Merge branch 'fisher_tests' into fix_complex_samples

test/test_operators/test_fisher_metric.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) 2013-2020 Max-Planck-Society
+#
+# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
+
+import numpy as np
+import pytest
+
+import nifty6 as ift
+
+from ..common import list2fixture, setup_function, teardown_function
+
+spaces = [ift.GLSpace(5),
+          ift.MultiDomain.make({'': ift.RGSpace(5, distances=.789)}),
+          (ift.RGSpace(3, distances=.789), ift.UnstructuredDomain(2))]
+pmp = pytest.mark.parametrize
+field = list2fixture([ift.from_random(sp, 'normal') for sp in spaces] +
+        [ift.from_random(sp, 'normal', dtype=np.complex128) for sp in spaces])
+
+Nsamp = 2000
+np.random.seed(42)
+
+def _to_array(d):
+    if isinstance(d, np.ndarray):
+        return d
+    assert isinstance(d, dict)
+    return np.concatenate(list(d.values()))
+
+def energy_tester(pos, get_noisy_data, energy_initializer):
+    domain = pos.domain
+    test_vec = ift.from_random(domain, 'normal')
+    results = []
+    lin = ift.Linearization.make_var(pos)
+    for i in range(Nsamp):
+        data = get_noisy_data(pos)
+        energy = energy_initializer(data)
+        grad = energy(lin).jac.adjoint(ift.full(energy.target, 1.))
+        results.append(_to_array((grad*grad.s_vdot(test_vec)).val))
+    res = np.mean(np.array(results), axis=0)
+    std = np.std(np.array(results), axis=0)/np.sqrt(Nsamp)
+    energy = energy_initializer(data)
+    lin = ift.Linearization.make_var(pos, want_metric=True)
+    res2 = _to_array(energy(lin).metric(test_vec).val)
+    np.testing.assert_allclose(res/std, res2/std, atol=6)
+
+def test_GaussianEnergy(field):
+    dtype = field.dtype
+    icov = ift.from_random(field.domain, 'normal')**2
+    icov = ift.makeOp(icov)
+    get_noisy_data = lambda mean : mean + icov.draw_sample_with_dtype(
+            from_inverse=True, dtype=dtype)
+    E_init = lambda mean : ift.GaussianEnergy(mean=mean,
+            inverse_covariance=icov)
+    energy_tester(field, get_noisy_data, E_init)
+
+def test_PoissonEnergy(field):
+    if not isinstance(field, ift.Field):
+        return
+    if np.iscomplexobj(field.val):
+        return
+    def get_noisy_data(mean):
+        return ift.makeField(mean.domain, np.random.poisson(mean.val))
+    lam = 10*(field**2).clip(0.1,None) # make rate positive and high enough to avoid bad statistic
+    E_init = lambda mean : ift.PoissonianEnergy(mean)
+    energy_tester(lam, get_noisy_data, E_init)
+