def test_18(self):
N = 64
M = 4
Nd = 8
D0 = cr.normalise(cr.zeromean(np.random.randn(Nd, Nd, M), (Nd, Nd, M),
dimN=2),
dimN=2)
X = np.zeros((N, N, M))
xr = np.random.randn(N, N, M)
xp = np.abs(xr) > 3
X[xp] = np.random.randn(X[xp].size)
S = np.sum(ifftn(
fftn(D0, (N, N), (0, 1)) * fftn(X, None, (0, 1)), None,
(0, 1)).real,
axis=2)
L = 50.0
opt = ccmod.ConvCnstrMOD.Options({
'Verbose': False,
'MaxMainIter': 3000,
'ZeroMean': True,
'RelStopTol': 0.,
'L': L,
'Monotone': True
})
Xr = X.reshape(X.shape[0:2] + (
1,
1,
) + X.shape[2:])
Sr = S.reshape(S.shape + (1, ))
c = ccmod.ConvCnstrMOD(Xr, Sr, D0.shape, opt)
c.solve()
D1 = cr.bcrop(c.X, D0.shape).squeeze()
assert rrs(D0, D1) < 1e-4
assert np.array(c.getitstat().Rsdl)[-1] < 1e-5
def test_02(self):
N = 16
M = 8
X = np.random.randn(N, N, 1, 1, M)
S = np.random.randn(N, N, 1)
try:
c = ccmod.ConvCnstrMOD(X, S, ((4, 4, 4), (8, 8, 4)))
c.solve()
except Exception as e:
print(e)
assert 0
def test_01(self):
N = 16
M = 4
Nd = 8
X = np.random.randn(N, N, 1, 1, M)
S = np.random.randn(N, N, 1)
try:
c = ccmod.ConvCnstrMOD(X, S, (Nd, Nd, M))
c.solve()
except Exception as e:
print(e)
assert 0
def test_06(self):
N = 16
M = 4
Nd = 8
X = np.random.randn(N, N, 1, 1, M)
S = np.random.randn(N, N)
try:
opt = ccmod.ConvCnstrMOD.Options({
'Verbose': False,
'MaxMainIter': 20
})
c = ccmod.ConvCnstrMOD(X, S, (Nd, Nd, 1, M), opt=opt, dimK=0)
c.solve()
except Exception as e:
print(e)
assert 0
def test_05(self):
N = 16
M = 4
Nd = 8
X = np.random.randn(N, N, 1, 1, M)
S = np.random.randn(N, N, 1)
dt = np.float64
opt = ccmod.ConvCnstrMOD.Options({
'Verbose': False,
'MaxMainIter': 20,
'Backtrack': BacktrackStandard(),
'DataType': dt
})
c = ccmod.ConvCnstrMOD(X, S, (Nd, Nd, M), opt=opt)
c.solve()
assert c.X.dtype == dt
assert c.Xf.dtype == complex_dtype(dt)
assert c.Yf.dtype == complex_dtype(dt)
def test_10(self):
N = 16
M = 4
K = 2
Nc = 3
Nd = 8
X = np.random.randn(N, N, Nc, K, M)
S = np.random.randn(N, N, Nc, K)
try:
opt = ccmod.ConvCnstrMOD.Options({
'Verbose': False,
'MaxMainIter': 20
})
c = ccmod.ConvCnstrMOD(X, S, (Nd, Nd, Nc, M), opt=opt)
c.solve()
except Exception as e:
print(e)
assert 0
def test_03(self):
N = 16
M = 4
Nc = 3
Nd = 8
X = np.random.randn(N, N, Nc, 1, M)
S = np.random.randn(N, N, Nc)
L = 2e3
try:
opt = ccmod.ConvCnstrMOD.Options({
'Verbose': False,
'MaxMainIter': 100,
'L': L
})
c = ccmod.ConvCnstrMOD(X, S, (Nd, Nd, 1, M), opt=opt, dimK=0)
c.solve()
except Exception as e:
print(e)
assert 0
assert np.array(c.getitstat().Rsdl)[-1] < 5e-3
'Verbose': True,
'MaxMainIter': 100,
'HighMemSolve': True
})
c = cbpdn.ConvBPDN(D0, sh, lmbda, opt)
X = c.solve()
"""
Update dictionary for training image set. Nesterov momentum coefficients :cite:`beck-2009-fast`.
"""
opt = ccmod.ConvCnstrMOD.Options({
'Verbose': True,
'MaxMainIter': 100,
'L': 50
})
c1 = ccmod.ConvCnstrMOD(X, sh, D0.shape, opt)
c1.solve()
D11 = c1.getdict().squeeze()
print("ConvCnstrMOD solve time: %.2fs" % c1.timer.elapsed('solve'))
"""
Update dictionary for training image set. Linear momentum coefficients :cite:`chambolle-2015-convergence`.
"""
opt = ccmod.ConvCnstrMOD.Options({
'Verbose': True,
'MaxMainIter': 100,
'Momentum': MomentumLinear(),
'L': 50
})
c2 = ccmod.ConvCnstrMOD(X, sh, D0.shape, opt)
c2.solve()