jpt.distributions.univariate.integer

Classes

`IntegerMap`	A mapping of external integers to their internal representation and vice versa.
`IntegerLabelToValueMap`	Maps integer labels to their internal values
`IntegerValueToLabelMap`	Maps internal integer values to integer labels.
`Integer`	Abstract supertype of all domains and distributions

Functions

IntegerType(→ Type[Integer])

Module Contents

class jpt.distributions.univariate.integer.IntegerMap(lmin: int | None = None, lmax: int | None = None)

Bases: jpt.distributions.univariate.distribution.ValueMap

A mapping of external integers to their internal representation and vice versa.

_min

_max

__eq__(other: IntegerMap) → bool

__len__()

__hash__()

__contains__(item)

__iter__()

abstract as_set() → jpt.base.intervals.IntSet

class jpt.distributions.univariate.integer.IntegerLabelToValueMap(lmin: int | None = None, lmax: int | None = None)

Bases: IntegerMap

Maps integer labels to their internal values

property min

property max

__getitem__(label: int) → int

as_set() → jpt.base.intervals.IntSet

class jpt.distributions.univariate.integer.IntegerValueToLabelMap(lmin: int | None = None, lmax: int | None = None)

Bases: IntegerMap

Maps internal integer values to integer labels.

property min

property max

__getitem__(value: int) → int

as_set() → jpt.base.intervals.IntSet

class jpt.distributions.univariate.integer.Integer(**settings)

Bases: jpt.distributions.univariate.Distribution

Abstract supertype of all domains and distributions

values: IntegerLabelToValueMap | None

labels

OPEN_DOMAIN = 'open_domain'

AUTO_DOMAIN = 'auto_domain'

SETTINGS

min() → int | None

max() → int | None

_min() → int | None

_max() → int | None

_params: Dict[int, float] | None = None

to_json: types.FunctionType

classmethod hash()

__add__(other: Integer) → Integer

__neg__() → Integer

property cdf: jpt.base.functions.PiecewiseFunction

add(other: Integer, name: str | None = None) → Integer

classmethod equiv(other: Type[jpt.distributions.univariate.Distribution]) → bool

classmethod type_to_json() → Dict[str, Any]

inst_to_json() → Dict[str, Any]

static type_from_json(data)

classmethod from_json(data: Dict[str, Any]) → Integer

copy()

property probabilities: Dict[int, float]

n_values() → int | None

classmethod value2label(value: int | Iterable[int] | jpt.base.intervals.IntSet | jpt.base.intervals.UnionSet) → int | Iterable[int] | jpt.base.intervals.IntSet | jpt.base.intervals.UnionSet

classmethod label2value(label: int | Iterable[int] | jpt.base.intervals.IntSet | jpt.base.intervals.UnionSet) → int | Iterable[int] | jpt.base.intervals.IntSet | jpt.base.intervals.UnionSet

_sample(n: int) → Iterable[int]

_sample_one() → int

sample(n: int) → Iterable[int]

sample_one() → int

property _pdf: types.FunctionType

property pdf: types.FunctionType

p(labels: int | Iterable[int]) → float

_p(values: int | Iterable[int]) → float

expectation() → float

_expectation() → float

variance() → float

_variance() → float

_k_mpe(k: int | None = None) → Iterable[Tuple[jpt.base.intervals.NumberSet, float]]

Calculate the k most probable explanation states.

Parameters:: k – The number of solutions to generate
Returns:: An list containing a tuple containing the likelihood and state in descending order.

k_mpe(k: int = None) → Iterable[Tuple[jpt.base.intervals.NumberSet, float]]

mpe()

_mpe()

mode()

_mode()

crop(restriction: jpt.base.intervals.NumberSet | int) → Integer

_crop(restriction: jpt.base.intervals.NumberSet | int) → Integer

static merge(distributions: Iterable[Integer], weights: Iterable[numbers.Real]) → Integer

update(dist: Integer, weight: int) → Integer

fit(data: numpy.ndarray, rows: numpy.ndarray = None, col: int = None) → Integer

_fit(data: numpy.ndarray, rows: numpy.ndarray = None, col: int = None) → Integer

_set(params: Dict[int, float] or Iterable[float]) → Integer

set(params: Dict[int, float] or Iterable[float]) → Integer

__eq__(other) → bool

__str__()

__repr__()

infinite() → bool

finite() → bool

_sorted(exhaustive: bool = True, reverse: bool = False, max_items: int = None) → Iterable[Tuple[int, float]]

sorted(exhaustive: bool = True, reverse: bool = False, max_items: int = None) → Iterable[Tuple[int, float]]

_items(exhaustive: bool = False, max_items: int = None) → Iterable[Tuple[int, float]]: Return a list of (probability, value) pairs representing this distribution.

items(exhaustive: bool = True, max_items: int = None) → Iterable[Tuple[int, float]]: Return a list of (probability, label) pairs representing this distribution.

kl_divergence(other: Integer) → float

number_of_parameters() → int

moment(order: int = 1, center: float = 0) → float

Calculate the central moment of the r-th order almost everywhere.

\[\int (x-c)^{r} p(x)\]

Parameters:

order – The order of the moment to calculate
center – The constant to subtract in the basis of the exponent

static wasserstein_distance(d1: Integer, d2: Integer) → float

distance(other: Integer) → float

static jaccard_similarity(d1: Integer, d2: Integer) → float

similarity(other: Integer) → float

plot(engine=None, **kwargs) → Any

Plots the distribution using the given engine.

Parameters:

engine – Can be either one of ["plotly", "matplotlib"], or an instance of a rendering engine subclassing DistributionRendering.
kwargs – The keyword arguments to pass to the engine as defined in the .plot_integer() function of DistributionRendering or its respective subclass defined by engine.

Returns:

the figure object of the plotting engine

jpt.distributions.univariate.integer.IntegerType(name: str, lmin: int | None = None, lmax: int | None = None) → Type[Integer]