ddl.base
.IdentityDestructor¶
-
class
ddl.base.
IdentityDestructor
[source]¶ Bases:
ddl.base.BaseDensityDestructor
Identity destructor/transform.
This assumes a canonical uniform density on the unit hypercube and has a domain of [0, 1].
Attributes: - density_ : estimator
Fitted underlying density.
See also
Methods
fit
(self, X[, y, density_fit_params])[Placeholder]. fit_from_density
(self, density)[Placeholder]. fit_transform
(self, X[, y])Fit to data, then transform it. get_domain
(self)Get the domain of this destructor. get_params
(self[, deep])Get parameters for this estimator. inverse_transform
(self, X[, y, copy])[Placeholder]. 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. transform
(self, X[, y, copy])[Placeholder]. create_fitted -
__init__
(self, /, *args, **kwargs)¶ Initialize self. See help(type(self)) for accurate signature.
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fit
(self, X, y=None, density_fit_params=None)¶ [Placeholder].
Parameters: - X :
- y :
- density_fit_params :
Returns: - obj : object
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fit_from_density
(self, density)¶ [Placeholder].
Parameters: - density :
Returns: - obj : object
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fit_transform
(self, X, y=None, **fit_params)¶ Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Parameters: - X : numpy array of shape [n_samples, n_features]
Training set.
- y : numpy array of shape [n_samples]
Target values.
Returns: - X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
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get_domain
(self)[source]¶ Get the domain of this destructor.
Returns: - domain : array-like, shape (2,) or shape (n_features, 2)
If shape is (2, ), then
domain[0]
is the minimum anddomain[1]
is the maximum for all features. If shape is (n_features, 2), then each feature’s domain (which could be different for each feature) is given similar to the first case.
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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.
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inverse_transform
(self, X, y=None, copy=True)[source]¶ [Placeholder].
Parameters: - X :
- y :
- copy :
Returns: - obj : object
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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 theRandomState
instance used bynumpy.random
.
Returns: - X : array, shape (n_samples, n_features)
Randomly generated sample.
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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.
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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.
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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