skdim.id.MLE.fit

MLE.fit(X, y=None, precomputed_knn_arrays=None, smooth=False, n_neighbors=None, comb='mle', n_jobs=1)

Fitting method for local ID estimators :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 :param precomputed_knn_arrays: Provide two precomputed arrays: (sorted nearest neighbor distances, sorted nearest neighbor indices) :type precomputed_knn_arrays: tuple[ np.array (n_samples x n_dims), np.array (n_samples x n_dims) ] :param n_neighbors: Number of nearest neighbors to use (ignored when using precomputed_knn) :type n_neighbors: int, default=self._N_NEIGHBORS :param n_jobs: Number of processes :type n_jobs: int :param smooth: 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.