ddl.base.UniformDensity

class ddl.base.UniformDensity

Bases: sklearn.base.BaseEstimator, ddl.base.ScoreMixin

Uniform density estimator.

Only the n_features_ attribute needs fitting. This nearly trivial density is used as the underlying density for the IdentityDestructor.

Attributes:

n_features_ : int

Number of features of the training data.

See also

IdentityDestructor

Methods

fit(self, X[, y])	Fit estimator to X.
get_params(self[, deep])	Get parameters for this estimator.
get_support(self)	Get the support of this density (i.e.
sample(self[, n_samples, random_state])	Generate random samples from this density/destructor.
score(self, X[, y])	Return the mean log likelihood (or log(det(Jacobian))).
score_samples(self, X[, y])	Compute log-likelihood (or log(det(Jacobian))) for each sample.
set_params(self, \*\*params)	Set the parameters of this estimator.

create_fitted

__init__(self)

Initialize self. See help(type(self)) for accurate signature.

classmethod create_fitted(n_features)

fit(self, X, y=None)

Fit estimator to X.

Parameters:

X : array-like, shape (n_samples, n_features)

Training data, where n_samples is the number of samples and n_features is the number of features.

y : None, default=None

Not used in the fitting process but kept for compatibility.

Returns:

self : estimator

Returns the instance itself.

get_params(self, deep=True)

Get parameters for this estimator.

Parameters:

deep : boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

get_support(self)

Get the support of this density (i.e. the positive density region).

Returns:

support : array-like, shape (2,) or shape (n_features, 2)

If shape is (2, ), then support[0] is the minimum and support[1] is the maximum for all features. If shape is (n_features, 2), then each feature’s support (which could be different for each feature) is given similar to the first case.

sample(self, n_samples=1, random_state=None)

Generate random samples from this density/destructor.

Parameters:

n_samples : int, default=1

Number of samples to generate. Defaults to 1.

random_state : int, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by numpy.random.

Returns:

X : array, shape (n_samples, n_features)

Randomly generated sample.

score(self, X, y=None)

Return the mean log likelihood (or log(det(Jacobian))).

Parameters:

X : array-like, shape (n_samples, n_features)

New data, where n_samples is the number of samples and n_features is the number of features.

y : None, default=None

Not used but kept for compatibility.

Returns:

log_likelihood : float

Mean log likelihood data points in X.

score_samples(self, X, y=None)

Compute log-likelihood (or log(det(Jacobian))) for each sample.

Parameters:

X : array-like, shape (n_samples, n_features)

New data, where n_samples is the number of samples and n_features is the number of features.

y : None, default=None

Not used but kept for compatibility.

Returns:

log_likelihood : array, shape (n_samples,)

Log likelihood of each data point in X.

set_params(self, **params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:

self