skdim.id.MADA

class skdim.id.MADA(DM=False)

Intrinsic dimension estimation using the Manifold-Adaptive Dimension Estimation algorithm. [Farahmand2007], [IDHino]

MADA uses a variant of fractal dimension called the local information dimension. MADA considers the first order expansion of the probability mass around the inspection point, and it estimates the local information dimension by using two different radii from the inspection point.

Parameters:

DM : bool

Whether input is a precomputed distance matrix

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