checking library energies for consistency

test/test_energies/consistency_check.py

+# 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
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# 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 <http://www.gnu.org/licenses/>.
+#
+# 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.
+
+import unittest
+from itertools import product
+from test.common import expand
+
+import nifty5 as ift
+import numpy as np
+
+
+class Energy_Tests(unittest.TestCase):
+    def make_model(self, type, **kwargs):
+        if type == 'Constant':
+            np.random.seed(kwargs['seed'])
+            S = ift.ScalingOperator(1., kwargs['space'])
+            s = S.draw_sample()
+            return ift.Constant(
+                ift.MultiField.from_dict({kwargs['space_key']: s}),
+                ift.MultiField.from_dict({kwargs['space_key']: s}))
+        elif type == 'Variable':
+            np.random.seed(kwargs['seed'])
+            S = ift.ScalingOperator(1., kwargs['space'])
+            s = S.draw_sample()
+            return ift.Variable(
+                ift.MultiField.from_dict({kwargs['space_key']: s}))
+        elif type == 'LinearModel':
+            return ift.LinearModel(
+                inp=kwargs['model'], lin_op=kwargs['lin_op'])
+        else:
+            raise ValueError('unknown type passed')
+
+    @expand(product(
+        ['Variable'],
+        [ift.GLSpace(15),
+         ift.RGSpace(64, distances=.789),
+         ift.RGSpace([32, 32], distances=.789)],
+        [4, 78, 23]
+        ))
+    def testGaussian(self, type1, space, seed):
+        model = self.make_model(
+            type1, space_key='s1', space=space, seed=seed)['s1']
+        energy = ift.GaussianEnergy(model)
+        ift.extra.check_value_gradient_consistency(energy)
+
+    @expand(product(
+        ['Variable'],
+        [ift.GLSpace(15),
+         ift.RGSpace(64, distances=.789),
+         ift.RGSpace([32, 32], distances=.789)],
+        [4, 78, 23]
+        ))
+    def testPoissonian(self, type1, space, seed):
+        model = self.make_model(
+            type1, space_key='s1', space=space, seed=seed)['s1']
+        model = ift.PointwiseExponential(model)
+        d = np.random.poisson(120, size = space.shape)
+        d = ift.Field.from_global_data(space, d)
+        energy = ift.PoissonianEnergy(model, d)
+        ift.extra.check_value_gradient_consistency(energy)
+
+    @expand(product(
+        ['Variable'],
+        [ift.GLSpace(15),
+         ift.RGSpace(64, distances=.789),
+         ift.RGSpace([32, 32], distances=.789)],
+        [4, 78, 23]
+        ))
+    def testBernoulli(self, type1, space, seed):
+        model = self.make_model(
+            type1, space_key='s1', space=space, seed=seed)['s1']
+        model = ift.PointwisePositiveTanh(model)
+        d = np.random.binomial(1, 0.1, size = space.shape)
+        d = ift.Field.from_global_data(space, d)
+        energy = ift.BernoulliEnergy(model, d)
+        ift.extra.check_value_gradient_consistency(energy)
+