probabilistic_model.probabilistic_circuit.jax.input_layer

probabilistic_model.probabilistic_circuit.jax.input_layer#

Classes#

`ContinuousLayer`	Abstract base class for continuous univariate input units.
`ContinuousLayerWithFiniteSupport`	Abstract class for continuous univariate input units with finite support.
`DiracDeltaLayer`	A layer that represents Dirac delta distributions over a single variable.

Module Contents#

class probabilistic_model.probabilistic_circuit.jax.input_layer.ContinuousLayer(variable: int)#

Bases: probabilistic_model.probabilistic_circuit.jax.inner_layer.InputLayer, abc.ABC

Abstract base class for continuous univariate input units.

class probabilistic_model.probabilistic_circuit.jax.input_layer.ContinuousLayerWithFiniteSupport(variable: int, interval: jax.Array)#

Bases: ContinuousLayer, abc.ABC

Abstract class for continuous univariate input units with finite support.

interval: jax.Array#: The interval of the distribution as a array of shape (num_nodes, 2). The first column contains the lower bounds and the second column the upper bounds. The intervals are treated as open intervals (>/< comparator).

property lower: jax.Array#

property upper: jax.Array#

left_included_condition(x: jax.Array) → jax.Array#: Check if x is included in the left bound of the intervals. :param x: The data :return: A boolean array of shape (#x, #nodes)

right_included_condition(x: jax.Array) → jax.Array#: Check if x is included in the right bound of the intervals. :param x: The data :return: A boolean array of shape (#x, #nodes)

included_condition(x: jax.Array) → jax.Array#: Check if x is included in the interval. :param x: The data :return: A boolean array of shape (#x, #nodes)

to_json() → Dict[str, Any]#

__deepcopy__(memo=None)#

Create a deep copy of the layer.

Parameters:: memo – A dictionary that is used to keep track of objects that have already been copied.

class probabilistic_model.probabilistic_circuit.jax.input_layer.DiracDeltaLayer(variable_index, location, density_cap)#

Bases: ContinuousLayer

A layer that represents Dirac delta distributions over a single variable.

location: jax.Array#: The locations of the Dirac delta distributions.

density_cap: jax.Array#: The density caps of the Dirac delta distributions. This value will be used to replace infinity in likelihoods.

validate()#: Validate the parameters and their layouts.

property number_of_nodes#

Returns:: The number of nodes in the layer.

log_likelihood_of_nodes(x: jax.Array) → jax.Array#: Vectorized version of log_likelihood_of_nodes_single()

log_likelihood_of_nodes_single(x: jax.Array) → jax.Array#

Calculate the log-likelihood of the distribution.

Parameters:: x – The input vector.
Returns:: The log-likelihood of every node in the layer for x.

classmethod nx_classes() → typing_extensions.Tuple[typing_extensions.Type, Ellipsis]#

Returns:: The tuple of matching classes of the layer in the probabilistic_model.probabilistic_circuit.rx package.

classmethod create_layer_from_nodes_with_same_type_and_scope(nodes: List[probabilistic_model.probabilistic_circuit.rx.probabilistic_circuit.UnivariateContinuousLeaf], child_layers: List[probabilistic_model.probabilistic_circuit.jax.inner_layer.NXConverterLayer], progress_bar: bool = True) → probabilistic_model.probabilistic_circuit.jax.inner_layer.NXConverterLayer#: Create a layer from a list of nodes with the same type and scope.

to_json() → Dict[str, Any]#

classmethod _from_json(data: Dict[str, Any]) → typing_extensions.Self#

Create a variable from a json dict. This method is called from the from_json method after the correct subclass is determined and should be overwritten by the respective subclass.

Parameters:: data – The json dict
Returns:: The deserialized object

to_nx(variables: sortedcontainers.SortedSet[random_events.variable.Variable], result: probabilistic_model.probabilistic_circuit.rx.probabilistic_circuit.ProbabilisticCircuit, progress_bar: typing_extensions.Optional[tqdm.tqdm] = None) → List[probabilistic_model.probabilistic_circuit.rx.probabilistic_circuit.Unit]#

Convert the layer to a networkx circuit. For every node in this circuit, a corresponding node in the networkx circuit is created. The nodes all belong to the same circuit.

Parameters:

variables – The variables of the circuit.
result – The resulting circuit to write into
progress_bar – A progress bar to show the progress.

Returns:

The nodes of the networkx circuit.