diff --git a/demos/bernoulli_demo.py b/demos/bernoulli_demo.py
index f423bd3be3a6f4d17f1a0563f16958f6aea14481..700b377c6b4987c0b787c7a7e77a8f26729e793b 100644
--- a/demos/bernoulli_demo.py
+++ b/demos/bernoulli_demo.py
@@ -73,7 +73,7 @@ if __name__ == '__main__':
# Minimize the Hamiltonian
H = ift.Hamiltonian(likelihood, ic_sampling)
- H = ift.EnergyAdapter(position, H)
+ H = ift.EnergyAdapter(position, H, want_metric=True)
# minimizer = ift.L_BFGS(ic_newton)
H, convergence = minimizer(H)
diff --git a/demos/getting_started_2.py b/demos/getting_started_2.py
index 947a3da04e5cfb2f26cd74607e99df15d3002e71..467fbedc044c8902e3c4ce71d7020ac810fb574f 100644
--- a/demos/getting_started_2.py
+++ b/demos/getting_started_2.py
@@ -93,7 +93,7 @@ if __name__ == '__main__':
# Minimize the Hamiltonian
H = ift.Hamiltonian(likelihood)
- H = ift.EnergyAdapter(position, H)
+ H = ift.EnergyAdapter(position, H, want_metric=True)
H, convergence = minimizer(H)
# Plot results
diff --git a/demos/getting_started_3.py b/demos/getting_started_3.py
index e613a4803129c000a7ee418e531cc9a54bd5b9c1..4b9b7e59ab180f39e522b771552e75117397964b 100644
--- a/demos/getting_started_3.py
+++ b/demos/getting_started_3.py
@@ -91,30 +91,24 @@ if __name__ == '__main__':
# number of samples used to estimate the KL
N_samples = 20
for i in range(2):
- metric = H(ift.Linearization.make_var(position)).metric
- samples = [
- metric.draw_sample(from_inverse=True) for _ in range(N_samples)
- ]
-
- KL = ift.SampledKullbachLeiblerDivergence(H, samples)
- KL = ift.EnergyAdapter(position, KL)
+ KL = ift.KL_Energy(position, H, N_samples, want_metric=True)
KL, convergence = minimizer(KL)
position = KL.position
plot = ift.Plot()
- plot.add(signal(position), title="reconstruction")
- plot.add([A(position), A(MOCK_POSITION)], title="power")
+ plot.add(signal(KL.position), title="reconstruction")
+ plot.add([A(KL.position), A(MOCK_POSITION)], title="power")
plot.output(ny=1, ysize=6, xsize=16, name="loop.png")
plot = ift.Plot()
sc = ift.StatCalculator()
- for sample in samples:
- sc.add(signal(sample+position))
+ for sample in KL.samples:
+ sc.add(signal(sample+KL.position))
plot.add(sc.mean, title="Posterior Mean")
plot.add(ift.sqrt(sc.var), title="Posterior Standard Deviation")
- powers = [A(s+position) for s in samples]
+ powers = [A(s+KL.position) for s in KL.samples]
plot.add(
- [A(position), A(MOCK_POSITION)]+powers,
+ [A(KL.position), A(MOCK_POSITION)]+powers,
title="Sampled Posterior Power Spectrum")
plot.output(ny=1, nx=3, xsize=24, ysize=6, name="results.png")
diff --git a/demos/polynomial_fit.py b/demos/polynomial_fit.py
index f49cbec8b0a9e26810ae3e5e65cf51cd226aaaf8..403269c791db3b55c9327264cdb8c30c6d050c1c 100644
--- a/demos/polynomial_fit.py
+++ b/demos/polynomial_fit.py
@@ -86,15 +86,16 @@ N = ift.DiagonalOperator(ift.from_global_data(d_space, var))
IC = ift.GradientNormController(tol_abs_gradnorm=1e-8)
likelihood = ift.GaussianEnergy(d, N)(R)
-H = ift.Hamiltonian(likelihood, IC)
-H = ift.EnergyAdapter(params, H, IC)
+Ham = ift.Hamiltonian(likelihood, IC)
+H = ift.EnergyAdapter(params, Ham, want_metric=True)
# Minimize
minimizer = ift.NewtonCG(IC)
H, _ = minimizer(H)
# Draw posterior samples
-samples = [H.metric.draw_sample(from_inverse=True) + H.position
+metric = Ham(ift.Linearization.make_var(H.position, want_metric=True)).metric
+samples = [metric.draw_sample(from_inverse=True) + H.position
for _ in range(N_samples)]
# Plotting
diff --git a/nifty5/__init__.py b/nifty5/__init__.py
index 231d2636828da0049fb4538c9f8230b2761fa215..8019ecbcf77ac36636bc92a590b2bb95c3ebf705 100644
--- a/nifty5/__init__.py
+++ b/nifty5/__init__.py
@@ -67,6 +67,7 @@ from .minimization.energy import Energy
from .minimization.quadratic_energy import QuadraticEnergy
from .minimization.line_energy import LineEnergy
from .minimization.energy_adapter import EnergyAdapter
+from .minimization.kl_energy import KL_Energy
from .sugar import *
from .plotting.plot import Plot
diff --git a/nifty5/extra/energy_and_model_tests.py b/nifty5/extra/energy_and_model_tests.py
index 60022c10129a337759b102e3c6622e7518e56692..f151e5756a659ee558128499d9af103c13f93d85 100644
--- a/nifty5/extra/energy_and_model_tests.py
+++ b/nifty5/extra/energy_and_model_tests.py
@@ -41,7 +41,7 @@ def _get_acceptable_location(op, loc, lin):
for i in range(50):
try:
loc2 = loc+dir
- lin2 = op(Linearization.make_var(loc2))
+ lin2 = op(Linearization.make_var(loc2, lin.want_metric))
if np.isfinite(lin2.val.sum()) and abs(lin2.val.sum()) < 1e20:
break
except FloatingPointError:
@@ -54,14 +54,14 @@ def _get_acceptable_location(op, loc, lin):
def _check_consistency(op, loc, tol, ntries, do_metric):
for _ in range(ntries):
- lin = op(Linearization.make_var(loc))
+ lin = op(Linearization.make_var(loc, do_metric))
loc2, lin2 = _get_acceptable_location(op, loc, lin)
dir = loc2-loc
locnext = loc2
dirnorm = dir.norm()
for i in range(50):
locmid = loc + 0.5*dir
- linmid = op(Linearization.make_var(locmid))
+ linmid = op(Linearization.make_var(locmid, do_metric))
dirder = linmid.jac(dir)
numgrad = (lin2.val-lin.val)
xtol = tol * dirder.norm() / np.sqrt(dirder.size)
diff --git a/nifty5/library/inverse_gamma_model.py b/nifty5/library/inverse_gamma_model.py
index 9be2acce1a7b4aad7e292849034cca8cd10a60b0..831546453062e734b0e708dd33245e5fd13304e2 100644
--- a/nifty5/library/inverse_gamma_model.py
+++ b/nifty5/library/inverse_gamma_model.py
@@ -53,7 +53,7 @@ class InverseGammaModel(Operator):
outer = 1/outer_inv
jac = makeOp(Field.from_local_data(self._domain, inner*outer))
jac = jac(x.jac)
- return Linearization(points, jac)
+ return x.new(points, jac)
@staticmethod
def IG(field, alpha, q):
diff --git a/nifty5/linearization.py b/nifty5/linearization.py
index aa98d156f9d04ef3e5e29a7ee5ec7d5049affec7..691d7f386d3ad197209a22f17d46283fff2fd39c 100644
--- a/nifty5/linearization.py
+++ b/nifty5/linearization.py
@@ -9,13 +9,17 @@ from .sugar import makeOp
class Linearization(object):
- def __init__(self, val, jac, metric=None):
+ def __init__(self, val, jac, metric=None, want_metric=False):
self._val = val
self._jac = jac
if self._val.domain != self._jac.target:
raise ValueError("domain mismatch")
+ self._want_metric = want_metric
self._metric = metric
+ def new(self, val, jac, metric=None):
+ return Linearization(val, jac, metric, self._want_metric)
+
@property
def domain(self):
return self._jac.domain
@@ -37,6 +41,10 @@ class Linearization(object):
"""Only available if target is a scalar"""
return self._jac.adjoint_times(Field.scalar(1.))
+ @property
+ def want_metric(self):
+ return self._want_metric
+
@property
def metric(self):
"""Only available if target is a scalar"""
@@ -44,35 +52,34 @@ class Linearization(object):
def __getitem__(self, name):
from .operators.simple_linear_operators import FieldAdapter
- return Linearization(self._val[name], FieldAdapter(self.domain, name))
+ return self.new(self._val[name], FieldAdapter(self.domain, name))
def __neg__(self):
- return Linearization(
- -self._val, -self._jac,
- None if self._metric is None else -self._metric)
+ return self.new(-self._val, -self._jac,
+ None if self._metric is None else -self._metric)
def conjugate(self):
- return Linearization(
+ return self.new(
self._val.conjugate(), self._jac.conjugate(),
None if self._metric is None else self._metric.conjugate())
@property
def real(self):
- return Linearization(self._val.real, self._jac.real)
+ return self.new(self._val.real, self._jac.real)
def _myadd(self, other, neg):
if isinstance(other, Linearization):
met = None
if self._metric is not None and other._metric is not None:
met = self._metric._myadd(other._metric, neg)
- return Linearization(
+ return self.new(
self._val.flexible_addsub(other._val, neg),
self._jac._myadd(other._jac, neg), met)
if isinstance(other, (int, float, complex, Field, MultiField)):
if neg:
- return Linearization(self._val-other, self._jac, self._metric)
+ return self.new(self._val-other, self._jac, self._metric)
else:
- return Linearization(self._val+other, self._jac, self._metric)
+ return self.new(self._val+other, self._jac, self._metric)
def __add__(self, other):
return self._myadd(other, False)
@@ -98,7 +105,7 @@ class Linearization(object):
if isinstance(other, Linearization):
if self.target != other.target:
raise ValueError("domain mismatch")
- return Linearization(
+ return self.new(
self._val*other._val,
(makeOp(other._val)(self._jac))._myadd(
makeOp(self._val)(other._jac), False))
@@ -106,11 +113,11 @@ class Linearization(object):
if other == 1:
return self
met = None if self._metric is None else self._metric.scale(other)
- return Linearization(self._val*other, self._jac.scale(other), met)
+ return self.new(self._val*other, self._jac.scale(other), met)
if isinstance(other, (Field, MultiField)):
if self.target != other.domain:
raise ValueError("domain mismatch")
- return Linearization(self._val*other, makeOp(other)(self._jac))
+ return self.new(self._val*other, makeOp(other)(self._jac))
def __rmul__(self, other):
return self.__mul__(other)
@@ -118,46 +125,48 @@ class Linearization(object):
def vdot(self, other):
from .operators.simple_linear_operators import VdotOperator
if isinstance(other, (Field, MultiField)):
- return Linearization(
+ return self.new(
Field.scalar(self._val.vdot(other)),
VdotOperator(other)(self._jac))
- return Linearization(
+ return self.new(
Field.scalar(self._val.vdot(other._val)),
VdotOperator(self._val)(other._jac) +
VdotOperator(other._val)(self._jac))
def sum(self):
from .operators.simple_linear_operators import SumReductionOperator
- return Linearization(
+ return self.new(
Field.scalar(self._val.sum()),
SumReductionOperator(self._jac.target)(self._jac))
def exp(self):
tmp = self._val.exp()
- return Linearization(tmp, makeOp(tmp)(self._jac))
+ return self.new(tmp, makeOp(tmp)(self._jac))
def log(self):
tmp = self._val.log()
- return Linearization(tmp, makeOp(1./self._val)(self._jac))
+ return self.new(tmp, makeOp(1./self._val)(self._jac))
def tanh(self):
tmp = self._val.tanh()
- return Linearization(tmp, makeOp(1.-tmp**2)(self._jac))
+ return self.new(tmp, makeOp(1.-tmp**2)(self._jac))
def positive_tanh(self):
tmp = self._val.tanh()
tmp2 = 0.5*(1.+tmp)
- return Linearization(tmp2, makeOp(0.5*(1.-tmp**2))(self._jac))
+ return self.new(tmp2, makeOp(0.5*(1.-tmp**2))(self._jac))
def add_metric(self, metric):
- return Linearization(self._val, self._jac, metric)
+ return self.new(self._val, self._jac, metric)
@staticmethod
- def make_var(field):
+ def make_var(field, want_metric=False):
from .operators.scaling_operator import ScalingOperator
- return Linearization(field, ScalingOperator(1., field.domain))
+ return Linearization(field, ScalingOperator(1., field.domain),
+ want_metric=want_metric)
@staticmethod
- def make_const(field):
+ def make_const(field, want_metric=False):
from .operators.simple_linear_operators import NullOperator
- return Linearization(field, NullOperator(field.domain, field.domain))
+ return Linearization(field, NullOperator(field.domain, field.domain),
+ want_metric=want_metric)
diff --git a/nifty5/minimization/conjugate_gradient.py b/nifty5/minimization/conjugate_gradient.py
index 9a621917c4234c2bd8f0aa30d2a55f09e39728c8..f2487a3dde1aba9298ab2b3dccc4a2b967f8cf7c 100644
--- a/nifty5/minimization/conjugate_gradient.py
+++ b/nifty5/minimization/conjugate_gradient.py
@@ -75,7 +75,7 @@ class ConjugateGradient(Minimizer):
return energy, controller.CONVERGED
while True:
- q = energy.metric(d)
+ q = energy.apply_metric(d)
ddotq = d.vdot(q).real
if ddotq == 0.:
logger.error("Error: ConjugateGradient: ddotq==0.")
diff --git a/nifty5/minimization/descent_minimizers.py b/nifty5/minimization/descent_minimizers.py
index 5eddad19ca680e5111393709236825d8048e8ade..bc0da9017747c69e2c0a744fa116d94927c94a6c 100644
--- a/nifty5/minimization/descent_minimizers.py
+++ b/nifty5/minimization/descent_minimizers.py
@@ -180,7 +180,7 @@ class NewtonCG(DescentMinimizer):
while True:
if abs(ri).sum() <= termcond:
return xsupi
- Ap = energy.metric(psupi)
+ Ap = energy.apply_metric(psupi)
# check curvature
curv = psupi.vdot(Ap)
if 0 <= curv <= 3*float64eps:
diff --git a/nifty5/minimization/energy.py b/nifty5/minimization/energy.py
index c213a6e8f71396e3a508e8abb778b5649a275db4..cfa59fb552dcfc3df8f37503bb765037834ed47a 100644
--- a/nifty5/minimization/energy.py
+++ b/nifty5/minimization/energy.py
@@ -109,6 +109,20 @@ class Energy(NiftyMetaBase()):
"""
raise NotImplementedError
+ def apply_metric(self, x):
+ """
+ Parameters
+ ----------
+ x: Field/MultiField
+ Argument for the metric operator
+
+ Returns
+ -------
+ Field/MultiField:
+ Output of the metric operator
+ """
+ raise NotImplementedError
+
def longest_step(self, dir):
"""Returns the longest allowed step size along `dir`
diff --git a/nifty5/minimization/energy_adapter.py b/nifty5/minimization/energy_adapter.py
index f85d2e9d8c215a034319949190a5d8f2c063633e..985459cc56f162fe7b5306577d605b598129e4fc 100644
--- a/nifty5/minimization/energy_adapter.py
+++ b/nifty5/minimization/energy_adapter.py
@@ -8,58 +8,38 @@ from ..operators.scaling_operator import ScalingOperator
class EnergyAdapter(Energy):
- def __init__(self, position, op, controller=None, preconditioner=None,
- constants=[]):
+ def __init__(self, position, op, constants=[], want_metric=False):
super(EnergyAdapter, self).__init__(position)
self._op = op
- self._val = self._grad = self._metric = None
- self._controller = controller
- self._preconditioner = preconditioner
self._constants = constants
-
- def at(self, position):
- return EnergyAdapter(position, self._op, self._controller,
- self._preconditioner, self._constants)
-
- def _fill_all(self):
+ self._want_metric = want_metric
if len(self._constants) == 0:
- tmp = self._op(Linearization.make_var(self._position))
+ tmp = self._op(Linearization.make_var(self._position, want_metric))
else:
ops = [ScalingOperator(0. if key in self._constants else 1., dom)
for key, dom in self._position.domain.items()]
bdop = BlockDiagonalOperator(self._position.domain, tuple(ops))
- tmp = self._op(Linearization(self._position, bdop))
+ tmp = self._op(Linearization(self._position, bdop,
+ want_metric=want_metric))
self._val = tmp.val.local_data[()]
self._grad = tmp.gradient
- if self._controller is not None:
- from ..operators.linear_operator import LinearOperator
- from ..operators.inversion_enabler import InversionEnabler
+ self._metric = tmp._metric
- if self._preconditioner is None:
- precond = None
- elif isinstance(self._preconditioner, LinearOperator):
- precond = self._preconditioner
- elif isinstance(self._preconditioner, Energy):
- precond = self._preconditioner.at(self._position).metric
- self._metric = InversionEnabler(tmp._metric, self._controller,
- precond)
- else:
- self._metric = tmp._metric
+ def at(self, position):
+ return EnergyAdapter(position, self._op, self._constants,
+ self._want_metric)
@property
def value(self):
- if self._val is None:
- self._val = self._op(self._position).local_data[()]
return self._val
@property
def gradient(self):
- if self._grad is None:
- self._fill_all()
return self._grad
@property
def metric(self):
- if self._metric is None:
- self._fill_all()
return self._metric
+
+ def apply_metric(self, x):
+ return self._metric(x)
diff --git a/nifty5/minimization/kl_energy.py b/nifty5/minimization/kl_energy.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7a98364d6024cbada97acca11d0d460864c8280
--- /dev/null
+++ b/nifty5/minimization/kl_energy.py
@@ -0,0 +1,57 @@
+from __future__ import absolute_import, division, print_function
+
+from ..compat import *
+from .energy import Energy
+from ..linearization import Linearization
+from ..operators.scaling_operator import ScalingOperator
+from ..operators.block_diagonal_operator import BlockDiagonalOperator
+from .. import utilities
+
+
+class KL_Energy(Energy):
+ def __init__(self, position, h, nsamp, constants=[], _samples=None,
+ want_metric=False):
+ super(KL_Energy, self).__init__(position)
+ self._h = h
+ self._constants = constants
+ self._want_metric = want_metric
+ if _samples is None:
+ met = h(Linearization.make_var(position, True)).metric
+ _samples = tuple(met.draw_sample(from_inverse=True)
+ for _ in range(nsamp))
+ self._samples = _samples
+ if len(constants) == 0:
+ tmp = Linearization.make_var(position, want_metric)
+ else:
+ ops = [ScalingOperator(0. if key in constants else 1., dom)
+ for key, dom in position.domain.items()]
+ bdop = BlockDiagonalOperator(position.domain, tuple(ops))
+ tmp = Linearization(position, bdop, want_metric=want_metric)
+ mymap = map(lambda v: self._h(tmp+v), self._samples)
+ tmp = utilities.my_sum(mymap) * (1./len(self._samples))
+ self._val = tmp.val.local_data[()]
+ self._grad = tmp.gradient
+ self._metric = tmp.metric
+
+ def at(self, position):
+ return KL_Energy(position, self._h, 0, self._constants, self._samples,
+ self._want_metric)
+
+ @property
+ def value(self):
+ return self._val
+
+ @property
+ def gradient(self):
+ return self._grad
+
+ def apply_metric(self, x):
+ return self._metric(x)
+
+ @property
+ def metric(self):
+ return self._metric
+
+ @property
+ def samples(self):
+ return self._samples
diff --git a/nifty5/minimization/quadratic_energy.py b/nifty5/minimization/quadratic_energy.py
index a0949adb1c429d351c8b1959cdfaa42d255f00e7..254bc5ab3d275ec6da9f4ef143e366e73b2d7899 100644
--- a/nifty5/minimization/quadratic_energy.py
+++ b/nifty5/minimization/quadratic_energy.py
@@ -77,3 +77,6 @@ class QuadraticEnergy(Energy):
@property
def metric(self):
return self._A
+
+ def apply_metric(self, x):
+ return self._A(x)
diff --git a/nifty5/minimization/scipy_minimizer.py b/nifty5/minimization/scipy_minimizer.py
index bb94f66490606511fadcb90a02b924ba94a239ed..49e7a4ddaf5d23001ed628023a870550a904659e 100644
--- a/nifty5/minimization/scipy_minimizer.py
+++ b/nifty5/minimization/scipy_minimizer.py
@@ -93,7 +93,7 @@ class _MinHelper(object):
def hessp(self, x, p):
self._update(x)
- res = self._energy.metric(_toField(p, self._energy.position))
+ res = self._energy.apply_metric(_toField(p, self._energy.position))
return _toArray_rw(res)
diff --git a/nifty5/operators/energy_operators.py b/nifty5/operators/energy_operators.py
index b2449755f96316c3061b0826a16b86e82a026371..55de137012c2f9d5f64d71ca652924407ee8552a 100644
--- a/nifty5/operators/energy_operators.py
+++ b/nifty5/operators/energy_operators.py
@@ -42,7 +42,7 @@ class SquaredNormOperator(EnergyOperator):
if isinstance(x, Linearization):
val = Field.scalar(x.val.vdot(x.val))
jac = VdotOperator(2*x.val)(x.jac)
- return Linearization(val, jac)
+ return x.new(val, jac)
return Field.scalar(x.vdot(x))
@@ -59,7 +59,7 @@ class QuadraticFormOperator(EnergyOperator):
t1 = self._op(x.val)
jac = VdotOperator(t1)(x.jac)
val = Field.scalar(0.5*x.val.vdot(t1))
- return Linearization(val, jac)
+ return x.new(val, jac)
return Field.scalar(0.5*x.vdot(self._op(x)))
@@ -91,7 +91,7 @@ class GaussianEnergy(EnergyOperator):
def apply(self, x):
residual = x if self._mean is None else x-self._mean
res = self._op(residual).real
- if not isinstance(x, Linearization):
+ if not isinstance(x, Linearization) or not x.want_metric:
return res
metric = SandwichOperator.make(x.jac, self._icov)
return res.add_metric(metric)
@@ -107,6 +107,8 @@ class PoissonianEnergy(EnergyOperator):
res = x.sum() - x.log().vdot(self._d)
if not isinstance(x, Linearization):
return Field.scalar(res)
+ if not x.want_metric:
+ return res
metric = SandwichOperator.make(x.jac, makeOp(1./x.val))
return res.add_metric(metric)
@@ -136,6 +138,8 @@ class BernoulliEnergy(EnergyOperator):
v = x.log().vdot(-self._d) - (1.-x).log().vdot(1.-self._d)
if not isinstance(x, Linearization):
return Field.scalar(v)
+ if not x.want_metric:
+ return v
met = makeOp(1./(x.val*(1.-x.val)))
met = SandwichOperator.make(x.jac, met)
return v.add_metric(met)
@@ -149,11 +153,11 @@ class Hamiltonian(EnergyOperator):
self._domain = lh.domain
def apply(self, x):
- if self._ic_samp is None or not isinstance(x, Linearization):
+ if (self._ic_samp is None or not isinstance(x, Linearization) or
+ not x.want_metric):
return self._lh(x)+self._prior(x)
else:
- lhx = self._lh(x)
- prx = self._prior(x)
+ lhx, prx = self._lh(x), self._prior(x)
mtr = SamplingEnabler(lhx.metric, prx.metric.inverse,
self._ic_samp, prx.metric.inverse)
return (lhx+prx).add_metric(mtr)
diff --git a/nifty5/operators/linear_operator.py b/nifty5/operators/linear_operator.py
index 26a0cf81dc0dfac484ce06f2ba567158198c09c6..aa330c2f4ccede83ad73de5b72810da71ed542ba 100644
--- a/nifty5/operators/linear_operator.py
+++ b/nifty5/operators/linear_operator.py
@@ -175,7 +175,7 @@ class LinearOperator(Operator):
return self.apply(x, self.TIMES)
from ..linearization import Linearization
if isinstance(x, Linearization):
- return Linearization(self(x._val), self(x._jac))
+ return x.new(self(x._val), self(x._jac))
return self.__matmul__(x)
def times(self, x):
diff --git a/nifty5/operators/operator.py b/nifty5/operators/operator.py
index 73ece15e64ef3e1f64b8d8e27f75e09f4ced4f89..13b7e6fb7bfb1e27f12e3f292744b239f868b8b5 100644
--- a/nifty5/operators/operator.py
+++ b/nifty5/operators/operator.py
@@ -144,11 +144,12 @@ class _OpProd(Operator):
v2 = v.extract(self._op2.domain)
if not lin:
return self._op1(v1) * self._op2(v2)
- lin1 = self._op1(Linearization.make_var(v1))
- lin2 = self._op2(Linearization.make_var(v2))
+ wm = x.want_metric
+ lin1 = self._op1(Linearization.make_var(v1, wm))
+ lin2 = self._op2(Linearization.make_var(v2, wm))
op = (makeOp(lin1._val)(lin2._jac))._myadd(
makeOp(lin2._val)(lin1._jac), False)
- return Linearization(lin1._val*lin2._val, op(x.jac))
+ return lin1.new(lin1._val*lin2._val, op(x.jac))
class _OpSum(Operator):
@@ -168,10 +169,11 @@ class _OpSum(Operator):
res = None
if not lin:
return self._op1(v1).unite(self._op2(v2))
- lin1 = self._op1(Linearization.make_var(v1))
- lin2 = self._op2(Linearization.make_var(v2))
+ wm = x.want_metric
+ lin1 = self._op1(Linearization.make_var(v1, wm))
+ lin2 = self._op2(Linearization.make_var(v2, wm))
op = lin1._jac._myadd(lin2._jac, False)
- res = Linearization(lin1._val+lin2._val, op(x.jac))
+ res = lin1.new(lin1._val+lin2._val, op(x.jac))
if lin1._metric is not None and lin2._metric is not None:
res = res.add_metric(lin1._metric + lin2._metric)
return res
diff --git a/nifty5/plotting/plot.py b/nifty5/plotting/plot.py
index c2e0b91ccd3c580ea3f63a9a9d89646ed6156458..6e414f5bc5760b6bdb67d12331f0418431640131 100644
--- a/nifty5/plotting/plot.py
+++ b/nifty5/plotting/plot.py
@@ -267,7 +267,6 @@ class Plot(object):
self._plots = []
self._kwargs = []
-
def add(self, f, **kwargs):
"""Add a figure to the current list of plots.
@@ -303,7 +302,6 @@ class Plot(object):
self._plots.append(f)
self._kwargs.append(kwargs)
-
def output(self, **kwargs):
"""Plot the accumulated list of figures.
diff --git a/test/test_minimization/test_minimizers.py b/test/test_minimization/test_minimizers.py
index 7b6df9e777c3386a8d7eed98985e91ea2b07e72e..5ec52853538d3e488fda8bc69277a21e2a120e96 100644
--- a/test/test_minimization/test_minimizers.py
+++ b/test/test_minimization/test_minimizers.py
@@ -113,6 +113,12 @@ class Test_Minimizers(unittest.TestCase):
iteration_limit=1000)
return ift.InversionEnabler(RBCurv(self._position), t1)
+ def apply_metric(self, x):
+ inp = x.to_global_data_rw()
+ pos = self._position.to_global_data_rw()
+ return ift.Field.from_global_data(
+ space, rosen_hess_prod(pos, inp))
+
try:
minimizer = eval(minimizer)
energy = RBEnergy(position=starting_point)
@@ -145,12 +151,11 @@ class Test_Minimizers(unittest.TestCase):
return ift.Field.full(self.position.domain,
2*x*np.exp(-(x**2)))
- @property
- def metric(self):
- x = self.position.to_global_data()[0]
- v = (2 - 4*x*x)*np.exp(-x**2)
+ def apply_metric(self, x):
+ p = self.position.to_global_data()[0]
+ v = (2 - 4*p*p)*np.exp(-p**2)
return ift.DiagonalOperator(
- ift.Field.full(self.position.domain, v))
+ ift.Field.full(self.position.domain, v))(x)
try:
minimizer = eval(minimizer)
@@ -190,6 +195,12 @@ class Test_Minimizers(unittest.TestCase):
return ift.DiagonalOperator(
ift.Field.full(self.position.domain, v))
+ def apply_metric(self, x):
+ p = self.position.to_global_data()[0]
+ v = np.cosh(p)
+ return ift.DiagonalOperator(
+ ift.Field.full(self.position.domain, v))(x)
+
try:
minimizer = eval(minimizer)
energy = CoshEnergy(position=starting_point)