package sklearn

You can search for identifiers within the package.

in-package search v0.2.0

sklearn
- Sklearn

Legend:
Library
Module
Module type
Parameter
Class
Class type

type tag = [

| `KMeans

]

type t =
  [ `BaseEstimator | `ClusterMixin | `KMeans | `Object | `TransformerMixin ]
    Obj.t

val of_pyobject : Py.Object.t -> t

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

val as_transformer : t -> [ `TransformerMixin ] Obj.t

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

val as_cluster : t -> [ `ClusterMixin ] Obj.t

val create : 
  ?n_clusters:int ->
  ?init:
    [ `K_means_
    | `Random
    | `Callable of Py.Object.t
    | `Arr of [> `ArrayLike ] Np.Obj.t ] ->
  ?n_init:int ->
  ?max_iter:int ->
  ?tol:float ->
  ?precompute_distances:[ `Auto | `Bool of bool ] ->
  ?verbose:int ->
  ?random_state:int ->
  ?copy_x:bool ->
  ?n_jobs:int ->
  ?algorithm:[ `Auto | `Full | `Elkan ] ->
  unit ->
  t

K-Means clustering.

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

Parameters ----------

n_clusters : int, default=8 The number of clusters to form as well as the number of centroids to generate.

init : 'k-means++', 'random', ndarray, callable, default='k-means++' Method for initialization:

'k-means++' : selects initial cluster centers for k-mean clustering in a smart way to speed up convergence. See section Notes in k_init for more details.

'random': choose `n_clusters` observations (rows) at random from data for the initial centroids.

If an ndarray is passed, it should be of shape (n_clusters, n_features) and gives the initial centers.

If a callable is passed, it should take arguments X, n_clusters and a random state and return an initialization.

n_init : int, default=10 Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia.

max_iter : int, default=300 Maximum number of iterations of the k-means algorithm for a single run.

tol : float, default=1e-4 Relative tolerance with regards to Frobenius norm of the difference in the cluster centers of two consecutive iterations to declare convergence.

precompute_distances : 'auto', True, False, default='auto' Precompute distances (faster but takes more memory).

'auto' : do not precompute distances if n_samples * n_clusters > 12 million. This corresponds to about 100MB overhead per job using double precision.

True : always precompute distances.

False : never precompute distances.

.. deprecated:: 0.23 'precompute_distances' was deprecated in version 0.22 and will be removed in 0.25. It has no effect.

verbose : int, default=0 Verbosity mode.

random_state : int, RandomState instance, default=None Determines random number generation for centroid initialization. Use an int to make the randomness deterministic. See :term:`Glossary <random_state>`.

copy_x : bool, default=True When pre-computing distances it is more numerically accurate to center the data first. If copy_x is True (default), then the original data is not modified. If False, the original data is modified, and put back before the function returns, but small numerical differences may be introduced by subtracting and then adding the data mean. Note that if the original data is not C-contiguous, a copy will be made even if copy_x is False. If the original data is sparse, but not in CSR format, a copy will be made even if copy_x is False.

n_jobs : int, default=None The number of OpenMP threads to use for the computation. Parallelism is sample-wise on the main cython loop which assigns each sample to its closest center.

``None`` or ``-1`` means using all processors.

.. deprecated:: 0.23 ``n_jobs`` was deprecated in version 0.23 and will be removed in 0.25.

algorithm : 'auto', 'full', 'elkan', default='auto' K-means algorithm to use. The classical EM-style algorithm is 'full'. The 'elkan' variation is more efficient on data with well-defined clusters, by using the triangle inequality. However it's more memory intensive due to the allocation of an extra array of shape (n_samples, n_clusters).

For now 'auto' (kept for backward compatibiliy) chooses 'elkan' but it might change in the future for a better heuristic.

.. versionchanged:: 0.18 Added Elkan algorithm

Attributes ---------- cluster_centers_ : ndarray of shape (n_clusters, n_features) Coordinates of cluster centers. If the algorithm stops before fully converging (see ``tol`` and ``max_iter``), these will not be consistent with ``labels_``.

labels_ : ndarray of shape (n_samples,) Labels of each point

inertia_ : float Sum of squared distances of samples to their closest cluster center.

n_iter_ : int Number of iterations run.

See also --------

MiniBatchKMeans Alternative online implementation that does incremental updates of the centers positions using mini-batches. For large scale learning (say n_samples > 10k) MiniBatchKMeans is probably much faster than the default batch implementation.

Notes ----- The k-means problem is solved using either Lloyd's or Elkan's algorithm.

The average complexity is given by O(k n T), were n is the number of samples and T is the number of iteration.

The worst case complexity is given by O(n^(k+2/p)) with n = n_samples, p = n_features. (D. Arthur and S. Vassilvitskii, 'How slow is the k-means method?' SoCG2006)

In practice, the k-means algorithm is very fast (one of the fastest clustering algorithms available), but it falls in local minima. That's why it can be useful to restart it several times.

If the algorithm stops before fully converging (because of ``tol`` or ``max_iter``), ``labels_`` and ``cluster_centers_`` will not be consistent, i.e. the ``cluster_centers_`` will not be the means of the points in each cluster. Also, the estimator will reassign ``labels_`` after the last iteration to make ``labels_`` consistent with ``predict`` on the training set.

Examples --------

>>> from sklearn.cluster import KMeans >>> import numpy as np >>> X = np.array([1, 2], [1, 4], [1, 0], ... [10, 2], [10, 4], [10, 0]) >>> kmeans = KMeans(n_clusters=2, random_state=0).fit(X) >>> kmeans.labels_ array(1, 1, 1, 0, 0, 0, dtype=int32) >>> kmeans.predict([0, 0], [12, 3]) array(1, 0, dtype=int32) >>> kmeans.cluster_centers_ array([10., 2.], [ 1., 2.])

val fit : 
  ?y:Py.Object.t ->
  ?sample_weight:[> `ArrayLike ] Np.Obj.t ->
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  t

Compute k-means clustering.

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) Training instances to cluster. It must be noted that the data will be converted to C ordering, which will cause a memory copy if the given data is not C-contiguous. If a sparse matrix is passed, a copy will be made if it's not in CSR format.

y : Ignored Not used, present here for API consistency by convention.

sample_weight : array-like of shape (n_samples,), default=None The weights for each observation in X. If None, all observations are assigned equal weight.

.. versionadded:: 0.20

Returns ------- self Fitted estimator.

val fit_predict : 
  ?y:Py.Object.t ->
  ?sample_weight:[> `ArrayLike ] Np.Obj.t ->
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

Compute cluster centers and predict cluster index for each sample.

Convenience method; equivalent to calling fit(X) followed by predict(X).

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) New data to transform.

y : Ignored Not used, present here for API consistency by convention.

sample_weight : array-like of shape (n_samples,), default=None The weights for each observation in X. If None, all observations are assigned equal weight.

Returns ------- labels : ndarray of shape (n_samples,) Index of the cluster each sample belongs to.

val fit_transform : 
  ?y:Py.Object.t ->
  ?sample_weight:[> `ArrayLike ] Np.Obj.t ->
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

Compute clustering and transform X to cluster-distance space.

Equivalent to fit(X).transform(X), but more efficiently implemented.

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) New data to transform.

y : Ignored Not used, present here for API consistency by convention.

sample_weight : array-like of shape (n_samples,), default=None The weights for each observation in X. If None, all observations are assigned equal weight.

Returns ------- X_new : array of shape (n_samples, n_clusters) X transformed in the new space.

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 : 
  ?sample_weight:[> `ArrayLike ] Np.Obj.t ->
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

Predict the closest cluster each sample in X belongs to.

In the vector quantization literature, `cluster_centers_` is called the code book and each value returned by `predict` is the index of the closest code in the code book.

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) New data to predict.

sample_weight : array-like of shape (n_samples,), default=None The weights for each observation in X. If None, all observations are assigned equal weight.

Returns ------- labels : ndarray of shape (n_samples,) Index of the cluster each sample belongs to.

val score : 
  ?y:Py.Object.t ->
  ?sample_weight:[> `ArrayLike ] Np.Obj.t ->
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  float

Opposite of the value of X on the K-means objective.

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) New data.

y : Ignored Not used, present here for API consistency by convention.

sample_weight : array-like of shape (n_samples,), default=None The weights for each observation in X. If None, all observations are assigned equal weight.

Returns ------- score : float Opposite of the value of X on the K-means objective.

val set_params : ?params:(string * Py.Object.t) list -> [> tag ] Obj.t -> t

Set 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 transform : 
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

Transform X to a cluster-distance space.

In the new space, each dimension is the distance to the cluster centers. Note that even if X is sparse, the array returned by `transform` will typically be dense.

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) New data to transform.

Returns ------- X_new : ndarray of shape (n_samples, n_clusters) X transformed in the new space.

val cluster_centers_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute cluster_centers_: get value or raise Not_found if None.

val cluster_centers_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute cluster_centers_: get value as an option.

val labels_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute labels_: get value or raise Not_found if None.

val labels_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute labels_: get value as an option.

val inertia_ : t -> float

Attribute inertia_: get value or raise Not_found if None.

val inertia_opt : t -> float option

Attribute inertia_: get value as an option.

val n_iter_ : t -> int

Attribute n_iter_: get value or raise Not_found if None.

val n_iter_opt : t -> int option

Attribute n_iter_: get value as an option.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Format.formatter -> t -> unit

Pretty-print the object to a formatter.