skdim.id.MiND_ML¶

class skdim.id.MiND_ML(k=20, D=10, ver='MLk')[source]¶

Intrinsic dimension estimation using the MiND_MLk and MiND_MLi algorithms. [Rozza2012] [IDJohnsson]

Parameters:	k : int, default=20 Neighborhood parameter for ver=’MLk’ or ver=’MLi’. ver : str ‘MLk’ or ‘MLi’. See the reference paper

Methods

`fit`(X[, y])	A reference implementation of a fitting function.
`fit_pw`(X[, precomputed_knn, smooth, …])	Creates an array of pointwise ID estimates (self.dimension_pw_) by fitting the estimator in kNN of each point.
`fit_transform`(X[, y])	Fit estimator and return ID
`fit_transform_pw`(X[, precomputed_knn, …])	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 dimension 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)[source]¶

A reference implementation of a fitting function.

Parameters:	X : {array-like}, shape (n_samples, n_features) The training input samples. y : dummy parameter to respect the sklearn API
Returns:	self (object) – Returns self.

fit_pw(X, precomputed_knn=None, smooth=False, n_neighbors=100, n_jobs=1)¶

Creates an array of pointwise ID estimates (self.dimension_pw_) by fitting the estimator in kNN of each point.

Parameters:

X : np.array (n_samples x n_neighbors)

Dataset to fit

precomputed_knn : np.array (n_samples x n_dims)

An array of precomputed (sorted) nearest neighbor indices

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)¶

Fit estimator and return ID

Parameters:	X : {array-like}, shape (n_samples, n_features) The training input samples.
Returns:	dimension_ ({int, float}) – The estimated intrinsic dimension

fit_transform_pw(X, precomputed_knn=None, smooth=False, n_neighbors=100, 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 : bool

An array of precomputed (sorted) nearest neighbor indices

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 with dtype {int, float}) – Pointwise ID estimates
dimension_pw_smooth_ (np.array with dtype float) – Smoothed pointwise ID estimates returned if self.fit_pw(smooth=True)

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 dimension 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 with dtype {int, float}) – Pointwise ID estimates
dimension_pw_smooth_ (np.array with dtype float) – Smoothed pointwise ID estimates returned if self.fit_pw(smooth=True)