package sklearn

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sklearn
- Sklearn

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type tag = [

| `AgglomerativeClustering

]

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

val of_pyobject : Py.Object.t -> t

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

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

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

val create : 
  ?n_clusters:[ `I of int | `None ] ->
  ?affinity:[ `Callable of Py.Object.t | `S of string ] ->
  ?memory:
    [ `S of string | `Object_with_the_joblib_Memory_interface of Py.Object.t ] ->
  ?connectivity:[ `Callable of Py.Object.t | `Arr of [> `ArrayLike ] Np.Obj.t ] ->
  ?compute_full_tree:[ `Auto | `Bool of bool ] ->
  ?linkage:[ `Ward | `Complete | `Average | `Single ] ->
  ?distance_threshold:float ->
  unit ->
  t

Agglomerative Clustering

Recursively merges the pair of clusters that minimally increases a given linkage distance.

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

Parameters ---------- n_clusters : int or None, default=2 The number of clusters to find. It must be ``None`` if ``distance_threshold`` is not ``None``.

affinity : str or callable, default='euclidean' Metric used to compute the linkage. Can be 'euclidean', 'l1', 'l2', 'manhattan', 'cosine', or 'precomputed'. If linkage is 'ward', only 'euclidean' is accepted. If 'precomputed', a distance matrix (instead of a similarity matrix) is needed as input for the fit method.

memory : str or object with the joblib.Memory interface, default=None Used to cache the output of the computation of the tree. By default, no caching is done. If a string is given, it is the path to the caching directory.

connectivity : array-like or callable, default=None Connectivity matrix. Defines for each sample the neighboring samples following a given structure of the data. This can be a connectivity matrix itself or a callable that transforms the data into a connectivity matrix, such as derived from kneighbors_graph. Default is None, i.e, the hierarchical clustering algorithm is unstructured.

compute_full_tree : 'auto' or bool, default='auto' Stop early the construction of the tree at n_clusters. This is useful to decrease computation time if the number of clusters is not small compared to the number of samples. This option is useful only when specifying a connectivity matrix. Note also that when varying the number of clusters and using caching, it may be advantageous to compute the full tree. It must be ``True`` if ``distance_threshold`` is not ``None``. By default `compute_full_tree` is 'auto', which is equivalent to `True` when `distance_threshold` is not `None` or that `n_clusters` is inferior to the maximum between 100 or `0.02 * n_samples`. Otherwise, 'auto' is equivalent to `False`.

linkage : 'ward', 'complete', 'average', 'single', default='ward' Which linkage criterion to use. The linkage criterion determines which distance to use between sets of observation. The algorithm will merge the pairs of cluster that minimize this criterion.

ward minimizes the variance of the clusters being merged.
average uses the average of the distances of each observation of the two sets.
complete or maximum linkage uses the maximum distances between all observations of the two sets.
single uses the minimum of the distances between all observations of the two sets.

distance_threshold : float, default=None The linkage distance threshold above which, clusters will not be merged. If not ``None``, ``n_clusters`` must be ``None`` and ``compute_full_tree`` must be ``True``.

.. versionadded:: 0.21

Attributes ---------- n_clusters_ : int The number of clusters found by the algorithm. If ``distance_threshold=None``, it will be equal to the given ``n_clusters``.

labels_ : ndarray of shape (n_samples) cluster labels for each point

n_leaves_ : int Number of leaves in the hierarchical tree.

n_connected_components_ : int The estimated number of connected components in the graph.

children_ : array-like of shape (n_samples-1, 2) The children of each non-leaf node. Values less than `n_samples` correspond to leaves of the tree which are the original samples. A node `i` greater than or equal to `n_samples` is a non-leaf node and has children `children_i - n_samples`. Alternatively at the i-th iteration, childreni0 and childreni1 are merged to form node `n_samples + i`

Examples -------- >>> from sklearn.cluster import AgglomerativeClustering >>> import numpy as np >>> X = np.array([1, 2], [1, 4], [1, 0], ... [4, 2], [4, 4], [4, 0]) >>> clustering = AgglomerativeClustering().fit(X) >>> clustering AgglomerativeClustering() >>> clustering.labels_ array(1, 1, 1, 0, 0, 0)

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

Fit the hierarchical clustering from features, or distance matrix.

Parameters ---------- X : array-like, shape (n_samples, n_features) or (n_samples, n_samples) Training instances to cluster, or distances between instances if ``affinity='precomputed'``.

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

Returns ------- self

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

Fit the hierarchical clustering from features or distance matrix, and return cluster labels.

Parameters ---------- X : array-like, shape (n_samples, n_features) or (n_samples, n_samples) Training instances to cluster, or distances between instances if ``affinity='precomputed'``.

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

Returns ------- labels : ndarray, shape (n_samples,) Cluster labels.

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 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 n_clusters_ : t -> int

Attribute n_clusters_: get value or raise Not_found if None.

val n_clusters_opt : t -> int option

Attribute n_clusters_: 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 n_leaves_ : t -> int

Attribute n_leaves_: get value or raise Not_found if None.

val n_leaves_opt : t -> int option

Attribute n_leaves_: get value as an option.

val n_connected_components_ : t -> int

Attribute n_connected_components_: get value or raise Not_found if None.

val n_connected_components_opt : t -> int option

Attribute n_connected_components_: get value as an option.

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

Attribute children_: get value or raise Not_found if None.

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

Attribute children_: 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.