Functional CPD

class pgmpy.factors.hybrid.FunctionalCPD.FunctionalCPD(variable, fn, parents=[])

Defines a Functional CPD.

Functional CPD can represent any arbitrary conditional probability distribution where the distribution to represented is defined by function (input as parameter) which calls pyro.sample function.

Parameters:

• variable (str) – Name of the variable for which this CPD is defined.

• fn (callable) – A lambda function that takes a dictionary of parent variable values and returns a sampled value for the variable by calling pyro.sample.

• parents (list[str], optional) – List of parent variable names (default is None for no parents).

Examples

# For P(X3| X1, X2) = N(0.2x1 + 0.3x2 + 1.0; 1), we can write

>>> from pgmpy.factors.hybrid import FunctionalCPD
>>> import pyro.distributions as dist
>>> cpd = FunctionalCPD(
...    variable="x3",
...    fn=lambda parent_sample: dist.Normal(
...        0.2 * parent_sample["x1"] + 0.3 * parent_sample["x2"] + 1.0, 1),
...    parents=["x1", "x2"])
>>> cpd.variable
'x3'
>>> cpd.parents
['x1', 'x2']

sample(n_samples=100, parent_sample=None)

Simulates a value for the variable based on its CPD.

Parameters:

n_samples: int, (default: 100)

The number of samples to generate.

parent_sample: pandas.DataFrame, optional

A DataFrame where each column represents a parent variable and rows are samples.

Returns:

sampled_values: numpy.ndarray

Array of sampled values for the variable.

Examples

>>> from pgmpy.factors.hybrid import FunctionalCPD
>>> import pyro.distributions as dist
>>> cpd = FunctionalCPD(
...    variable="x3",
...    fn=lambda parent_sample: dist.Normal(
...        1.0 + 0.2 * parent_sample["x1"] + 0.3 * parent_sample["x2"], 1),
...    parents=["x1", "x2"])

>>> parent_samples = pd.DataFrame({'x1' : [5, 10], 'x2' : [1, -1]})
>>> cpd.sample(2, parent_samples)