diff --git a/nifty6/minimization/metric_gaussian_kl.py b/nifty6/minimization/metric_gaussian_kl.py index e665efd2c407096fc5398334652bff6fdaab6566..ca09b97478639e59d5671810607655f80f259661 100644 --- a/nifty6/minimization/metric_gaussian_kl.py +++ b/nifty6/minimization/metric_gaussian_kl.py @@ -79,7 +79,7 @@ class MetricGaussianKL(Energy): def __init__(self, mean, hamiltonian, n_samples, constants=[], point_estimates=[], mirror_samples=False, - napprox=0, _samples=None, lh_sampling_dtype = np.float): + napprox=0, _samples=None, lh_sampling_dtype=np.float64): super(MetricGaussianKL, self).__init__(mean) if not isinstance(hamiltonian, StandardHamiltonian): @@ -101,7 +101,7 @@ class MetricGaussianKL(Energy): if napprox > 1: met._approximation = makeOp(approximation2endo(met, napprox)) _samples = tuple(met.draw_sample(from_inverse=True, - dtype = lh_sampling_dtype) + dtype=lh_sampling_dtype) for _ in range(n_samples)) if mirror_samples: _samples += tuple(-s for s in _samples) diff --git a/nifty6/operators/sampling_enabler.py b/nifty6/operators/sampling_enabler.py index 3797f11197940a7214e16943dc13bd34629891ca..e78846d94630e6e141e26b73988e778b3f06cab1 100644 --- a/nifty6/operators/sampling_enabler.py +++ b/nifty6/operators/sampling_enabler.py @@ -71,7 +71,7 @@ class SamplingEnabler(EndomorphicOperator): else: s = self._prior.draw_sample(from_inverse=True) sp = self._prior(s) - nj = self._likelihood.draw_sample(dtype = dtype) + nj = self._likelihood.draw_sample(dtype=dtype) energy = QuadraticEnergy(s, self._op, sp + nj, _grad=self._likelihood(s) - nj) inverter = ConjugateGradient(self._ic)