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Agglomerate features. This node has been automatically generated by wrapping the ``sklearn.cluster.hierarchical.FeatureAgglomeration`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. Similar to AgglomerativeClustering, but recursively merges features instead of samples. Read more in the :ref:`User Guide <hierarchical_clustering>`. **Parameters** n_clusters : int, default 2 The number of clusters to find. connectivity : arraylike or callable, optional Connectivity matrix. Defines for each feature the neighboring features 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. affinity : string 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. memory : Instance of joblib.Memory or string, optional 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. n_components : int (optional) Number of connected components. If None the number of connected components is estimated from the connectivity matrix. NOTE: This parameter is now directly determined from the connectivity matrix and will be removed in 0.18 compute_full_tree : bool or 'auto', optional, 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 features. 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. linkage : {"ward", "complete", "average"}, optional, default "ward" Which linkage criterion to use. The linkage criterion determines which distance to use between sets of features. 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 feature of the two sets.  complete or maximum linkage uses the maximum distances between all features of the two sets. pooling_func : callable, default np.mean This combines the values of agglomerated features into a single value, and should accept an array of shape [M, N] and the keyword argument `axis=1`, and reduce it to an array of size [M]. **Attributes** ``labels_`` : arraylike, (n_features,) cluster labels for each feature. ``n_leaves_`` : int Number of leaves in the hierarchical tree. ``n_components_`` : int The estimated number of connected components in the graph. ``children_`` : arraylike, shape (n_nodes1, 2) The children of each nonleaf node. Values less than `n_features` correspond to leaves of the tree which are the original samples. A node `i` greater than or equal to `n_features` is a nonleaf node and has children `children_[i  n_features]`. Alternatively at the ith iteration, children[i][0] and children[i][1] are merged to form node `n_features + i`














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_train_seq List of tuples: 

dtype dtype 

input_dim Input dimensions 

output_dim Output dimensions 

supported_dtypes Supported dtypes 

Agglomerate features. This node has been automatically generated by wrapping the ``sklearn.cluster.hierarchical.FeatureAgglomeration`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. Similar to AgglomerativeClustering, but recursively merges features instead of samples. Read more in the :ref:`User Guide <hierarchical_clustering>`. **Parameters** n_clusters : int, default 2 The number of clusters to find. connectivity : arraylike or callable, optional Connectivity matrix. Defines for each feature the neighboring features 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. affinity : string 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. memory : Instance of joblib.Memory or string, optional 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. n_components : int (optional) Number of connected components. If None the number of connected components is estimated from the connectivity matrix. NOTE: This parameter is now directly determined from the connectivity matrix and will be removed in 0.18 compute_full_tree : bool or 'auto', optional, 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 features. 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. linkage : {"ward", "complete", "average"}, optional, default "ward" Which linkage criterion to use. The linkage criterion determines which distance to use between sets of features. 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 feature of the two sets.  complete or maximum linkage uses the maximum distances between all features of the two sets. pooling_func : callable, default np.mean This combines the values of agglomerated features into a single value, and should accept an array of shape [M, N] and the keyword argument `axis=1`, and reduce it to an array of size [M]. **Attributes** ``labels_`` : arraylike, (n_features,) cluster labels for each feature. ``n_leaves_`` : int Number of leaves in the hierarchical tree. ``n_components_`` : int The estimated number of connected components in the graph. ``children_`` : arraylike, shape (n_nodes1, 2) The children of each nonleaf node. Values less than `n_features` correspond to leaves of the tree which are the original samples. A node `i` greater than or equal to `n_features` is a nonleaf node and has children `children_[i  n_features]`. Alternatively at the ith iteration, children[i][0] and children[i][1] are merged to form node `n_features + i`




Transform a new matrix using the built clustering This node has been automatically generated by wrapping the sklearn.cluster.hierarchical.FeatureAgglomeration class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
Returns



Fit the hierarchical clustering on the data This node has been automatically generated by wrapping the sklearn.cluster.hierarchical.FeatureAgglomeration class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
Returns self

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