Remove ParametricGaussianKL

src/minimization/kl_energies.py

@@ -521,40 +521,3 @@ def GeoMetricKL(mean, hamiltonian, n_samples, minimizer_samp, mirror_samples,
     mean, hamiltonian = _reduce_by_keys(mean, hamiltonian, constants)
     return _SampledKLEnergy(mean, hamiltonian, sampler.n_eff_samples, False,
                             comm, local_samples, nanisinf)
-
-def ParametricGaussianKL(variational_parameters, hamiltonian,
-        variational_model, n_samples, mirror_samples, comm=None,
-        nanisinf=False):
-    """Provide the sampled Kullback-Leibler divergence between a distribution
-    and a Parametric Gaussian.
-
-    FIXME
-    """
-    if not isinstance(hamiltonian, StandardHamiltonian):
-        raise TypeError
-    if hamiltonian.domain is not variational_model.generator.target:
-        raise ValueError
-    if not isinstance(n_samples, int):
-        raise TypeError
-    if not isinstance(mirror_samples, bool):
-        raise TypeError
-
-    from ..sugar import full, from_random
-    full_model = hamiltonian(variational_model.generator) + variational_model.entropy
-
-    local_samples = []
-    sseq = random.spawn_sseq(n_samples)
-    # FIXME dirty trick, many multiplications with zero
-    DirtyMaskDict = full(variational_model.generator.domain, 0.0).to_dict()
-    DirtyMaskDict["latent"] = full(
-        variational_model.generator.domain["latent"], 1.0
-    )
-    DirtyMask = MultiField.from_dict(DirtyMaskDict)
-    for i in range(*_get_lo_hi(comm, n_samples)):
-        with random.Context(sseq[i]):
-            s = DirtyMask * from_random(variational_model.generator.domain)
-            local_samples.append(s)
-    local_samples = tuple(local_samples)
-
-    return _SampledKLEnergy(variational_parameters, full_model, n_samples,
-            mirror_samples, comm, local_samples, nanisinf)