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5 changes: 5 additions & 0 deletions bayesflow/adapters/__init__.py
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"""
A collection of :py:class:`~bayesflow.adapters.Adapter` transforms, which tell BayesFlow how to interpret your
:py:class:`~bayesflow.simulators.Simulator` output and plug it into neural networks for training and inference.
"""

from . import transforms
from .adapter import Adapter

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5 changes: 5 additions & 0 deletions bayesflow/approximators/__init__.py
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"""
A collection of :py:class:`~bayesflow.approximators.Approximator`\ s, which embody the inference task and the
neural network components used to perform it.
"""

from .approximator import Approximator
from .continuous_approximator import ContinuousApproximator
from .point_approximator import PointApproximator
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6 changes: 6 additions & 0 deletions bayesflow/datasets/__init__.py
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"""
A collection of `keras.utils.PyDataset <https://keras.io/api/utils/python_utils/#pydataset-class>`__\ s, which
wrap your data-generating process (i.e., your :py:class:`~bayesflow.simulators.Simulator`).
What type of Dataset you use determines the effective training strategy (e.g., online or offline).
"""

from .offline_dataset import OfflineDataset
from .online_dataset import OnlineDataset
from .disk_dataset import DiskDataset
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4 changes: 4 additions & 0 deletions bayesflow/diagnostics/__init__.py
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"""
A collection of plotting utilities and metrics for evaluating trained :py:class:`~bayesflow.workflows.Workflow`\ s.
"""

from .metrics import root_mean_squared_error, calibration_error, posterior_contraction

from .plots import (
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6 changes: 6 additions & 0 deletions bayesflow/distributions/__init__.py
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"""
A collection of :py:class:`~bayesflow.distributions.Distribution`\ s,
which represent the latent space for :py:class:`~bayesflow.networks.InferenceNetwork`\ s
or the summary space of :py:class:`~bayesflow.networks.SummaryNetwork`\ s.
"""

from .distribution import Distribution
from .diagonal_normal import DiagonalNormal
from .diagonal_student_t import DiagonalStudentT
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4 changes: 4 additions & 0 deletions bayesflow/experimental/__init__.py
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"""
Unstable or largely untested networks. Proceed with caution.
"""

from .cif import CIF
from .continuous_time_consistency_model import ContinuousTimeConsistencyModel
from .free_form_flow import FreeFormFlow
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5 changes: 5 additions & 0 deletions bayesflow/metrics/__init__.py
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"""
A collection of `keras.Metric <https://keras.io/api/metrics/base_metric/#metric-class>`__\ s for evaluating the
performance of models.
"""

from . import functional
from .maximum_mean_discrepancy import MaximumMeanDiscrepancy
from .root_mean_squard_error import RootMeanSquaredError
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4 changes: 4 additions & 0 deletions bayesflow/networks/__init__.py
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"""
A rich collection of neural network architectures for use in :py:class:`~bayesflow.approximators.Approximator`\ s.
"""

from .consistency_models import ConsistencyModel
from .coupling_flow import CouplingFlow
from .deep_set import DeepSet
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8 changes: 8 additions & 0 deletions bayesflow/simulators/__init__.py
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"""
This module provides the :py:class:`~bayesflow.simulators.Simulator`, which is the base implementation of a
generative mathematical model, or data generating process.
Its primary function is to sample data with the :py:meth:`~bayesflow.simulators.Simulator.sample` method.
The module also contains several other kinds of Simulators, as well as the function
:py:func:`~bayesflow.simulators.make_simulator` to facilitate easy implementation.
"""

from .sequential_simulator import SequentialSimulator
from .hierarchical_simulator import HierarchicalSimulator
from .lambda_simulator import LambdaSimulator
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2 changes: 1 addition & 1 deletion bayesflow/types/__init__.py
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"""
Custom types.
Custom types used for type annotations.

.. currentmodule:: bayesflow.types

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4 changes: 4 additions & 0 deletions bayesflow/utils/__init__.py
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"""
A collection of utility functions, mostly used for internal purposes.
"""

from . import (
keras_utils,
logging,
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5 changes: 5 additions & 0 deletions bayesflow/workflows/__init__.py
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"""
Provides :py:class:`~bayesflow.workflows.BasicWorkflow`, a high-level interface for working on typical BayesFlow
applications without having to worry about the internals.
"""

from .basic_workflow import BasicWorkflow

from ..utils._docs import _add_imports_to_all
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