From 61b1f73bcc19121ed2ecb9f19ffe67564169530b Mon Sep 17 00:00:00 2001
From: "Knollmueller, Jakob (kjako)" <jakob@knollmueller.de>
Date: Fri, 2 Jun 2017 14:30:38 +0200
Subject: [PATCH] critical filtering seems to work
---
.gitignore | 1 +
demos/critical_filtering.py | 33 +++++++++----------
demos/laplace_testing.py | 14 ++++----
.../energy_library/critical_power_energy.py | 25 +++++++-------
.../critical_power_curvature.py | 8 ++---
.../operator_library/smoothness_operator.py | 4 +--
6 files changed, 41 insertions(+), 44 deletions(-)
diff --git a/.gitignore b/.gitignore
index 311909456..1716a17d0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,6 +7,7 @@
*.egg
*.egg-info
*~
+*.DS_Store
*.csv
.git/
.svn/
diff --git a/demos/critical_filtering.py b/demos/critical_filtering.py
index 6d157fc1a..42360bda0 100644
--- a/demos/critical_filtering.py
+++ b/demos/critical_filtering.py
@@ -8,7 +8,7 @@ from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.rank
-np.random.seed(232)
+np.random.seed(42)
def plot_parameters(m,t,t_true, t_real, t_d):
@@ -60,8 +60,8 @@ if __name__ == "__main__":
h_space = fft.target[0]
# Setting up power space
- p_space = PowerSpace(h_space, logarithmic=False,
- distribution_strategy=distribution_strategy, nbin=128)
+ p_space = PowerSpace(h_space, logarithmic=True,
+ distribution_strategy=distribution_strategy, nbin=120)
# Choosing the prior correlation structure and defining correlation operator
pow_spec = (lambda k: (.05 / (k + 1) ** 2))
@@ -83,7 +83,7 @@ if __name__ == "__main__":
#Adding a harmonic transformation to the instrument
R = AdjointFFTResponse(fft, Instrument)
- noise = 1.
+ noise = 5.
N = DiagonalOperator(s_space, diagonal=noise, bare=True)
n = Field.from_random(domain=s_space,
random_type='normal',
@@ -95,7 +95,7 @@ if __name__ == "__main__":
realized_power = log(sh.power_analyze(logarithmic=p_space.config["logarithmic"],
nbin=p_space.config["nbin"])**2)
- data_power = log(fft(d).power_analyze(logarithmic=p_space.config["logarithmic"],
+ data_power = log(fft(d).power_analyze(logarithmic=p_space.config["logarithmic"],
nbin=p_space.config["nbin"])**2)
d_data = d.val.get_full_data().real
if rank == 0:
@@ -110,24 +110,24 @@ if __name__ == "__main__":
minimizer1 = RelaxedNewton(convergence_tolerance=0,
convergence_level=1,
- iteration_limit=2,
+ iteration_limit=3,
callback=convergence_measure)
minimizer2 = VL_BFGS(convergence_tolerance=0,
iteration_limit=50,
callback=convergence_measure,
- max_history_length=3)
+ max_history_length=10)
# Setting starting position
flat_power = Field(p_space,val=10e-8)
m0 = flat_power.power_synthesize(real_signal=True)
- t0 = Field(p_space, val=log(1./(1+p_space.kindex)**2))
- #t0 = data_power
+ # t0 = Field(p_space, val=log(1./(1+p_space.kindex)**2))
+ t0 = data_power- 1.
S0 = create_power_operator(h_space, power_spectrum=exp(t0),
distribution_strategy=distribution_strategy)
- for i in range(100):
+ for i in range(500):
S0 = create_power_operator(h_space, power_spectrum=exp(t0),
distribution_strategy=distribution_strategy)
@@ -140,14 +140,13 @@ if __name__ == "__main__":
m0 = map_energy.position
D0 = map_energy.curvature
# Initializing the power energy with updated parameters
- power_energy = CriticalPowerEnergy(position=t0, m=m0, D=D0, sigma=10., samples=3)
- if i > 0:
- (power_energy, convergence) = minimizer1(power_energy)
- else:
- (power_energy, convergence) = minimizer2(power_energy)
+ power_energy = CriticalPowerEnergy(position=t0, m=m0, D=D0, sigma=1000., samples=1)
+
+ (power_energy, convergence) = minimizer1(power_energy)
+
# Setting new power spectrum
- t0 = power_energy.position
- t0.val[-1] = t0.val[-2]
+ t0.val = power_energy.position.val.real
+ # t0.val[-1] = t0.val[-2]
# Plotting current estimate
plot_parameters(m0,t0,log(sp**2),realized_power, data_power)
diff --git a/demos/laplace_testing.py b/demos/laplace_testing.py
index 3577720c8..db8d1e7b7 100644
--- a/demos/laplace_testing.py
+++ b/demos/laplace_testing.py
@@ -58,23 +58,23 @@ if __name__ == "__main__":
# Setting up power space
p_space = PowerSpace(h_space, logarithmic=False,
- distribution_strategy=distribution_strategy, nbin=70)
+ distribution_strategy=distribution_strategy, nbin=200)
# Choosing the prior correlation structure and defining correlation operator
pow_spec = (lambda k: (.05 / (k + 1) ** 2))
# t = Field(p_space, val=pow_spec)
t= Field.from_random("normal", domain=p_space)
- lap = LaplaceOperator(p_space)
- T = SmoothnessOperator(p_space,sigma=1.)
+ lap = LaplaceOperator(p_space, logarithmic=True)
+ T = SmoothnessOperator(p_space,sigma=1., logarithmic=True)
test_energy = TestEnergy(t,T)
def convergence_measure(a_energy, iteration): # returns current energy
x = a_energy.value
print (x, iteration)
minimizer1 = VL_BFGS(convergence_tolerance=0,
- iteration_limit=1000,
+ iteration_limit=10,
callback=convergence_measure,
- max_history_length=3)
+ max_history_length=10)
def explicify(op, domain):
@@ -91,7 +91,7 @@ if __name__ == "__main__":
B = explicify(lap.adjoint_times, p_space)
test_energy,convergence = minimizer1(test_energy)
data = test_energy.position.val.get_full_data()
- pl.plot([go.Scatter(x=log(p_space.kindex)[1:], y=data[1:])], filename="t.html")
+ pl.plot([go.Scatter(x=(p_space.kindex)[1:], y=data[1:])], filename="t.html")
tt = Field.from_random("normal", domain=t.domain)
print "adjointness"
print t.dot(lap(tt))
@@ -100,6 +100,6 @@ if __name__ == "__main__":
aa = Field(p_space, val=p_space.kindex.copy())
aa.val[0] = 1
- print lap(log(aa)).val
+ print lap(log(aa)**2).val
print "######################"
print test_energy.position.val
\ No newline at end of file
diff --git a/nifty/library/energy_library/critical_power_energy.py b/nifty/library/energy_library/critical_power_energy.py
index 81c75c005..46edc70b6 100644
--- a/nifty/library/energy_library/critical_power_energy.py
+++ b/nifty/library/energy_library/critical_power_energy.py
@@ -1,6 +1,6 @@
from nifty.energies.energy import Energy
from nifty.library.operator_library import CriticalPowerCurvature,\
- LaplaceOperator
+ SmoothnessOperator
from nifty.sugar import generate_posterior_sample
from nifty import Field, exp
@@ -31,14 +31,12 @@ class CriticalPowerEnergy(Energy):
self.sigma = sigma
self.alpha = Field(self.position.domain, val=alpha)
self.q = Field(self.position.domain, val=q)
- #self.T = SmoothnessOperator(domain=self.position.domain[0], sigma=self.sigma)
- self.Laplace = LaplaceOperator(self.position.domain[0])
- self.roughness = self.Laplace(self.position)
+ self.T = SmoothnessOperator(domain=self.position.domain[0], sigma=self.sigma)
self.rho = self.position.domain[0].rho
self.w = w
if self.w is None:
self.w = self._calculate_w(self.m, self.D, self.samples)
- self.theta = exp(-self.position) * (self.q + self.w / 2.)
+ self.theta = (exp(-self.position) * (self.q + self.w / 2.))
def at(self, position):
return self.__class__(position, self.m, D=self.D,
@@ -49,22 +47,22 @@ class CriticalPowerEnergy(Energy):
@property
def value(self):
- energy = exp(-self.position).dot(self.q + self.w / 2.)
- energy += self.position.dot(self.alpha - 1. + self.rho / 2.)
- energy += 0.5 * self.roughness.dot(self.roughness) / self.sigma**2
+ energy = exp(-self.position).dot(self.q + self.w / 2., bare= True)
+ energy += self.position.dot(self.alpha - 1. + self.rho / 2., bare=True)
+ energy += 0.5 * self.position.dot(self.T(self.position))
return energy.real
@property
def gradient(self):
- gradient = - self.theta
- gradient += self.alpha - 1. + self.rho / 2.
- gradient += self.Laplace(self.roughness) / self.sigma **2
+ gradient = - self.theta.weight(-1)
+ gradient += (self.alpha - 1. + self.rho / 2.).weight(-1)
+ gradient += self.T(self.position)
+ gradient.val = gradient.val.real
return gradient
@property
def curvature(self):
- curvature = CriticalPowerCurvature(theta=self.theta, Laplace = self.Laplace,
- sigma = self.sigma)
+ curvature = CriticalPowerCurvature(theta=self.theta.weight(-1), T=self.T)
return curvature
def _calculate_w(self, m, D, samples):
@@ -81,6 +79,7 @@ class CriticalPowerEnergy(Energy):
else:
w = m.power_analyze(
logarithmic=self.position.domain[0].config["logarithmic"],
+ nbin=self.position.domain[0].config["nbin"],
decompose_power=False)
w **= 2
w *= self.rho
diff --git a/nifty/library/operator_library/critical_power_curvature.py b/nifty/library/operator_library/critical_power_curvature.py
index ece3cb58b..3f5478071 100644
--- a/nifty/library/operator_library/critical_power_curvature.py
+++ b/nifty/library/operator_library/critical_power_curvature.py
@@ -3,11 +3,10 @@ from nifty.operators import EndomorphicOperator,\
class CriticalPowerCurvature(InvertibleOperatorMixin, EndomorphicOperator):
- def __init__(self, theta, Laplace, sigma, inverter=None, preconditioner=None):
+ def __init__(self, theta, T, inverter=None, preconditioner=None):
self.theta = theta
- self.Laplace = Laplace
- self.sigma = sigma
+ self.T = T
# if preconditioner is None:
# preconditioner = self.T.times
self._domain = self.theta.domain
@@ -28,5 +27,4 @@ class CriticalPowerCurvature(InvertibleOperatorMixin, EndomorphicOperator):
# ---Added properties and methods---
def _times(self, x, spaces):
- return self.Laplace.adjoint_times(self.Laplace(x)) / self.sigma ** 2 \
- + self.theta * x
+ return self.T(x) + self.theta * x
diff --git a/nifty/library/operator_library/smoothness_operator.py b/nifty/library/operator_library/smoothness_operator.py
index fca9d91e2..50e62f29a 100644
--- a/nifty/library/operator_library/smoothness_operator.py
+++ b/nifty/library/operator_library/smoothness_operator.py
@@ -6,7 +6,7 @@ from laplace_operator import LaplaceOperator
class SmoothnessOperator(EndomorphicOperator):
- def __init__(self, domain, sigma=1.,
+ def __init__(self, domain, sigma=1.,logarithmic = True,
default_spaces=None):
super(SmoothnessOperator, self).__init__(default_spaces)
@@ -21,7 +21,7 @@ class SmoothnessOperator(EndomorphicOperator):
self._sigma = sigma
- self._Laplace = LaplaceOperator(domain=self._domain[0])
+ self._Laplace = LaplaceOperator(domain=self._domain[0], logarithmic=logarithmic)
"""
SmoothnessOperator
--
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