Commit f3abedf5 authored by Reimar H Leike's avatar Reimar H Leike
Browse files

added the krylov sampling method to library

parent 3a61c518
Pipeline #27736 passed with stage
in 1 minute and 27 seconds
......@@ -5,3 +5,4 @@ from .nonlinear_power_energy import NonlinearPowerEnergy
from .nonlinear_wiener_filter_energy import NonlinearWienerFilterEnergy
from .poisson_energy import PoissonEnergy
from .nonlinearities import Exponential, Linear, Tanh, PositiveTanh
from .krylov_sampling import generate_krylov_samples
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <>.
# Copyright(C) 2013-2018 Max-Planck-Society
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.
from numpy.random import randn
from numpy import sqrt
from ..field import Field
def generate_krylov_samples(D_inv, S, j=None, N_samps=1, N_iter=10):
Generates inverse samples from a curvature D
This algorithm iteratively generates samples from
a curvature D by applying conjugate gradient steps
and resampling the curvature in search direction.
D_inv : EndomorphicOperator
The curvature which will be the inverse of the covarianc
of the generated samples
S : EndomorphicOperator (from which one can sample)
A prior covariance operator which is used to generate prior
samples that are then iteratively updated
j : Field, optional
A Field to which the inverse of D_inv is applied. The solution
of this matrix inversion problem is a side product of generating
the samples.
If not supplied, it is sampled from the prior.
N_samps : Int, optional
How many samples to generate. Default: 1
N_iter : Int, optional
How many iterations of the conjugate gradient to run. Default: 10
(solution, samples) : A tuple of a field 'solution' and a list of fields
'samples'. The first entry of the tuple is the solution x to
D_inv(x) = j
and the second entry are a list of samples from D_inv.inverse
if j == None:
j = S.draw_sample()
space = D_inv.domain
x = Field.zeros(space)
r = j.copy()
p = r.copy()
d = p.vdot(D_inv(p))
y = []
for i in range(N_samps):
y += [S.draw_sample()]
for k in range(1, 1 + N_iter):
gamma = r.vdot(r) / d
if gamma == 0.:
x += gamma * p
for i in range(N_samps):
y[i] -= p.vdot(D_inv(y[i])) * p / d
y[i] += randn() / sqrt(d) * p
r_new = r - gamma * D_inv(p)
beta = r_new.vdot(r_new) / (r.vdot(r))
r = r_new
p = r + beta * p
d = p.vdot(D_inv(p))
if d == 0.:
return x, y
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