skdim.id.lPCA.fit_transform_pw

lPCA.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)