sklearn sk0.23-0.3.1 · OCaml Package

val get_py : string -> Py.Object.t

Get an attribute of this module as a Py.Object.t. This is useful to pass a Python function to another function.

module BallTree : sig ... end

module DistanceMetric : sig ... end

module KDTree : sig ... end

module KNeighborsClassifier : sig ... end

module KNeighborsRegressor : sig ... end

module KNeighborsTransformer : sig ... end

module KernelDensity : sig ... end

module LocalOutlierFactor : sig ... end

module NearestCentroid : sig ... end

module NearestNeighbors : sig ... end

module NeighborhoodComponentsAnalysis : sig ... end

module RadiusNeighborsClassifier : sig ... end

module RadiusNeighborsRegressor : sig ... end

module RadiusNeighborsTransformer : sig ... end

val kneighbors_graph : 
  ?mode:[ `Connectivity | `Distance ] ->
  ?metric:string ->
  ?p:int ->
  ?metric_params:Dict.t ->
  ?include_self:[ `Auto | `Bool of bool ] ->
  ?n_jobs:int ->
  x:[ `BallTree of Py.Object.t | `Arr of [> `ArrayLike ] Np.Obj.t ] ->
  n_neighbors:int ->
  unit ->
  [ `ArrayLike | `Object | `Spmatrix ] Np.Obj.t

Computes the (weighted) graph of k-Neighbors for points in X

Read more in the :ref:`User Guide <unsupervised_neighbors>`.

Parameters ---------- X : array-like of shape (n_samples, n_features) or BallTree Sample data, in the form of a numpy array or a precomputed :class:`BallTree`.

n_neighbors : int Number of neighbors for each sample.

mode : 'connectivity', 'distance', default='connectivity' Type of returned matrix: 'connectivity' will return the connectivity matrix with ones and zeros, and 'distance' will return the distances between neighbors according to the given metric.

metric : str, default='minkowski' The distance metric used to calculate the k-Neighbors for each sample point. The DistanceMetric class gives a list of available metrics. The default distance is 'euclidean' ('minkowski' metric with the p param equal to 2.)

p : int, default=2 Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.

metric_params : dict, default=None additional keyword arguments for the metric function.

include_self : bool or 'auto', default=False Whether or not to mark each sample as the first nearest neighbor to itself. If 'auto', then True is used for mode='connectivity' and False for mode='distance'.

n_jobs : int, default=None The number of parallel jobs to run for neighbors search. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details.

Returns ------- A : sparse matrix of shape (n_samples, n_samples) Graph where Ai, j is assigned the weight of edge that connects i to j. The matrix is of CSR format.

Examples -------- >>> X = [0], [3], [1] >>> from sklearn.neighbors import kneighbors_graph >>> A = kneighbors_graph(X, 2, mode='connectivity', include_self=True) >>> A.toarray() array([1., 0., 1.], [0., 1., 1.], [1., 0., 1.])

See also -------- radius_neighbors_graph

val radius_neighbors_graph : 
  ?mode:[ `Connectivity | `Distance ] ->
  ?metric:string ->
  ?p:int ->
  ?metric_params:Dict.t ->
  ?include_self:[ `Auto | `Bool of bool ] ->
  ?n_jobs:int ->
  x:[ `BallTree of Py.Object.t | `Arr of [> `ArrayLike ] Np.Obj.t ] ->
  radius:float ->
  unit ->
  [ `ArrayLike | `Object | `Spmatrix ] Np.Obj.t

Computes the (weighted) graph of Neighbors for points in X

Neighborhoods are restricted the points at a distance lower than radius.

Read more in the :ref:`User Guide <unsupervised_neighbors>`.

Parameters ---------- X : array-like of shape (n_samples, n_features) or BallTree Sample data, in the form of a numpy array or a precomputed :class:`BallTree`.

radius : float Radius of neighborhoods.

mode : 'connectivity', 'distance', default='connectivity' Type of returned matrix: 'connectivity' will return the connectivity matrix with ones and zeros, and 'distance' will return the distances between neighbors according to the given metric.

metric : str, default='minkowski' The distance metric used to calculate the neighbors within a given radius for each sample point. The DistanceMetric class gives a list of available metrics. The default distance is 'euclidean' ('minkowski' metric with the param equal to 2.)

p : int, default=2 Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.

metric_params : dict, default=None additional keyword arguments for the metric function.

include_self : bool or 'auto', default=False Whether or not to mark each sample as the first nearest neighbor to itself. If 'auto', then True is used for mode='connectivity' and False for mode='distance'.

n_jobs : int, default=None The number of parallel jobs to run for neighbors search. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details.

Returns ------- A : sparse matrix of shape (n_samples, n_samples) Graph where Ai, j is assigned the weight of edge that connects i to j. The matrix is of CSR format.

Examples -------- >>> X = [0], [3], [1] >>> from sklearn.neighbors import radius_neighbors_graph >>> A = radius_neighbors_graph(X, 1.5, mode='connectivity', ... include_self=True) >>> A.toarray() array([1., 0., 1.], [0., 1., 0.], [1., 0., 1.])

package sklearn