testing successfull

src/library/variational_models.py

@@ -70,8 +70,8 @@ class MeanFieldVI:
         else:
             pos['std'] = full(Flat.target, initial_sig)
         pos = MultiField.from_dict(pos)
-        op = hamiltonian(self._generator) + self._entropy
-        self._KL = StochasticEnergyAdapter.make(pos, op, ['latent',], n_samples,
+        self._op = hamiltonian(self._generator) + self._entropy
+        self._KL = StochasticEnergyAdapter.make(pos, self._op, ['latent',], n_samples,
                                     mirror_samples, nanisinf=nanisinf, comm=comm)
         self._samdom = latent.domain
 
@@ -139,8 +139,8 @@ class FullCovarianceVI:
         pos = MultiField.from_dict(
                 {'mean': Flat(position),
                  'cov': LT.adjoint(makeField(mat_space, diag_tri))})
-        op = hamiltonian(self._generator) + self._entropy
-        self._KL = StochasticEnergyAdapter.make(pos, op, ['latent',], n_samples,
+        self._op = hamiltonian(self._generator) + self._entropy
+        self._KL = StochasticEnergyAdapter.make(pos, self._op, ['latent',], n_samples,
                                     mirror_samples, nanisinf=nanisinf, comm=comm)
         self._mean = Flat.adjoint @ mean
         self._samdom = lat.domain

test/test_kl.py

@@ -103,3 +103,39 @@ def test_kl(constants, point_estimates, mirror_samples, mf, geo):
         else:
             res0 = klpure.gradient[kk].val
         assert_allclose(res0, res1)
+
+@pmp('mirror_samples', (True, False))
+@pmp('fc',(True, False))
+def test_ParametricVI(mirror_samples, fc):
+    dom = ift.RGSpace((12,), (2.12))
+    op = ift.HarmonicSmoothingOperator(dom, 3)
+    op = ift.ducktape(dom, None, 'a')*(op.ducktape('b'))
+    lh = ift.GaussianEnergy(domain=op.target, sampling_dtype=np.float64) @ op
+    ic = ift.GradientNormController(iteration_limit=5)
+    ic.enable_logging()
+    h = ift.StandardHamiltonian(lh, ic_samp=ic)
+    initial_mean = ift.from_random(h.domain, 'normal')
+    nsamps = 1000
+    if fc:
+        model = ift.library.variational_models.FullCovarianceVI(initial_mean, h, nsamps, mirror_samples, initial_sig=0.01)
+    else:
+        model = ift.library.variational_models.MeanFieldVI(initial_mean, h, nsamps, mirror_samples, initial_sig=0.01)
+    kl = model._KL
+    expected_nsamps = 2*nsamps if mirror_samples else nsamps
+    myassert(len(tuple(kl._local_ops)) == expected_nsamps)
+
+
+    true_val = []
+    for i in range(expected_nsamps):
+        lat_rnd = ift.from_random(model._op.domain['latent'])
+        samp = kl.position.to_dict()
+        samp['latent'] = lat_rnd
+        samp = ift.MultiField.from_dict(samp)
+        true_val.append(model._op(samp))
+    true_val = sum(true_val)/expected_nsamps
+    assert_allclose(true_val.val, kl.value, rtol=0.1)
+
+
+
+
+