rename EnergyAdapter to PointKL

demos/getting_started_2.py

@@ -102,7 +102,7 @@ def main():
     # Compute MAP solution by minimizing the information Hamiltonian
     H = ift.StandardHamiltonian(likelihood)
     initial_position = ift.from_random(domain, 'normal')
-    H = ift.EnergyAdapter(initial_position, H, want_metric=True)
+    H = ift.PointKL(initial_position, H, want_metric=True)
     H, convergence = minimizer(H)
 
     # Plotting

demos/mgvi_visualized.py

@@ -75,7 +75,7 @@ def main():
     # plt.close()
 
     pos = ift.from_random(ham.domain, 'normal')
-    MAP = ift.EnergyAdapter(pos, ham, want_metric=True)
+    MAP = ift.PointKL(pos, ham, want_metric=True)
     minimizer = ift.NewtonCG(
         ift.GradientNormController(iteration_limit=20, name='Mini'))
     minimizer_mf = ift.ADVIOptimizer(10)

demos/polynomial_fit.py

@@ -106,7 +106,7 @@ def main():
     IC = ift.DeltaEnergyController(tol_rel_deltaE=1e-12, iteration_limit=200)
     likelihood = ift.GaussianEnergy(d, N) @ R
     Ham = ift.StandardHamiltonian(likelihood, IC)
-    H = ift.EnergyAdapter(params, Ham, want_metric=True)
+    H = ift.PointKL(params, Ham, want_metric=True)
 
     # Minimize
     minimizer = ift.NewtonCG(IC)

src/__init__.py

@@ -71,7 +71,7 @@ from .minimization.stochastic_minimizer import ADVIOptimizer
 from .minimization.scipy_minimizer import L_BFGS_B
 from .minimization.energy import Energy
 from .minimization.quadratic_energy import QuadraticEnergy
-from .minimization.energy_adapter import EnergyAdapter
+from .minimization.point_kl import PointKL
 from .minimization.gaussian_kl import MetricGaussianKL, ParametricGaussianKL
 
 from .sugar import *

src/minimization/energy_adapter.py → src/minimization/point_kl.py

@@ -22,7 +22,7 @@ from ..minimization.energy import Energy
 from ..sugar import makeDomain
 
 
-class EnergyAdapter(Energy):
+class PointKL(Energy):
     """Helper class which provides the traditional Nifty Energy interface to
     Nifty operators with a scalar target domain.