val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?radius:float ->
?weights:[ `String of string | `Callable of Py.Object.t ] ->
?algorithm:[ `Auto | `Ball_tree | `Kd_tree | `Brute ] ->
?leaf_size:int ->
?p:int ->
?metric:[ `String of string | `Callable of Py.Object.t ] ->
?metric_params:Py.Object.t ->
?n_jobs:[ `Int of int | `None ] ->
?kwargs:(string * Py.Object.t) list ->
unit ->
tRegression based on neighbors within a fixed radius.
The target is predicted by local interpolation of the targets associated of the nearest neighbors in the training set.
Read more in the :ref:`User Guide <regression>`.
.. versionadded:: 0.9
Parameters ---------- radius : float, optional (default = 1.0) Range of parameter space to use by default for :meth:`radius_neighbors` queries.
weights : str or callable weight function used in prediction. Possible values:
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', optional Algorithm used to compute the nearest neighbors:
Note: fitting on sparse input will override the setting of this parameter, using brute force.
leaf_size : int, optional (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 : integer, optional (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 : string 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 the DistanceMetric class 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:`Glossary <sparse graph>`, in which case only "nonzero" elements may be considered neighbors.
metric_params : dict, optional (default = None) Additional keyword arguments for the metric function.
n_jobs : int or None, optional (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 ---------- effective_metric_ : string or callable The distance metric to use. 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'.
Examples -------- >>> X = [0], [1], [2], [3] >>> y = 0, 0, 1, 1 >>> from sklearn.neighbors import RadiusNeighborsRegressor >>> neigh = RadiusNeighborsRegressor(radius=1.0) >>> neigh.fit(X, y) RadiusNeighborsRegressor(...) >>> print(neigh.predict([1.5])) 0.5
See also -------- NearestNeighbors KNeighborsRegressor KNeighborsClassifier RadiusNeighborsClassifier
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:
[ `Ndarray of Ndarray.t
| `SparseMatrix of Csr_matrix.t
| `PyObject of Py.Object.t ] ->
y:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
tFit the model using X as training data and y as target values
Parameters ---------- X : array-like, sparse matrix, BallTree, KDTree Training data. If array or matrix, shape n_samples, n_features, or n_samples, n_samples if metric='precomputed'.
y : array-like, sparse matrix Target values, array of float values, shape = n_samples or n_samples, n_outputs
val get_params : ?deep:bool -> t -> Py.Object.tGet 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.
Predict the target for the provided data
Parameters ---------- X : array-like, shape (n_queries, n_features), or (n_queries, n_indexed) if metric == 'precomputed' Test samples.
Returns ------- y : array of float, shape = n_queries or n_queries, n_outputs Target values
val radius_neighbors :
?x:[ `Ndarray of Ndarray.t | `PyObject of Py.Object.t ] ->
?radius:float ->
?sort_results:bool ->
t ->
Ndarray.t array * Ndarray.t arrayFinds 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:Ndarray.t ->
?radius:float ->
?mode:[ `Connectivity | `Distance ] ->
?sort_results:bool ->
t ->
Csr_matrix.tComputes 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.])
See also -------- kneighbors_graph
Return the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.
Parameters ---------- X : array-like of shape (n_samples, n_features) Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
y : array-like of shape (n_samples,) or (n_samples, n_outputs) True values for X.
sample_weight : array-like of shape (n_samples,), default=None Sample weights.
Returns ------- score : float R^2 of self.predict(X) wrt. y.
Notes ----- The R2 score used when calling ``score`` on a regressor will use ``multioutput='uniform_average'`` from version 0.23 to keep consistent with :func:`~sklearn.metrics.r2_score`. This will influence the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`). To specify the default value manually and avoid the warning, please either call :func:`~sklearn.metrics.r2_score` directly or make a custom scorer with :func:`~sklearn.metrics.make_scorer` (the built-in scorer ``'r2'`` uses ``multioutput='uniform_average'``).
val set_params : ?params:(string * Py.Object.t) list -> t -> tSet the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.
Parameters ---------- **params : dict Estimator parameters.
Returns ------- self : object Estimator instance.
val effective_metric_ : t -> Py.Object.tAttribute effective_metric_: see constructor for documentation
val effective_metric_params_ : t -> Py.Object.tAttribute effective_metric_params_: see constructor for documentation
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.