reno.ops.Bernoulli

class reno.ops.Bernoulli(p, use_p_dist=False, per_timestep=False)

Bases: Distribution

Discrete single event probability (p is probability of eval == 1).

Specifying use_p_dist models p (in pymc) with an additional interpolated distribution with a prior pdf_points set at 0 and 1 with the 1-p, p values.

String notation: Bernoulli(p, use_p_dist=False, per_timestep=False)

Methods

__init__(p[, use_p_dist, per_timestep])	Create a distribution equation part, this is likely being called from a subclass.
astype(dtype)	Returns a symbolic operation to convert the output of this equation to the specified type.
clean_part_repr(part_index)	Get a non-"Scalar()" string version of a particular sub equation part.
clip(min, max)	Returns a symbolic operation for enforcing the output is between the passed min and max.
equal(obj)	Returns a symbolic operation for checking equality with passed object.
eval([t, save, force])	Get the vector of samples pulled from the probability distribution.
find_parts_of_type(search_type[, ...])	Recursively search for all EquationParts in the tree of the specified type.
get_shape()	Get the size of the additional "data" dimension.
get_type()	Get the type of the target output of this equation expression.
is_static()	Convenience shortcut for reno.utils.is_static() - True if this equation doesn't rely on any dynamic values (thus constant), False if it does.
latex(**kwargs)	Construct a string representation of this portion of the equation for use in a latex display.
mean([axis])	Returns a symbolic operation to find the series-wise mean of the array.
not_equal(obj)	Returns a symbolic operation for checking inequality with passed object.
op_latex(**kwargs)
populate(n[, steps, dim])	Generate n x dim samples based on this probability distribution, assigns as a vector/matrix to self.value.
pt(**refs)	Get a pytensor graph representing this piece of an equation.
pt_str(**refs)	Construct a string containing relevant pytensor code for this piece of the equation.
seek_refs([include_ref_types])	Recursively find a list of all References immediately underneath this part.
series_max()	Returns a symbolic operation to find the series-wise maximum of the array.
series_min()	Returns a symbolic operation to find the series-wise minimum of the array.
sum([axis])	Returns a symbolic operation to find the series-wise sum of the array.

Attributes

dtype	The type of each underlying value.
shape	The size of the data dimension, 1 by default.
timeseries	Returns symbolic operation for getting a timeseries view of the data.

Parameters:

• p (float)

• use_p_dist (bool)

• per_timestep (bool)

__annotations__ = {}

__module__ = 'reno.ops'

__repr__()

Return repr(self).

Return type:

str

get_type()

Get the type of the target output of this equation expression.

Prefer using dtype property over directly calling this function.

Override this in any subclass to control how the type gets determined.

A None return means that the type doesn’t matter or shouldn’t influence or override anything upstream.

Similar to shape, this gets computed recursively, used to automatically determine if the value needs to be initialized with a certain numpy type.

Return type:

type

op_latex(**kwargs)

Parameters:

kwargs (dict)

Return type:

str

populate(n, steps=0, dim=1)

Generate n x dim samples based on this probability distribution, assigns as a vector/matrix to self.value.

Parameters:

• n (int) – Number of samples to draw.

• steps (int) – Number of timesteps for which to draw samples for, only relevant if per_timestep is True.

• dim (int) – If > 1, draw samples into a vector of this size for each n.

Return type:

None

pt(**refs)

Get a pytensor graph representing this piece of an equation.

Parameters:

refs (dict[str, TensorVariable])

Return type:

TensorVariable

pt_str(**refs)

Construct a string containing relevant pytensor code for this piece of the equation. This is useful for “compiling” into pymc code.

Parameters:

refs (dict[str, str])

Return type:

str