skdim.id.KNN¶

class skdim.id.KNN(k=None, ps=None, M=1, gamma=2)[source]¶

Intrinsic dimension estimation using the kNN algorithm. [Carter2010] [IDJohnsson]

This is a simplified version of the kNN dimension estimation method described by Carter et al. (2010), the difference being that block bootstrapping is not used.

Parameters:	X : 2D numeric array A 2D data set with each row describing a data point. k : int Number of distances to neighbors used at a time. ps : 1D numeric array Vector with sample sizes; each sample size has to be larger than k and smaller than nrow(data). M : int, default=1 Number of bootstrap samples for each sample size. gamma : int, default=2 Weighting constant.

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. :param X: The training input samples. :type X: {array-like}, shape (n_samples, n_features) :param y: :type y: dummy parameter to respect the sklearn API

Returns:	self (object) – Returns self. self.dimension_ (float) – The estimated intrinsic dimension self.residual_ (float) – Residuals

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)