skdim.id.ESS.fit

ESS.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.