diff --git a/demos/Wiener_Filter.ipynb b/demos/Wiener_Filter.ipynb
index 19074536831d2c9912584097283a5d3beb05409f..59f046d5b8d9c91bd4430f6a8b5049dd68506d6b 100644
--- a/demos/Wiener_Filter.ipynb
+++ b/demos/Wiener_Filter.ipynb
@@ -169,10 +169,9 @@
"def Curvature(R, N, Sh):\n",
" IC = ift.GradientNormController(iteration_limit=50000,\n",
" tol_abs_gradnorm=0.1)\n",
- " inverter = ift.ConjugateGradient(controller=IC)\n",
" # WienerFilterCurvature is (R.adjoint*N.inverse*R + Sh.inverse) plus some handy\n",
" # helper methods.\n",
- " return ift.library.WienerFilterCurvature(R,N,Sh,inverter, sampling_inverter=inverter)"
+ " return ift.library.WienerFilterCurvature(R,N,Sh,iteration_controller=IC)"
]
},
{
diff --git a/demos/critical_filtering.py b/demos/critical_filtering.py
index d859ab895223e110139e4d92d6c42c350e124092..51bcc81b4182b03feb2f3e499410ce97bda64a75 100644
--- a/demos/critical_filtering.py
+++ b/demos/critical_filtering.py
@@ -85,15 +85,13 @@ if __name__ == "__main__":
LS = ift.LineSearchStrongWolfe(c2=0.02)
minimizer = ift.RelaxedNewton(IC1, line_searcher=LS)
- ICI = ift.GradientNormController(iteration_limit=500,
- tol_abs_gradnorm=1e-3)
- inverter = ift.ConjugateGradient(controller=ICI)
+ IC = ift.GradientNormController(iteration_limit=500,
+ tol_abs_gradnorm=1e-3)
for i in range(20):
power0 = Distributor(ift.exp(0.5*t0))
map0_energy = ift.library.NonlinearWienerFilterEnergy(
- m0, d, MeasurementOperator, nonlinearity, HT, power0, N, S,
- inverter=inverter)
+ m0, d, MeasurementOperator, nonlinearity, HT, power0, N, S, IC)
# Minimization with chosen minimizer
map0_energy, convergence = minimizer(map0_energy)
@@ -106,7 +104,8 @@ if __name__ == "__main__":
power0_energy = ift.library.NonlinearPowerEnergy(
position=t0, d=d, N=N, xi=m0, D=D0, ht=HT,
Instrument=MeasurementOperator, nonlinearity=nonlinearity,
- Distributor=Distributor, sigma=1., samples=2, inverter=inverter)
+ Distributor=Distributor, sigma=1., samples=2,
+ iteration_controller=IC)
power0_energy = minimizer(power0_energy)[0]
diff --git a/demos/nonlinear_critical_filter.py b/demos/nonlinear_critical_filter.py
index 8df6da0aa76c204180148cdfb0bbd042aa311ac6..8aee9c6407b44aa89a0b43726b4395243f4ec49a 100644
--- a/demos/nonlinear_critical_filter.py
+++ b/demos/nonlinear_critical_filter.py
@@ -78,15 +78,13 @@ if __name__ == "__main__":
LS = ift.LineSearchStrongWolfe(c2=0.02)
minimizer = ift.RelaxedNewton(IC1, line_searcher=LS)
- ICI = ift.GradientNormController(iteration_limit=500,
- tol_abs_gradnorm=1e-3)
- inverter = ift.ConjugateGradient(controller=ICI)
+ IC = ift.GradientNormController(iteration_limit=500,
+ tol_abs_gradnorm=1e-3)
for i in range(20):
power0 = Distributor(ift.exp(0.5*t0))
map0_energy = ift.library.NonlinearWienerFilterEnergy(
- m0, d, MeasurementOperator, nonlinearity, HT, power0, N, S,
- inverter=inverter)
+ m0, d, MeasurementOperator, nonlinearity, HT, power0, N, S, IC)
# Minimization with chosen minimizer
map0_energy, convergence = minimizer(map0_energy)
@@ -99,7 +97,7 @@ if __name__ == "__main__":
power0_energy = ift.library.NonlinearPowerEnergy(
position=t0, d=d, N=N, xi=m0, D=D0, ht=HT,
Instrument=MeasurementOperator, nonlinearity=nonlinearity,
- Distributor=Distributor, sigma=1., samples=2, inverter=inverter)
+ Distributor=Distributor, sigma=1., samples=2, iteration_controller=IC)
power0_energy = minimizer(power0_energy)[0]
diff --git a/demos/nonlinear_wiener_filter.py b/demos/nonlinear_wiener_filter.py
index 3004b5a010507fda04200db3679ea6888b3f5850..01c2ea0f98ceba456b0421c9e7a66911801d9ae3 100644
--- a/demos/nonlinear_wiener_filter.py
+++ b/demos/nonlinear_wiener_filter.py
@@ -52,14 +52,13 @@ if __name__ == "__main__":
LS = ift.LineSearchStrongWolfe(c2=0.02)
minimizer = ift.RelaxedNewton(IC1, line_searcher=LS)
- ICI = ift.GradientNormController(iteration_limit=2000,
- tol_abs_gradnorm=1e-3)
- inverter = ift.ConjugateGradient(controller=ICI)
+ IC = ift.GradientNormController(iteration_limit=2000,
+ tol_abs_gradnorm=1e-3)
# initial guess
m = ift.full(h_space, 1e-7)
map_energy = ift.library.NonlinearWienerFilterEnergy(
- m, d, R, nonlinearity, HT, power, N, S, inverter=inverter)
+ m, d, R, nonlinearity, HT, power, N, S, IC)
# Minimization with chosen minimizer
map_energy, convergence = minimizer(map_energy)
diff --git a/demos/paper_demos/cartesian_wiener_filter.py b/demos/paper_demos/cartesian_wiener_filter.py
index 2cec36eb05993dd208b188d88d0617292fd46240..cf851e1327910601769496ee6ffc6ee713195afd 100644
--- a/demos/paper_demos/cartesian_wiener_filter.py
+++ b/demos/paper_demos/cartesian_wiener_filter.py
@@ -76,10 +76,9 @@ if __name__ == "__main__":
ctrl = ift.GradientNormController(name="inverter", tol_abs_gradnorm=0.1)
sampling_ctrl = ift.GradientNormController(name="sampling",
tol_abs_gradnorm=1e2)
- inverter = ift.ConjugateGradient(controller=ctrl)
- sampling_inverter = ift.ConjugateGradient(controller=sampling_ctrl)
wiener_curvature = ift.library.WienerFilterCurvature(
- S=S, N=N, R=R, inverter=inverter, sampling_inverter=sampling_inverter)
+ S=S, N=N, R=R, iteration_controller=ctrl,
+ iteration_controller_sampling=sampling_ctrl)
m_k = wiener_curvature.inverse_times(j)
m = ht(m_k)
diff --git a/demos/paper_demos/wiener_filter.py b/demos/paper_demos/wiener_filter.py
index 59f29ddb320cba87e061b799a4585221b7a9d6cd..e9f199d2ab35acbab626904434879fd4ec9d9a33 100644
--- a/demos/paper_demos/wiener_filter.py
+++ b/demos/paper_demos/wiener_filter.py
@@ -50,10 +50,9 @@ if __name__ == "__main__":
ctrl = ift.GradientNormController(name="inverter", tol_abs_gradnorm=1e-2)
sampling_ctrl = ift.GradientNormController(name="sampling",
tol_abs_gradnorm=2e1)
- inverter = ift.ConjugateGradient(controller=ctrl)
- sampling_inverter = ift.ConjugateGradient(controller=sampling_ctrl)
wiener_curvature = ift.library.WienerFilterCurvature(
- S=S, N=N, R=R, inverter=inverter, sampling_inverter=sampling_inverter)
+ S=S, N=N, R=R, iteration_controller=ctrl,
+ iteration_controller_sampling=sampling_ctrl)
m_k = wiener_curvature.inverse_times(j)
m = ht(m_k)
diff --git a/demos/poisson_demo.py b/demos/poisson_demo.py
index 26bff8186e0bda319a31554937323f450d51f66d..64a6e0ef52b41561e72a1702cd0288b372204ab3 100644
--- a/demos/poisson_demo.py
+++ b/demos/poisson_demo.py
@@ -81,9 +81,8 @@ if __name__ == "__main__":
j = R.adjoint_times(N.inverse_times(d))
IC = ift.GradientNormController(name="inverter", iteration_limit=500,
tol_abs_gradnorm=1e-3)
- inverter = ift.ConjugateGradient(controller=IC)
D = (ift.SandwichOperator.make(R, N.inverse) + Phi_h.inverse).inverse
- D = ift.InversionEnabler(D, inverter, approximation=Phi_h)
+ D = ift.InversionEnabler(D, IC, approximation=Phi_h)
m = HT(D(j))
# Uncertainty
@@ -116,8 +115,7 @@ if __name__ == "__main__":
# initial guess
psi0 = ift.full(h_domain, 1e-7)
- energy = ift.library.PoissonEnergy(psi0, data, R0, nonlin, HT, Phi_h,
- inverter)
+ energy = ift.library.PoissonEnergy(psi0, data, R0, nonlin, HT, Phi_h, IC)
IC1 = ift.GradientNormController(name="IC1", iteration_limit=200,
tol_abs_gradnorm=1e-4)
minimizer = ift.RelaxedNewton(IC1)
diff --git a/demos/sampling.py b/demos/sampling.py
index 43ef9d1eabe15d102935bd271cda59b53f81fa0f..21f0d2e86fbf3901296070866e05bbd4ea5b4f84 100644
--- a/demos/sampling.py
+++ b/demos/sampling.py
@@ -39,17 +39,17 @@ N_iter = 100
tol = 1e-3
IC = ift.GradientNormController(tol_abs_gradnorm=tol, iteration_limit=N_iter)
-inverter = ift.ConjugateGradient(IC)
-curv = ift.library.WienerFilterCurvature(S=S, N=N, R=R_p, inverter=inverter,
- sampling_inverter=inverter)
+curv = ift.library.WienerFilterCurvature(S=S, N=N, R=R_p,
+ iteration_controller=IC,
+ iteration_controller_sampling=IC)
m_xi = curv.inverse_times(j)
samps_long = [curv.draw_sample(from_inverse=True) for i in range(N_samps)]
tol = 1e2
IC = ift.GradientNormController(tol_abs_gradnorm=tol, iteration_limit=N_iter)
-inverter = ift.ConjugateGradient(IC)
-curv = ift.library.WienerFilterCurvature(S=S, N=N, R=R_p, inverter=inverter,
- sampling_inverter=inverter)
+curv = ift.library.WienerFilterCurvature(S=S, N=N, R=R_p,
+ iteration_controller=IC,
+ iteration_controller_sampling=IC)
samps_short = [curv.draw_sample(from_inverse=True) for i in range(N_samps)]
# Compute mean
diff --git a/demos/tomography.py b/demos/tomography.py
index b2e3cffdef65d629c9bd66194cae8fa01b7d34bc..3957e99c20405fa89740c7108a4022c04937a5d7 100644
--- a/demos/tomography.py
+++ b/demos/tomography.py
@@ -38,8 +38,8 @@ if __name__ == "__main__":
j = Rh.adjoint_times(N.inverse_times(d))
ctrl = ift.GradientNormController(name="Iter", tol_abs_gradnorm=1e-10,
iteration_limit=300)
- inverter = ift.ConjugateGradient(controller=ctrl)
- Di = ift.library.WienerFilterCurvature(S=S, R=Rh, N=N, inverter=inverter)
+ Di = ift.library.WienerFilterCurvature(S=S, R=Rh, N=N,
+ iteration_controller=ctrl)
mh = Di.inverse_times(j)
m = ht(mh)
diff --git a/demos/wiener_filter_data_space_noiseless.py b/demos/wiener_filter_data_space_noiseless.py
index a8911d546dd9de62b13151b47a6418e24461032d..9310f62660c99b631ef69e934dd5ca55f616c1a7 100644
--- a/demos/wiener_filter_data_space_noiseless.py
+++ b/demos/wiener_filter_data_space_noiseless.py
@@ -98,10 +98,9 @@ if __name__ == "__main__":
IC = ift.GradientNormController(name="inverter", iteration_limit=1000,
tol_abs_gradnorm=0.0001)
- inverter = ift.ConjugateGradient(controller=IC)
# setting up measurement precision matrix M
M = (ift.SandwichOperator.make(R.adjoint, Sh) + N)
- M = ift.InversionEnabler(M, inverter)
+ M = ift.InversionEnabler(M, IC)
m = Sh(R.adjoint(M.inverse_times(d)))
# Plotting
diff --git a/demos/wiener_filter_easy.py b/demos/wiener_filter_easy.py
index 3631c29922fb9e96f3f7a4cf993e012008a3ace1..49749fe957e86e2efa9a84cfd4330999b6ad1e3a 100644
--- a/demos/wiener_filter_easy.py
+++ b/demos/wiener_filter_easy.py
@@ -52,9 +52,8 @@ if __name__ == "__main__":
j = R.adjoint_times(N.inverse_times(d))
IC = ift.GradientNormController(name="inverter", iteration_limit=500,
tol_abs_gradnorm=0.1)
- inverter = ift.ConjugateGradient(controller=IC)
D = (ift.SandwichOperator.make(R, N.inverse) + Sh.inverse).inverse
- D = ift.InversionEnabler(D, inverter, approximation=Sh)
+ D = ift.InversionEnabler(D, IC, approximation=Sh)
m = D(j)
# Plotting
diff --git a/demos/wiener_filter_via_curvature.py b/demos/wiener_filter_via_curvature.py
index 5717120578a46ba9310639c67e0bd2a2fb4ff99f..d83553e86d02e040551a4db76c97c49b3eb1439d 100644
--- a/demos/wiener_filter_via_curvature.py
+++ b/demos/wiener_filter_via_curvature.py
@@ -78,9 +78,8 @@ if __name__ == "__main__":
j = R.adjoint_times(N.inverse_times(data))
ctrl = ift.GradientNormController(
name="inverter", tol_abs_gradnorm=1e-5/(nu.K*(nu.m**dimensionality)))
- inverter = ift.ConjugateGradient(controller=ctrl)
- wiener_curvature = ift.library.WienerFilterCurvature(
- S=S, N=N, R=R, inverter=inverter)
+ wiener_curvature = ift.library.WienerFilterCurvature(S=S, N=N, R=R,
+ iteration_controller=ctrl)
m = wiener_curvature.inverse_times(j)
m_s = HT(m)
diff --git a/demos/wiener_filter_via_hamiltonian.py b/demos/wiener_filter_via_hamiltonian.py
index a63eb3635a1e2ea6eb5c151a2f8a1f66cbb385fd..bc2d2a37badddeec6d7e1cc6b75800ad35f2d679 100644
--- a/demos/wiener_filter_via_hamiltonian.py
+++ b/demos/wiener_filter_via_hamiltonian.py
@@ -47,15 +47,14 @@ if __name__ == "__main__":
# Choose minimization strategy
ctrl = ift.GradientNormController(name="inverter", tol_abs_gradnorm=0.1)
- inverter = ift.ConjugateGradient(controller=ctrl)
controller = ift.GradientNormController(name="min", tol_abs_gradnorm=0.1)
minimizer = ift.RelaxedNewton(controller=controller)
m0 = ift.full(h_space, 0.)
# Initialize Wiener filter energy
energy = ift.library.WienerFilterEnergy(position=m0, d=d, R=R, N=N, S=S,
- inverter=inverter,
- sampling_inverter=inverter)
+ iteration_controller=ctrl,
+ iteration_controller_sampling=ctrl)
energy, convergence = minimizer(energy)
m = energy.position
diff --git a/nifty5/library/nonlinear_power_energy.py b/nifty5/library/nonlinear_power_energy.py
index 5d28126d963253b1fa7e4b60d24b6a8cce68ec6c..65ca8ebec04fc7bace95692fd1981b62926416a4 100644
--- a/nifty5/library/nonlinear_power_energy.py
+++ b/nifty5/library/nonlinear_power_energy.py
@@ -63,7 +63,7 @@ class NonlinearPowerEnergy(Energy):
# MR FIXME: docstring incomplete and outdated
def __init__(self, position, d, N, xi, D, ht, Instrument, nonlinearity,
Distributor, sigma=0., samples=3, xi_sample_list=None,
- inverter=None):
+ iteration_controller=None):
super(NonlinearPowerEnergy, self).__init__(position)
self.xi = xi
self.D = D
@@ -83,7 +83,7 @@ class NonlinearPowerEnergy(Energy):
xi_sample_list = [D.draw_sample(from_inverse=True) + xi
for _ in range(samples)]
self.xi_sample_list = xi_sample_list
- self.inverter = inverter
+ self._ic = iteration_controller
A = Distributor(exp(.5 * position))
@@ -118,7 +118,7 @@ class NonlinearPowerEnergy(Energy):
self.Distributor, sigma=self.sigma,
samples=len(self.xi_sample_list),
xi_sample_list=self.xi_sample_list,
- inverter=self.inverter)
+ iteration_controller=self._ic)
@property
def value(self):
@@ -139,4 +139,4 @@ class NonlinearPowerEnergy(Energy):
op = LinearizedResponse.adjoint*self.N.inverse*LinearizedResponse
result = op if result is None else result + op
result = result*(1./len(self.xi_sample_list)) + self.T
- return InversionEnabler(result, self.inverter)
+ return InversionEnabler(result, self._ic)
diff --git a/nifty5/library/nonlinear_wiener_filter_energy.py b/nifty5/library/nonlinear_wiener_filter_energy.py
index c0ecf3ef5288e43ba62a1f58d4a530a117718b75..def3a90fa23019ba141789651efa3c05479e7ad2 100644
--- a/nifty5/library/nonlinear_wiener_filter_energy.py
+++ b/nifty5/library/nonlinear_wiener_filter_energy.py
@@ -24,8 +24,7 @@ from ..sugar import makeOp
class NonlinearWienerFilterEnergy(Energy):
def __init__(self, position, d, Instrument, nonlinearity, ht, power, N, S,
- inverter=None,
- sampling_inverter=None):
+ iteration_controller=None, iteration_controller_sampling=None):
super(NonlinearWienerFilterEnergy, self).__init__(position=position)
self.d = d.lock()
self.Instrument = Instrument
@@ -37,10 +36,10 @@ class NonlinearWienerFilterEnergy(Energy):
residual = d - Instrument(nonlinearity(m))
self.N = N
self.S = S
- self.inverter = inverter
- if sampling_inverter is None:
- sampling_inverter = inverter
- self.sampling_inverter = sampling_inverter
+ self._ic = iteration_controller
+ if iteration_controller_sampling is None:
+ iteration_controller_sampling = self._ic
+ self._ic_samp = iteration_controller_sampling
t1 = S.inverse_times(position)
t2 = N.inverse_times(residual)
self._value = 0.5 * (position.vdot(t1) + residual.vdot(t2)).real
@@ -51,7 +50,7 @@ class NonlinearWienerFilterEnergy(Energy):
def at(self, position):
return self.__class__(position, self.d, self.Instrument,
self.nonlinearity, self.ht, self.power, self.N,
- self.S, self.inverter)
+ self.S, self._ic, self._ic_samp)
@property
def value(self):
@@ -64,5 +63,5 @@ class NonlinearWienerFilterEnergy(Energy):
@property
@memo
def curvature(self):
- return WienerFilterCurvature(self.R, self.N, self.S, self.inverter,
- self.sampling_inverter)
+ return WienerFilterCurvature(self.R, self.N, self.S, self._ic,
+ self._ic_samp)
diff --git a/nifty5/library/poisson_energy.py b/nifty5/library/poisson_energy.py
index f3ea413161ade66cd77ba28c2ad887c7d23f13ed..5705717461abae0fdc003644b989b486af88828a 100644
--- a/nifty5/library/poisson_energy.py
+++ b/nifty5/library/poisson_energy.py
@@ -25,9 +25,9 @@ from ..sugar import log
class PoissonEnergy(Energy):
def __init__(self, position, d, Instrument, nonlinearity, ht, Phi_h,
- inverter=None):
+ iteration_controller=None):
super(PoissonEnergy, self).__init__(position=position)
- self._inverter = inverter
+ self._ic = iteration_controller
self._d = d
self._Instrument = Instrument
self._nonlinearity = nonlinearity
@@ -51,7 +51,7 @@ class PoissonEnergy(Energy):
def at(self, position):
return self.__class__(position, self._d, self._Instrument,
self._nonlinearity, self._ht, self._Phi_h,
- self._inverter)
+ self._ic)
@property
def value(self):
@@ -63,5 +63,4 @@ class PoissonEnergy(Energy):
@property
def curvature(self):
- return InversionEnabler(self._curv, self._inverter,
- approximation=self._Phi_h.inverse)
+ return InversionEnabler(self._curv, self._ic, self._Phi_h.inverse)
diff --git a/nifty5/library/wiener_filter_curvature.py b/nifty5/library/wiener_filter_curvature.py
index eeed8d7a7878abddbfdb4e90b548b2cac8fc27f2..4ff71df6e20f75613c067c0590051fe0d09d3457 100644
--- a/nifty5/library/wiener_filter_curvature.py
+++ b/nifty5/library/wiener_filter_curvature.py
@@ -21,7 +21,8 @@ from ..operators.inversion_enabler import InversionEnabler
from ..operators.sampling_enabler import SamplingEnabler
-def WienerFilterCurvature(R, N, S, inverter, sampling_inverter=None):
+def WienerFilterCurvature(R, N, S, iteration_controller=None,
+ iteration_controller_sampling=None):
"""The curvature of the WienerFilterEnergy.
This operator implements the second derivative of the
@@ -37,16 +38,16 @@ def WienerFilterCurvature(R, N, S, inverter, sampling_inverter=None):
The noise covariance.
S : DiagonalOperator
The prior signal covariance
- inverter : Minimizer
- The minimizer to use during numerical inversion
- sampling_inverter : Minimizer
- The minimizer to use during numerical sampling
- if None, it is not possible to draw inverse samples
- default: None
+ iteration_controller : IterationController
+ The iteration controller to use during numerical inversion via
+ ConjugateGradient.
+ iteration_controller_sampling : IterationController
+ The iteration controller to use for sampling.
"""
M = SandwichOperator.make(R, N.inverse)
- if sampling_inverter is not None:
- op = SamplingEnabler(M, S.inverse, sampling_inverter, S.inverse)
+ if iteration_controller is not None:
+ op = SamplingEnabler(M, S.inverse, iteration_controller_sampling,
+ S.inverse)
else:
op = M + S.inverse
- return InversionEnabler(op, inverter, S.inverse)
+ return InversionEnabler(op, iteration_controller, S.inverse)
diff --git a/nifty5/library/wiener_filter_energy.py b/nifty5/library/wiener_filter_energy.py
index 276aa71e22953e908045491cf15fce2f2ec71137..c5b7dbdb19233d36bdc51a920ce35b6cde2457bc 100644
--- a/nifty5/library/wiener_filter_energy.py
+++ b/nifty5/library/wiener_filter_energy.py
@@ -20,8 +20,8 @@ from ..minimization.quadratic_energy import QuadraticEnergy
from .wiener_filter_curvature import WienerFilterCurvature
-def WienerFilterEnergy(position, d, R, N, S, inverter=None,
- sampling_inverter=None):
+def WienerFilterEnergy(position, d, R, N, S, iteration_controller=None,
+ iteration_controller_sampling=None):
"""The Energy for the Wiener filter.
It covers the case of linear measurement with
@@ -48,6 +48,7 @@ def WienerFilterEnergy(position, d, R, N, S, inverter=None,
if None, it is not possible to draw inverse samples
default: None
"""
- op = WienerFilterCurvature(R, N, S, inverter, sampling_inverter)
+ op = WienerFilterCurvature(R, N, S, iteration_controller,
+ iteration_controller_sampling)
vec = R.adjoint_times(N.inverse_times(d))
return QuadraticEnergy(position, op, vec)
diff --git a/nifty5/minimization/energy_sum.py b/nifty5/minimization/energy_sum.py
index 5a4453109787e8add6b9c34785e91284ca2f1d36..5155947a79800f04e168828b1c8b0ced52558210 100644
--- a/nifty5/minimization/energy_sum.py
+++ b/nifty5/minimization/energy_sum.py
@@ -61,6 +61,4 @@ class EnergySum(Energy):
if precon is None and self._precon_idx is not None:
precon = self._energies[self._precon_idx].curvature
from ..operators.inversion_enabler import InversionEnabler
- from .conjugate_gradient import ConjugateGradient
- return InversionEnabler(
- res, ConjugateGradient(self._min_controller), precon)
+ return InversionEnabler(res, self._min_controller, precon)
diff --git a/nifty5/operators/inversion_enabler.py b/nifty5/operators/inversion_enabler.py
index e670bed811338101bcf8a88f97ef186d5adfae1c..9980d8492f0781d436232712f63d5f38ae8eeb66 100644
--- a/nifty5/operators/inversion_enabler.py
+++ b/nifty5/operators/inversion_enabler.py
@@ -16,11 +16,13 @@
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.
-from ..minimization.quadratic_energy import QuadraticEnergy
-from ..minimization.iteration_controller import IterationController
+import numpy as np
+
from ..logger import logger
+from ..minimization.conjugate_gradient import ConjugateGradient
+from ..minimization.iteration_controller import IterationController
+from ..minimization.quadratic_energy import QuadraticEnergy
from .endomorphic_operator import EndomorphicOperator
-import numpy as np
class InversionEnabler(EndomorphicOperator):
@@ -34,9 +36,9 @@ class InversionEnabler(EndomorphicOperator):
The InversionEnabler object will support the same operation modes as
`op`, and additionally the inverse set. The newly-added modes will
be computed by iterative inversion.
- inverter : :class:`Minimizer`
- The minimizer to use for the iterative numerical inversion.
- Typically, this is a :class:`ConjugateGradient` object.
+ iteration_controller : :class:`IterationController`
+ The iteration controller to use for the iterative numerical inversion
+ done by a :class:`ConjugateGradient` object.
approximation : :class:`LinearOperator`, optional
if not None, this operator should be an approximation to `op`, which
supports the operation modes that `op` doesn't have. It is used as a
@@ -44,10 +46,10 @@ class InversionEnabler(EndomorphicOperator):
convergence.
"""
- def __init__(self, op, inverter, approximation=None):
+ def __init__(self, op, iteration_controller, approximation=None):
super(InversionEnabler, self).__init__()
self._op = op
- self._inverter = inverter
+ self._ic = iteration_controller
self._approximation = approximation
@property
@@ -70,7 +72,8 @@ class InversionEnabler(EndomorphicOperator):
if prec is not None:
prec = prec._flip_modes(self._ilog[mode])
energy = QuadraticEnergy(x0, invop, x)
- r, stat = self._inverter(energy, preconditioner=prec)
+ inverter = ConjugateGradient(self._ic)
+ r, stat = inverter(energy, preconditioner=prec)
if stat != IterationController.CONVERGED:
logger.warning("Error detected during operator inversion")
return r.position
diff --git a/nifty5/operators/sampling_enabler.py b/nifty5/operators/sampling_enabler.py
index 4fb373e8e800bcd7a77c03f997fb5f9061e2382b..e3a66a11061d8b3bc5970ff93fac828bb5f07723 100644
--- a/nifty5/operators/sampling_enabler.py
+++ b/nifty5/operators/sampling_enabler.py
@@ -18,6 +18,7 @@
import numpy as np
+from ..minimization.conjugate_gradient import ConjugateGradient
from ..minimization.quadratic_energy import QuadraticEnergy
from .endomorphic_operator import EndomorphicOperator
@@ -33,9 +34,9 @@ class SamplingEnabler(EndomorphicOperator):
The InversionEnabler object will support the same operation modes as
`op`, and additionally the inverse set. The newly-added modes will
be computed by iterative inversion.
- inverter : :class:`Minimizer`
- The minimizer to use for the iterative numerical inversion.
- Typically, this is a :class:`ConjugateGradient` object.
+ iteration_controller : :class:`IterationController`
+ The iteration controller to use for the iterative numerical inversion
+ done by a :class:`ConjugateGradient` object.
approximation : :class:`LinearOperator`, optional
if not None, this operator should be an approximation to `op`, which
supports the operation modes that `op` doesn't have. It is used as a
@@ -43,13 +44,13 @@ class SamplingEnabler(EndomorphicOperator):
convergence.
"""
- def __init__(self, likelihood, prior, sampling_inverter,
+ def __init__(self, likelihood, prior, iteration_controller,
approximation=None):
self._op = likelihood + prior
super(SamplingEnabler, self).__init__()
self._likelihood = likelihood
self._prior = prior
- self._sampling_inverter = sampling_inverter
+ self._ic = iteration_controller
self._approximation = approximation
def draw_sample(self, from_inverse=False, dtype=np.float64):
@@ -61,8 +62,9 @@ class SamplingEnabler(EndomorphicOperator):
nj = self._likelihood.draw_sample()
energy = QuadraticEnergy(s, self._op, sp + nj,
_grad=self._likelihood(s) - nj)
- energy, convergence = self._sampling_inverter(
- energy, preconditioner=self._approximation.inverse)
+ inverter = ConjugateGradient(self._ic)
+ energy, convergence = inverter(energy,
+ preconditioner=self._approximation.inverse)
return energy.position
@property
diff --git a/test/test_energies/test_map.py b/test/test_energies/test_map.py
index 597f499616d50daf014e55539c2128a428bfa97c..7c7e5f5208f150dfa8968460394abb7aa6f56cf4 100644
--- a/test/test_energies/test_map.py
+++ b/test/test_energies/test_map.py
@@ -56,11 +56,10 @@ class Energy_Tests(unittest.TestCase):
IC = ift.GradientNormController(
iteration_limit=100,
tol_abs_gradnorm=1e-5)
- inverter = ift.ConjugateGradient(IC)
S = ift.create_power_operator(hspace, power_spectrum=_flat_PS)
energy = ift.library.WienerFilterEnergy(
- position=s0, d=d, R=R, N=N, S=S, inverter=inverter)
+ position=s0, d=d, R=R, N=N, S=S, iteration_controller=IC)
ift.extra.check_value_gradient_curvature_consistency(
energy, tol=1e-4, ntries=10)
diff --git a/test/test_energies/test_noise.py b/test/test_energies/test_noise.py
index d62284ce7935263e4187a0ffe74d718c4831c6a2..a762919db25b13ff86f52c5afa7f3b2596c7bd4a 100644
--- a/test/test_energies/test_noise.py
+++ b/test/test_energies/test_noise.py
@@ -65,7 +65,6 @@ class Noise_Energy_Tests(unittest.TestCase):
IC = ift.GradientNormController(
iteration_limit=100,
tol_abs_gradnorm=1e-5)
- inverter = ift.ConjugateGradient(IC)
S = ift.create_power_operator(hspace, power_spectrum=_flat_PS)
C = ift.library.NonlinearWienerFilterEnergy(
@@ -77,7 +76,7 @@ class Noise_Energy_Tests(unittest.TestCase):
power=A,
N=N,
S=S,
- inverter=inverter).curvature
+ iteration_controller=IC).curvature
res_sample_list = [d - R(f(ht(C.draw_sample(from_inverse=True) + xi)))
for _ in range(10)]
diff --git a/test/test_energies/test_power.py b/test/test_energies/test_power.py
index fee623d886d306874f960b5cd9d95863fed02fc5..99669117f8a7ab42c89a49d7d43cca6cd0a0bc4f 100644
--- a/test/test_energies/test_power.py
+++ b/test/test_energies/test_power.py
@@ -57,7 +57,6 @@ class Energy_Tests(unittest.TestCase):
IC = ift.GradientNormController(
iteration_limit=100,
tol_abs_gradnorm=1e-5)
- inverter = ift.ConjugateGradient(IC)
S = ift.create_power_operator(hspace, power_spectrum=_flat_PS)
D = ift.library.NonlinearWienerFilterEnergy(
@@ -69,7 +68,7 @@ class Energy_Tests(unittest.TestCase):
N=N,
S=S,
ht=ht,
- inverter=inverter).curvature
+ iteration_controller=IC).curvature
energy = ift.library.NonlinearPowerEnergy(
position=tau0,
diff --git a/test/test_minimization/test_minimizers.py b/test/test_minimization/test_minimizers.py
index dbd029faac1497695705312f25054c79355d79fe..060fe30b35191596c7d7a9f9ffb933059e977ff4 100644
--- a/test/test_minimization/test_minimizers.py
+++ b/test/test_minimization/test_minimizers.py
@@ -116,9 +116,7 @@ class Test_Minimizers(unittest.TestCase):
t1 = ift.GradientNormController(tol_abs_gradnorm=1e-5,
iteration_limit=1000)
- t2 = ift.ConjugateGradient(controller=t1)
- return ift.InversionEnabler(RBCurv(self._position),
- inverter=t2)
+ return ift.InversionEnabler(RBCurv(self._position), t1)
try:
minimizer = eval(minimizer)