skdim.id.TLE¶

class skdim.id.TLE(epsilon=0.0001)[source]¶

Intrinsic dimension estimation using the Tight Local intrinsic dimensionality Estimator algorithm. [Amsaleg2019] [IDRadovanović]

Parameters:	epsilon : float

Methods

`fit`(X[, y, precomputed_knn_arrays, smooth, …])	Fitting method for local ID estimators
`fit_transform`(X[, y, …])	Fit-transform method for local ID estimators
`fit_transform_pw`(X[, …])	Returns an array of pointwise ID estimates by fitting the estimator in kNN of each point.
`get_params`([deep])	Get parameters for this estimator.
`set_params`(**params)	Set the parameters of this estimator.
`transform`([X])	Predict ID after a previous call to self.fit
`transform_pw`([X])	Return an array of pointwise ID estimates after a previous call to self.fit_pw

fit(X, y=None, precomputed_knn_arrays=None, smooth=False, n_neighbors=None, comb='mean', n_jobs=1)¶

Fitting method for local ID estimators

Parameters:

X : {array-like}, shape (n_samples, n_features): The training input samples.
y : dummy parameter to respect the sklearn API
precomputed_knn_arrays : tuple[ np.array (n_samples x n_dims), np.array (n_samples x n_dims) ]: Provide two precomputed arrays: (sorted nearest neighbor distances, sorted nearest neighbor indices)
n_neighbors : int, default=self._N_NEIGHBORS: Number of nearest neighbors to use (ignored when using precomputed_knn)
n_jobs : int: Number of processes
smooth : bool, default = False: Additionally computes a smoothed version of pointwise estimates by taking the ID of a point as the average ID of each point in its neighborhood (self.dimension_pw_) smooth_

Returns:

self (object) – Returns self.

fit_transform(X, y=None, precomputed_knn_arrays=None, smooth=False, n_neighbors=None, comb='mean', n_jobs=1)¶

Fit-transform method for local ID estimators

Parameters:

X : {array-like}, shape (n_samples, n_features): The training input samples.
y : dummy parameter to respect the sklearn API
precomputed_knn_arrays : tuple[ np.array (n_samples x n_dims), np.array (n_samples x n_dims) ]: Provide two precomputed arrays: (sorted nearest neighbor distances, sorted nearest neighbor indices)
n_neighbors : int, default=self._N_NEIGHBORS: Number of nearest neighbors to use (ignored when using precomputed_knn)
n_jobs : int: Number of processes
smooth : bool, default = False: Additionally computes a smoothed version of pointwise estimates by taking the ID of a point as the average ID of each point in its neighborhood (self.dimension_pw_) smooth_

Returns:

dimension_ ({int, float}) – The estimated intrinsic dimension

fit_transform_pw(X, precomputed_knn_arrays=None, smooth=False, n_neighbors=None, n_jobs=1)¶

Returns an array of pointwise ID estimates by fitting the estimator in kNN of each point.

Parameters:

X : np.array (n_samples x n_neighbors)

Dataset to fit

precomputed_knn_arrays : tuple[ np.array (n_samples x n_dims), np.array (n_samples x n_dims) ]

Provide two precomputed arrays: (sorted nearest neighbor distances, sorted nearest neighbor indices)

n_neighbors : int, default=self._N_NEIGHBORS

Number of nearest neighbors to use (ignored when using precomputed_knn).

n_jobs : int

Number of processes

smooth : bool, default = False

Additionally computes a smoothed version of pointwise estimates by: taking the ID of a point as the average ID of each point in its neighborhood (self.dimension_pw_)

smooth_

Returns:

dimension_pw (np.array) – Pointwise ID estimates
dimension_pw_smooth (np.array) – If smooth is True, additionally returns smoothed pointwise ID estimates

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:	deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params (dict) – Parameter names mapped to their values.

set_params(**params)¶

Set the parameters of this estimator.

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

Parameters:	*params : dict* Estimator parameters.
Returns:	self (estimator instance) – Estimator instance.

transform(X=None)¶

Predict ID after a previous call to self.fit

Parameters:	X : Dummy parameter
Returns:	dimension_ ({int, float}) – The estimated ID

transform_pw(X=None)¶

Return an array of pointwise ID estimates after a previous call to self.fit_pw

Parameters:

X : Dummy parameter

Returns:

dimension_pw (np.array) – Pointwise ID estimates
dimension_pw_smooth (np.array) – If self.fit_pw(smooth=True), additionally returns smoothed pointwise ID estimates