skdim.id.MLE.fit_transform_pw

MLE.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