package sklearn

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sklearn
- Sklearn

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type tag = [

| `RadiusNeighborsClassifier

]

type t =
  [ `BaseEstimator
  | `ClassifierMixin
  | `MultiOutputMixin
  | `NeighborsBase
  | `Object
  | `RadiusNeighborsClassifier
  | `RadiusNeighborsMixin
  | `SupervisedIntegerMixin ]
    Obj.t

val of_pyobject : Py.Object.t -> t

val to_pyobject : [> tag ] Obj.t -> Py.Object.t

val as_multi_output : t -> [ `MultiOutputMixin ] Obj.t

val as_radius_neighbors : t -> [ `RadiusNeighborsMixin ] Obj.t

val as_estimator : t -> [ `BaseEstimator ] Obj.t

val as_neighbors : t -> [ `NeighborsBase ] Obj.t

val as_classifier : t -> [ `ClassifierMixin ] Obj.t

val as_supervised_integer : t -> [ `SupervisedIntegerMixin ] Obj.t

val create : 
  ?radius:float ->
  ?weights:[ `Uniform | `Callable of Py.Object.t | `Distance ] ->
  ?algorithm:[ `Auto | `Ball_tree | `Kd_tree | `Brute ] ->
  ?leaf_size:int ->
  ?p:int ->
  ?metric:[ `S of string | `Callable of Py.Object.t ] ->
  ?outlier_label:[ `Manual_label of Py.Object.t | `Most_frequent ] ->
  ?metric_params:Dict.t ->
  ?n_jobs:int ->
  ?kwargs:(string * Py.Object.t) list ->
  unit ->
  t

Classifier implementing a vote among neighbors within a given radius

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

Parameters ---------- radius : float, default=1.0 Range of parameter space to use by default for :meth:`radius_neighbors` queries.

weights : 'uniform', 'distance' or callable, default='uniform' weight function used in prediction. Possible values:

'uniform' : uniform weights. All points in each neighborhood are weighted equally.
'distance' : weight points by the inverse of their distance. in this case, closer neighbors of a query point will have a greater influence than neighbors which are further away.
callable : a user-defined function which accepts an array of distances, and returns an array of the same shape containing the weights.

Uniform weights are used by default.

algorithm : 'auto', 'ball_tree', 'kd_tree', 'brute', default='auto' Algorithm used to compute the nearest neighbors:

'ball_tree' will use :class:`BallTree`
'kd_tree' will use :class:`KDTree`
'brute' will use a brute-force search.
'auto' will attempt to decide the most appropriate algorithm based on the values passed to :meth:`fit` method.

Note: fitting on sparse input will override the setting of this parameter, using brute force.

leaf_size : int, default=30 Leaf size passed to BallTree or KDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem.

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 : str or callable, default='minkowski' the distance metric to use for the tree. The default metric is minkowski, and with p=2 is equivalent to the standard Euclidean metric. See the documentation of :class:`DistanceMetric` for a list of available metrics. If metric is 'precomputed', X is assumed to be a distance matrix and must be square during fit. X may be a :term:`sparse graph`, in which case only 'nonzero' elements may be considered neighbors.

outlier_label : manual label, 'most_frequent', default=None label for outlier samples (samples with no neighbors in given radius).

manual label: str or int label (should be the same type as y) or list of manual labels if multi-output is used.
'most_frequent' : assign the most frequent label of y to outliers.
None : when any outlier is detected, ValueError will be raised.

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

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.

Attributes ---------- classes_ : ndarray of shape (n_classes,) Class labels known to the classifier.

effective_metric_ : str or callble The distance metric used. It will be same as the `metric` parameter or a synonym of it, e.g. 'euclidean' if the `metric` parameter set to 'minkowski' and `p` parameter set to 2.

effective_metric_params_ : dict Additional keyword arguments for the metric function. For most metrics will be same with `metric_params` parameter, but may also contain the `p` parameter value if the `effective_metric_` attribute is set to 'minkowski'.

outputs_2d_ : bool False when `y`'s shape is (n_samples, ) or (n_samples, 1) during fit otherwise True.

Examples -------- >>> X = [0], [1], [2], [3] >>> y = 0, 0, 1, 1 >>> from sklearn.neighbors import RadiusNeighborsClassifier >>> neigh = RadiusNeighborsClassifier(radius=1.0) >>> neigh.fit(X, y) RadiusNeighborsClassifier(...) >>> print(neigh.predict([1.5])) 0 >>> print(neigh.predict_proba([1.0])) [0.66666667 0.33333333]

See also -------- KNeighborsClassifier RadiusNeighborsRegressor KNeighborsRegressor NearestNeighbors

Notes ----- See :ref:`Nearest Neighbors <neighbors>` in the online documentation for a discussion of the choice of ``algorithm`` and ``leaf_size``.

https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm

val fit : 
  x:[ `Arr of [> `ArrayLike ] Np.Obj.t | `PyObject of Py.Object.t ] ->
  y:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  t

Fit the model using X as training data and y as target values

Parameters ---------- X : BallTree, KDTree or array-like, sparse matrix of shape (n_samples, n_features) or (n_samples, n_samples) Training data. If array or matrix, the shape is (n_samples, n_features), or (n_samples, n_samples) if metric='precomputed'.

y : array-like, sparse matrix of shape (n_samples,) or (n_samples, n_output) Target values.

val get_params : ?deep:bool -> [> tag ] Obj.t -> Dict.t

Get parameters for this estimator.

Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns ------- params : mapping of string to any Parameter names mapped to their values.

val predict : 
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

Predict the class labels for the provided data.

Parameters ---------- X : array-like of shape (n_queries, n_features), or (n_queries, n_indexed) if metric == 'precomputed' Test samples.

Returns ------- y : ndarray of shape (n_queries,) or (n_queries, n_outputs) Class labels for each data sample.

val predict_proba : 
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

Return probability estimates for the test data X.

Parameters ---------- X : array-like of shape (n_queries, n_features), or (n_queries, n_indexed) if metric == 'precomputed' Test samples.

Returns ------- p : ndarray of shape (n_queries, n_classes), or a list of n_outputs of such arrays if n_outputs > 1. The class probabilities of the input samples. Classes are ordered by lexicographic order.

val radius_neighbors : 
  ?x:[> `ArrayLike ] Np.Obj.t ->
  ?radius:float ->
  ?sort_results:bool ->
  [> tag ] Obj.t ->
  Np.Numpy.Ndarray.List.t * Np.Numpy.Ndarray.List.t

Finds the neighbors within a given radius of a point or points.

Return the indices and distances of each point from the dataset lying in a ball with size ``radius`` around the points of the query array. Points lying on the boundary are included in the results.

The result points are *not* necessarily sorted by distance to their query point.

Parameters ---------- X : array-like, (n_samples, n_features), optional The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor.

radius : float Limiting distance of neighbors to return. (default is the value passed to the constructor).

return_distance : boolean, optional. Defaults to True. If False, distances will not be returned.

sort_results : boolean, optional. Defaults to False. If True, the distances and indices will be sorted before being returned. If False, the results will not be sorted. If return_distance == False, setting sort_results = True will result in an error.

.. versionadded:: 0.22

Returns ------- neigh_dist : array, shape (n_samples,) of arrays Array representing the distances to each point, only present if return_distance=True. The distance values are computed according to the ``metric`` constructor parameter.

neigh_ind : array, shape (n_samples,) of arrays An array of arrays of indices of the approximate nearest points from the population matrix that lie within a ball of size ``radius`` around the query points.

Examples -------- In the following example, we construct a NeighborsClassifier class from an array representing our data set and ask who's the closest point to 1, 1, 1:

>>> import numpy as np >>> samples = [0., 0., 0.], [0., .5, 0.], [1., 1., .5] >>> from sklearn.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(radius=1.6) >>> neigh.fit(samples) NearestNeighbors(radius=1.6) >>> rng = neigh.radius_neighbors([1., 1., 1.]) >>> print(np.asarray(rng00)) 1.5 0.5 >>> print(np.asarray(rng10)) 1 2

The first array returned contains the distances to all points which are closer than 1.6, while the second array returned contains their indices. In general, multiple points can be queried at the same time.

Notes ----- Because the number of neighbors of each point is not necessarily equal, the results for multiple query points cannot be fit in a standard data array. For efficiency, `radius_neighbors` returns arrays of objects, where each object is a 1D array of indices or distances.

val radius_neighbors_graph : 
  ?x:[> `ArrayLike ] Np.Obj.t ->
  ?radius:float ->
  ?mode:[ `Connectivity | `Distance ] ->
  ?sort_results:bool ->
  [> tag ] Obj.t ->
  [ `ArrayLike | `Csr_matrix | `IndexMixin | `Object ] Np.Obj.t

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

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

Parameters ---------- X : array-like of shape (n_samples, n_features), default=None The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor.

radius : float Radius of neighborhoods. (default is the value passed to the constructor).

mode : 'connectivity', 'distance', optional Type of returned matrix: 'connectivity' will return the connectivity matrix with ones and zeros, in 'distance' the edges are Euclidean distance between points.

sort_results : boolean, optional. Defaults to False. If True, the distances and indices will be sorted before being returned. If False, the results will not be sorted. Only used with mode='distance'.

.. versionadded:: 0.22

Returns ------- A : sparse graph in CSR format, shape = n_queries, n_samples_fit n_samples_fit is the number of samples in the fitted data Ai, j is assigned the weight of edge that connects i to j.

Examples -------- >>> X = [0], [3], [1] >>> from sklearn.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(radius=1.5) >>> neigh.fit(X) NearestNeighbors(radius=1.5) >>> A = neigh.radius_neighbors_graph(X) >>> A.toarray() array([1., 0., 1.], [0., 1., 0.], [1., 0., 1.])