_IterSM.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodism_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD_IterSM(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodism_solve.svg', **kwargs):
b.solve()
## ConvCnstrMOD_CG class
opt = ccmod.ConvCnstrMOD_CG.Options({
'Verbose': False,
'MaxMainIter': 1,
'CG': {
'MaxIter': 1
}
})
with CallGraph(ct, mdnm, pth, 'ccmodcg_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD_CG(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodcg_solve.svg', **kwargs):
b.solve()
## ConvCnstrMOD_Consensus class
opt = ccmod.ConvCnstrMOD_Consensus.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodcnsns_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD_Consensus(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodcnsns_solve.svg', **kwargs):
b.solve()
#### ccmodmd module
from sporco.admm import ccmodmd
mdnm = 'sporco.admm.ccmodmd'
X = np.random.randn(8, 8, 1, 2, 1)
S = np.random.randn(8, 8, 2)
W = np.array([1.0])
dsz = (4, 4, 1)
## ConvCnstrMODMaskDcpl_IterSM class
opt = ccmodmd.ConvCnstrMODMaskDcpl_IterSM.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodmdism_init.svg', **kwargs):
b = ccmodmd.ConvCnstrMODMaskDcpl_IterSM(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdism_solve.svg', **kwargs):
b.solve()
## ConvCnstrMODMaskDcpl_CG class
opt = ccmodmd.ConvCnstrMODMaskDcpl_CG.Options({
'Verbose': False,
'MaxMainIter': 1,
'CG': {
'MaxIter': 1
}
})
with CallGraph(ct, mdnm, pth, 'ccmodmdcg_init.svg', **kwargs):
b = ccmodmd.ConvCnstrMODMaskDcpl_CG(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdcg_solve.svg', **kwargs):
b.solve()
## ConvCnstrMODMaskDcpl_Consensus class
opt = ccmodmd.ConvCnstrMODMaskDcpl_Consensus.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodmdcnsns_init.svg', **kwargs):
b = ccmodmd.ConvCnstrMODMaskDcpl_Consensus(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdcnsns_solve.svg', **kwargs):
b.solve()
#### bpdndl module
from sporco.admm import bpdndl
mdnm = 'sporco.admm.bpdndl'
D0 = np.random.randn(8, 8)
S = np.random.randn(8, 16)
lmbda = 0.1
## BPDNDictLearn class
opt = bpdndl.BPDNDictLearn.Options({
'Verbose': False,
'MaxMainIter': 1,
'AccurateDFid': True
})
with CallGraph(ct, mdnm, pth, 'bpdndl_init.svg', **kwargs):
b = bpdndl.BPDNDictLearn(D0, S, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'bpdndl_solve.svg', **kwargs):
b.solve()
#### cbpdndl module
from sporco.admm import cbpdndl
mdnm = 'sporco.admm.cbpdndl'
D0 = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8, 10)
lmbda = 0.1
## ConvBPDNDictLearn class
opt = cbpdndl.ConvBPDNDictLearn.Options({
'Verbose': False,
'MaxMainIter': 1,
'AccurateDFid': True
})
with CallGraph(ct, mdnm, pth, 'cbpdndl_init.svg', **kwargs):
b = cbpdndl.ConvBPDNDictLearn(D0, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'cbpdndl_solve.svg', **kwargs):
b.solve()
## ConvBPDNMaskDcplDictLearn class
W = np.array([1.0])
opt = cbpdndl.ConvBPDNMaskDcplDictLearn.Options({
'Verbose': False,
'MaxMainIter': 1,
'AccurateDFid': True
})
with CallGraph(ct, mdnm, pth, 'cbpdnmddl_init.svg', **kwargs):
b = cbpdndl.ConvBPDNMaskDcplDictLearn(D0, s, lmbda, W, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnmddl_solve.svg', **kwargs):
b.solve()
#### tvl1 module
from sporco.admm import tvl1
mdnm = 'sporco.admm.tvl1'
s = np.random.randn(16, 16)
lmbda = 0.1
## TVL1Denoise class
opt = tvl1.TVL1Denoise.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'tvl1den_init.svg', **kwargs):
b = tvl1.TVL1Denoise(s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl1den_solve.svg', **kwargs):
b.solve()
## TVL1Deconv class
opt = tvl1.TVL1Deconv.Options({'Verbose': False, 'MaxMainIter': 1})
h = np.random.randn(3, 3)
with CallGraph(ct, mdnm, pth, 'tvl1dcn_init.svg', **kwargs):
b = tvl1.TVL1Deconv(h, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl1dcn_solve.svg', **kwargs):
b.solve()
#### tvl2 module
from sporco.admm import tvl2
mdnm = 'sporco.admm.tvl2'
s = np.random.randn(16, 16)
lmbda = 0.1
## TVL2Denoise class
opt = tvl2.TVL2Denoise.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'tvl2den_init.svg', **kwargs):
b = tvl2.TVL2Denoise(s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl2den_solve.svg', **kwargs):
b.solve()
## TVL2Deconv class
opt = tvl2.TVL2Deconv.Options({'Verbose': False, 'MaxMainIter': 1})
h = np.random.randn(3, 3)
with CallGraph(ct, mdnm, pth, 'tvl2dcn_init.svg', **kwargs):
b = tvl2.TVL2Deconv(h, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl2dcn_solve.svg', **kwargs):
b.solve()
srcmodflt = '^sporco.fista'
fnmsub = ('^sporco.fista.', '')
lnksub = (r'^([^\.]*).(.*)',
r'../../sporco.fista.\1.html#sporco.fista.\1.\2')
ct = jonga.CallTracer(srcmodflt=srcmodflt,
srcqnmflt=srcqnmflt,
dstqnmflt=dstqnmflt,
fnmsub=fnmsub,
grpflt=grpflt,
lnksub=lnksub)
#### fista.cbpdn module
from sporco.fista import cbpdn
mdnm = 'sporco.fista.cbpdn'
D = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8)
lmbda = 0.1
## ConvBPDN class
opt = cbpdn.ConvBPDN.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'fista_cbpdn_init.svg', **kwargs):
b = cbpdn.ConvBPDN(D, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'fista_cbpdn_solve.svg', **kwargs):
b.solve()
#### fista.ccmod module
from sporco.fista import ccmod
mdnm = 'sporco.fista.ccmod'
X = np.random.randn(8, 8, 1, 2, 1)
S = np.random.randn(8, 8, 2)
dsz = (4, 4, 1)
## ConvCnstrMOD class
opt = ccmod.ConvCnstrMOD.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'ccmodfista_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodfista_solve.svg', **kwargs):
b.solve()
## ConvCnstrMODMaskDcpl class
opt = ccmod.ConvCnstrMODMaskDcpl.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodmdfista_init.svg', **kwargs):
b = ccmod.ConvCnstrMODMaskDcpl(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdfista_solve.svg', **kwargs):
b.solve()
import sporco.dictlearn.dictlearn as dictlearn
import sporco.cupy.admm.bpdn as bpdn
import sporco.admm.cmod as admm
import cupy as cp
import numpy as np
data_ra=np.load('Data_test/data_ra_test',allow_pickle=True)
v_ra=np.load('Data_81sets/v_ra',allow_pickle=True)
# REMEBER TO TRANSPOSE THE MATRICES IN THE FORMULATION
# v_ra.T @ u_ra.T = data_ra.T
x=bpdn.BPDN(np2cp(v_ra.T),np2cp(data_ra.T),0.4)
u_ra=x.
d=admm.CnstrMOD()
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)
'Verbose': True,
'MaxMainIter': 200,
'RelStopTol': 1e-3
})
b = bpdn.BPDN(D0, S, lmbda, opt)
b.solve()
print("BPDN solve time: %.2fs\n" % b.timer.elapsed('solve'))
# Update dictionary for training set S
opt = cmod.CnstrMOD.Options({
'Verbose': True,
'MaxMainIter': 100,
'RelStopTol': 1e-3,
'rho': 4e2
})
c = cmod.CnstrMOD(b.Y, S, None, opt)
c.solve()
print("CMOD solve time: %.2fs" % c.timer.elapsed('solve'))
# Display dictionaries
D0 = D0.reshape(8, 8, D0.shape[-1])
D1 = c.Y.reshape((8, 8, c.Y.shape[-1]))
fig1 = plot.figure(1, figsize=(20, 10))
plot.subplot(1, 2, 1)
plot.imview(util.tiledict(D0), fgrf=fig1, title='D0')
plot.subplot(1, 2, 2)
plot.imview(util.tiledict(D1), fgrf=fig1, title='D1')
fig1.show()
# Plot functional value, residuals, and rho
its = c.getitstat()
def gengraphs(pth):
"""
Generate call graph images when necessary. Parameter pth is the path
to the directory in which images are to be created.
"""
srcmodflt = '^sporco.admm'
srcqnmflt = r'^((?!<locals>|__new|_name_nested).)*$'
dstqnmflt = r'^((?!<locals>|__new|_name_nested).)*$'
fnmsub = ('^sporco.admm.', '')
grpflt = r'^[^\.]*.[^\.]*'
lnkpfx = '../../modules/'
lnksub = (r'^([^\.]*).([^\.]*)(?:(.__init__|.__call__)|(.[^\.]*))',
lnkpfx + r'sporco.admm.\1.html#sporco.admm.\1.\2\4')
fntsz = 9
fntfm = 'Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans'
kwargs = {'fntsz': fntsz, 'fntfm': fntfm, 'rmsz': True}
ct = jonga.CallTracer(srcmodflt=srcmodflt,
srcqnmflt=srcqnmflt,
dstqnmflt=dstqnmflt,
fnmsub=fnmsub,
grpflt=grpflt,
lnksub=lnksub)
# Make destination directory if it doesn't exist
if not os.path.exists(pth):
os.makedirs(pth, exist_ok=True)
import numpy as np
np.random.seed(12345)
#### bpdn module
from sporco.admm import bpdn
mdnm = 'sporco.admm.bpdn'
D = np.random.randn(8, 16)
s = np.random.randn(8, 1)
lmbda = 0.1
## BPDN class
opt = bpdn.BPDN.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'bpdn_init.svg', **kwargs):
b = bpdn.BPDN(D, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'bpdn_solve.svg', **kwargs):
b.solve()
## BPDNJoint class
opt = bpdn.BPDNJoint.Options({'Verbose': False, 'MaxMainIter': 1})
mu = 0.01
with CallGraph(ct, mdnm, pth, 'bpdnjnt_init.svg', **kwargs):
b = bpdn.BPDNJoint(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'bpdnjnt_solve.svg', **kwargs):
b.solve()
## ElasticNet class
opt = bpdn.ElasticNet.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'elnet_init.svg', **kwargs):
b = bpdn.ElasticNet(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'elnet_solve.svg', **kwargs):
b.solve()
# BPDNProjL1 class
opt = bpdn.BPDNProjL1.Options({'Verbose': False, 'MaxMainIter': 1})
gamma = 2.0
with CallGraph(ct, mdnm, pth, 'bpdnprjl1_init.svg', **kwargs):
b = bpdn.BPDNProjL1(D, s, gamma, opt)
with CallGraph(ct, mdnm, pth, 'bpdnprjl1_solve.svg', **kwargs):
b.solve()
## MinL1InL2Ball class
opt = bpdn.MinL1InL2Ball.Options({'Verbose': False, 'MaxMainIter': 1})
epsilon = 1.0
with CallGraph(ct, mdnm, pth, 'bpdnml1l2_init.svg', **kwargs):
b = bpdn.MinL1InL2Ball(D, s, epsilon, opt)
with CallGraph(ct, mdnm, pth, 'bpdnml1l2_solve.svg', **kwargs):
b.solve()
#### cbpdn module
from sporco.admm import cbpdn
mdnm = 'sporco.admm.cbpdn'
D = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8)
lmbda = 0.1
## ConvBPDN class
opt = cbpdn.ConvBPDN.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cbpdn_init.svg', **kwargs):
b = cbpdn.ConvBPDN(D, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'cbpdn_solve.svg', **kwargs):
b.solve()
## ConvBPDNJoint class
opt = cbpdn.ConvBPDNJoint.Options({'Verbose': False, 'MaxMainIter': 1})
mu = 0.01
with CallGraph(ct, mdnm, pth, 'cbpdnjnt_init.svg', **kwargs):
b = cbpdn.ConvBPDNJoint(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnjnt_solve.svg', **kwargs):
b.solve()
## ConvElasticNet class
opt = cbpdn.ConvElasticNet.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'celnet_init.svg', **kwargs):
b = cbpdn.ConvElasticNet(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'celnet_solve.svg', **kwargs):
b.solve()
## ConvBPDNGradReg class
opt = cbpdn.ConvBPDNGradReg.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cbpdngrd_init.svg', **kwargs):
b = cbpdn.ConvBPDNGradReg(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdngrd_solve.svg', **kwargs):
b.solve()
## ConvBPDNProjL1 class
opt = cbpdn.ConvBPDNProjL1.Options({'Verbose': False, 'MaxMainIter': 1})
gamma = 0.5
with CallGraph(ct, mdnm, pth, 'cbpdnprjl1_init.svg', **kwargs):
b = cbpdn.ConvBPDNProjL1(D, s, gamma, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnprjl1_solve.svg', **kwargs):
b.solve()
## ConvMinL1InL2Ball class
opt = cbpdn.ConvMinL1InL2Ball.Options({'Verbose': False, 'MaxMainIter': 1})
epsilon = 0.5
with CallGraph(ct, mdnm, pth, 'cbpdnml1l2_init.svg', **kwargs):
b = cbpdn.ConvMinL1InL2Ball(D, s, epsilon, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnml1l2_solve.svg', **kwargs):
b.solve()
## ConvBPDNMaskDcpl class
opt = cbpdn.ConvBPDNMaskDcpl.Options({'Verbose': False, 'MaxMainIter': 1})
W = np.ones(s.shape)
with CallGraph(ct, mdnm, pth, 'cbpdnmd_init.svg', **kwargs):
b = cbpdn.ConvBPDNMaskDcpl(D, s, lmbda, W, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnmd_solve.svg', **kwargs):
b.solve()
## ConvL1L1Grd class
opt = cbpdn.ConvL1L1Grd.Options({'Verbose': False, 'MaxMainIter': 1})
mu = 1e-2
with CallGraph(ct, mdnm, pth, 'cl1l1grd_init.svg', **kwargs):
b = cbpdn.ConvL1L1Grd(D, s, lmbda, mu, W, opt)
with CallGraph(ct, mdnm, pth, 'cl1l1grd_solve.svg', **kwargs):
b.solve()
#### cbpdntv module
from sporco.admm import cbpdntv
mdnm = 'sporco.admm.cbpdntv'
D = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8)
lmbda = 0.1
mu = 0.01
## ConvBPDNScalarTV class
opt = cbpdntv.ConvBPDNScalarTV.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'cbpdnstv_init.svg', **kwargs):
b = cbpdntv.ConvBPDNScalarTV(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnstv_solve.svg', **kwargs):
b.solve()
## ConvBPDNVectorTV class
opt = cbpdntv.ConvBPDNVectorTV.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'cbpdnvtv_init.svg', **kwargs):
b = cbpdntv.ConvBPDNVectorTV(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnvtv_solve.svg', **kwargs):
b.solve()
## ConvBPDNRecTV class
opt = cbpdntv.ConvBPDNRecTV.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cbpdnrtv_init.svg', **kwargs):
b = cbpdntv.ConvBPDNRecTV(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnrtv_solve.svg', **kwargs):
b.solve()
#### cbpdnin module
from sporco.admm import cbpdnin
mdnm = 'sporco.admm.cbpdnin'
D = np.random.randn(4, 4, 32)
s = np.random.randn(8, 8)
lmbda = 0.1
mu = 0.01
Wg = np.append(np.eye(16), np.eye(16), axis=-1)
## ConvBPDNInhib class
opt = cbpdnin.ConvBPDNInhib.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cbpdnin_init.svg', **kwargs):
b = cbpdnin.ConvBPDNInhib(D,
s,
Wg,
Whn=4,
lmbda=lmbda,
mu=mu,
gamma=None,
opt=opt)
with CallGraph(ct, mdnm, pth, 'cbpdnin_solve.svg', **kwargs):
b.solve()
#### cmod module
from sporco.admm import cmod
mdnm = 'sporco.admm.cmod'
X = np.random.randn(8, 16)
S = np.random.randn(8, 16)
## CnstrMOD class
opt = cmod.CnstrMOD.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cmod_init.svg', **kwargs):
b = cmod.CnstrMOD(X, S, opt=opt)
with CallGraph(ct, mdnm, pth, 'cmod_solve.svg', **kwargs):
b.solve()
#### ccmod module
from sporco.admm import ccmod
mdnm = 'sporco.admm.ccmod'
X = np.random.randn(8, 8, 1, 2, 1)
S = np.random.randn(8, 8, 2)
dsz = (4, 4, 1)
## ConvCnstrMOD_IterSM class
opt = ccmod.ConvCnstrMOD_IterSM.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodism_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD_IterSM(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodism_solve.svg', **kwargs):
b.solve()
## ConvCnstrMOD_CG class
opt = ccmod.ConvCnstrMOD_CG.Options({
'Verbose': False,
'MaxMainIter': 1,
'CG': {
'MaxIter': 1
}
})
with CallGraph(ct, mdnm, pth, 'ccmodcg_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD_CG(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodcg_solve.svg', **kwargs):
b.solve()
## ConvCnstrMOD_Consensus class
opt = ccmod.ConvCnstrMOD_Consensus.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodcnsns_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD_Consensus(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodcnsns_solve.svg', **kwargs):
b.solve()
#### ccmodmd module
from sporco.admm import ccmodmd
mdnm = 'sporco.admm.ccmodmd'
X = np.random.randn(8, 8, 1, 2, 1)
S = np.random.randn(8, 8, 2)
W = np.array([1.0])
dsz = (4, 4, 1)
## ConvCnstrMODMaskDcpl_IterSM class
opt = ccmodmd.ConvCnstrMODMaskDcpl_IterSM.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodmdism_init.svg', **kwargs):
b = ccmodmd.ConvCnstrMODMaskDcpl_IterSM(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdism_solve.svg', **kwargs):
b.solve()
## ConvCnstrMODMaskDcpl_CG class
opt = ccmodmd.ConvCnstrMODMaskDcpl_CG.Options({
'Verbose': False,
'MaxMainIter': 1,
'CG': {
'MaxIter': 1
}
})
with CallGraph(ct, mdnm, pth, 'ccmodmdcg_init.svg', **kwargs):
b = ccmodmd.ConvCnstrMODMaskDcpl_CG(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdcg_solve.svg', **kwargs):
b.solve()
## ConvCnstrMODMaskDcpl_Consensus class
opt = ccmodmd.ConvCnstrMODMaskDcpl_Consensus.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodmdcnsns_init.svg', **kwargs):
b = ccmodmd.ConvCnstrMODMaskDcpl_Consensus(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdcnsns_solve.svg', **kwargs):
b.solve()
#### tvl1 module
from sporco.admm import tvl1
mdnm = 'sporco.admm.tvl1'
s = np.random.randn(16, 16)
lmbda = 0.1
## TVL1Denoise class
opt = tvl1.TVL1Denoise.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'tvl1den_init.svg', **kwargs):
b = tvl1.TVL1Denoise(s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl1den_solve.svg', **kwargs):
b.solve()
## TVL1Deconv class
opt = tvl1.TVL1Deconv.Options({'Verbose': False, 'MaxMainIter': 1})
h = np.random.randn(3, 3)
with CallGraph(ct, mdnm, pth, 'tvl1dcn_init.svg', **kwargs):
b = tvl1.TVL1Deconv(h, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl1dcn_solve.svg', **kwargs):
b.solve()
#### tvl2 module
from sporco.admm import tvl2
mdnm = 'sporco.admm.tvl2'
s = np.random.randn(16, 16)
lmbda = 0.1
## TVL2Denoise class
opt = tvl2.TVL2Denoise.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'tvl2den_init.svg', **kwargs):
b = tvl2.TVL2Denoise(s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl2den_solve.svg', **kwargs):
b.solve()
## TVL2Deconv class
opt = tvl2.TVL2Deconv.Options({'Verbose': False, 'MaxMainIter': 1})
h = np.random.randn(3, 3)
with CallGraph(ct, mdnm, pth, 'tvl2dcn_init.svg', **kwargs):
b = tvl2.TVL2Deconv(h, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl2dcn_solve.svg', **kwargs):
b.solve()
srcmodflt = '^sporco.fista'
fnmsub = ('^sporco.fista.', '')
lnksub = (r'^([^\.]*).([^\.]*)(?:(.__init__|.__call__)|(.[^\.]*))',
lnkpfx + r'sporco.fista.\1.html#sporco.fista.\1.\2\4')
ct = jonga.CallTracer(srcmodflt=srcmodflt,
srcqnmflt=srcqnmflt,
dstqnmflt=dstqnmflt,
fnmsub=fnmsub,
grpflt=grpflt,
lnksub=lnksub)
#### fista.cbpdn module
from sporco.fista import cbpdn
mdnm = 'sporco.fista.cbpdn'
D = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8)
lmbda = 0.1
## ConvBPDN class
opt = cbpdn.ConvBPDN.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'fista_cbpdn_init.svg', **kwargs):
b = cbpdn.ConvBPDN(D, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'fista_cbpdn_solve.svg', **kwargs):
b.solve()
#### fista.ccmod module
from sporco.fista import ccmod
mdnm = 'sporco.fista.ccmod'
X = np.random.randn(8, 8, 1, 2, 1)
S = np.random.randn(8, 8, 2)
dsz = (4, 4, 1)
## ConvCnstrMOD class
opt = ccmod.ConvCnstrMOD.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'ccmodfista_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodfista_solve.svg', **kwargs):
b.solve()
## ConvCnstrMODMask class
opt = ccmod.ConvCnstrMODMask.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'ccmodmdfista_init.svg', **kwargs):
b = ccmod.ConvCnstrMODMask(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdfista_solve.svg', **kwargs):
b.solve()
srcmodflt = '^sporco.dictlrn'
fnmsub = ('^sporco.dictlrn.', '')
lnksub = (r'^([^\.]*).([^\.]*)(?:(.__init__|.__call__)|(.[^\.]*))',
lnkpfx + r'sporco.dictlrn.\1.html#sporco.dictlrn.\1.\2\4')
ct = jonga.CallTracer(srcmodflt=srcmodflt,
srcqnmflt=srcqnmflt,
dstqnmflt=dstqnmflt,
fnmsub=fnmsub,
grpflt=grpflt,
lnksub=lnksub)
#### bpdndl module
from sporco.dictlrn import bpdndl
mdnm = 'sporco.dictlrn.bpdndl'
D0 = np.random.randn(8, 8)
S = np.random.randn(8, 16)
lmbda = 0.1
## BPDNDictLearn class
opt = bpdndl.BPDNDictLearn.Options({
'Verbose': False,
'MaxMainIter': 1,
'AccurateDFid': True
})
with CallGraph(ct, mdnm, pth, 'bpdndl_init.svg', **kwargs):
b = bpdndl.BPDNDictLearn(D0, S, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'bpdndl_solve.svg', **kwargs):
b.solve()
#### cbpdndl module
from sporco.dictlrn import cbpdndl
mdnm = 'sporco.dictlrn.cbpdndl'
D0 = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8, 10)
lmbda = 0.1
## ConvBPDNDictLearn class
opt = cbpdndl.ConvBPDNDictLearn.Options({
'Verbose': False,
'MaxMainIter': 1,
'AccurateDFid': True
})
with CallGraph(ct, mdnm, pth, 'cbpdndl_init.svg', **kwargs):
b = cbpdndl.ConvBPDNDictLearn(D0, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'cbpdndl_solve.svg', **kwargs):
b.solve()
#### cbpdndlmd module
from sporco.dictlrn import cbpdndlmd
mdnm = 'sporco.dictlrn.cbpdndlmd'
## ConvBPDNMaskDcplDictLearn class
W = np.array([1.0])
opt = cbpdndlmd.ConvBPDNMaskDictLearn.Options({
'Verbose': False,
'MaxMainIter': 1,
'AccurateDFid': True
})
with CallGraph(ct, mdnm, pth, 'cbpdnmddl_init.svg', **kwargs):
b = cbpdndlmd.ConvBPDNMaskDictLearn(D0, s, lmbda, W, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnmddl_solve.svg', **kwargs):
b.solve()