def record(f, *args, **kwargs):
"""Records execution to a tape."""
name = kwargs.get("name")
output = traceable(f)(*args, **kwargs)
if name:
tape_data[name] = output
return output
def _condition(f, *args, **kwargs):
"""Sets random variable values to its aligned value."""
name = kwargs.get("name")
if name in model_kwargs:
kwargs["value"] = model_kwargs[name]
return traceable(f)(*args, **kwargs)
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Automatically generated random variables."""
from edward2.trace import traceable
import scipy.stats
# Note a vanilla Edward2-like PPL in SciPy would introduce a RandomVariable
# abstraction: it wraps SciPy frozen distributions and calls `rvs` to associate
# the RandomVariable with a sampled value. SciPy distributions already enable
# parameters as input to `rvs`. Therefore instead of introducing a new
# abstraction, we just wrap `rvs`. This enables the same manipulations.
__all__ = []
_globals = globals()
for candidate_name in sorted(dir(scipy.stats)):
candidate = getattr(scipy.stats, candidate_name)
if isinstance(
candidate,
(
scipy.stats._multivariate.multi_rv_generic, # pylint: disable=protected-access
scipy.stats.rv_continuous,
scipy.stats.rv_discrete,
scipy.stats.rv_histogram)):
candidate.rvs = traceable(candidate.rvs)
_globals[candidate_name] = candidate
__all__.append(candidate_name)
_HAS_DYNAMIC_ATTRIBUTES = True