<filename>resources/usr/local/lib/python2.7/dist-packages/sklearn/cluster/hierarchical.py
"""Hierarchical Agglomerative Clustering

These routines perform some hierarchical agglomerative clustering of some
input data. Currently, only Ward's algorithm is implemented.

Authors : <NAME>, <NAME>, <NAME>,
          <NAME>
License: BSD 3 clause
"""
from heapq import heapify, heappop, heappush, heappushpop
import warnings

import numpy as np
from scipy import sparse
from scipy.cluster import hierarchy

from ..base import BaseEstimator, ClusterMixin
from ..externals.joblib import Memory
from ..externals import six
from ..metrics import euclidean_distances
from ..utils import array2d
from ..utils.sparsetools import connected_components

from . import _hierarchical
from ._feature_agglomeration import AgglomerationTransform


###############################################################################
# Ward's algorithm

def ward_tree(X, connectivity=None, n_components=None, copy=True,
              n_clusters=None):
    """Ward clustering based on a Feature matrix.

    Recursively merges the pair of clusters that minimally increases
    within-cluster variance.

    The inertia matrix uses a Heapq-based representation.

    This is the structured version, that takes into account some topological
    structure between samples.

    Parameters
    ----------
    X : array of shape (n_samples, n_features)
        feature matrix  representing n_samples samples to be clustered

    connectivity : sparse matrix.
        connectivity matrix. Defines for each sample the neighboring samples
        following a given structure of the data. The matrix is assumed to
        be symmetric and only the upper triangular half is used.
        Default is None, i.e, the Ward algorithm is unstructured.

    n_components : int (optional)
        Number of connected components. If None the number of connected
        components is estimated from the connectivity matrix.

    copy : bool (optional)
        Make a copy of connectivity or work inplace. If connectivity
        is not of LIL type there will be a copy in any case.

    n_clusters : int (optional)
        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. In this case, the
        complete tree is not computed, thus the 'children' output is of
        limited use, and the 'parents' output should rather be used.
        This option is valid only when specifying a connectivity matrix.

    Returns
    -------
    children : 2D array, shape (n_nodes, 2)
        The children of each non-leaf node. Values less than `n_samples` refer
        to leaves of the tree. A greater value `i` indicates a node with
        children `children[i - n_samples]`.

    n_components : int
        The number of connected components in the graph.

    n_leaves : int
        The number of leaves in the tree

    parents : 1D array, shape (n_nodes, ) or None
        The parent of each node. Only returned when a connectivity matrix
        is specified, elsewhere 'None' is returned.
    """
    X = np.asarray(X)
    if X.ndim == 1:
        X = np.reshape(X, (-1, 1))
    n_samples, n_features = X.shape

    if connectivity is None:
        if n_clusters is not None:
            warnings.warn('Early stopping is implemented only for '
                          'structured Ward clustering (i.e. with '
                          'explicit connectivity.', stacklevel=2)
        out = hierarchy.ward(X)
        children_ = out[:, :2].astype(np.intp)
        return children_, 1, n_samples, None

    # Compute the number of nodes
    if n_components is None:
        n_components, labels = connected_components(connectivity)

    # Convert connectivity matrix to LIL with a copy if needed
    if sparse.isspmatrix_lil(connectivity) and copy:
        connectivity = connectivity.copy()
    elif not sparse.isspmatrix(connectivity):
        connectivity = sparse.lil_matrix(connectivity)
    else:
        connectivity = connectivity.tolil()

    if n_components > 1:
        warnings.warn("the number of connected components of the "
                      "connectivity matrix is %d > 1. Completing it to avoid "
                      "stopping the tree early." % n_components)
        connectivity = _fix_connectivity(X, connectivity, n_components, labels)
        n_components = 1

    if n_clusters is None:
        n_nodes = 2 * n_samples - n_components
    else:
        assert n_clusters <= n_samples
        n_nodes = 2 * n_samples - n_clusters

    if (connectivity.shape[0] != n_samples
            or connectivity.shape[1] != n_samples):
        raise ValueError('Wrong shape for connectivity matrix: %s '
                         'when X is %s' % (connectivity.shape, X.shape))

    # create inertia matrix
    coord_row = []
    coord_col = []
    A = []
    for ind, row in enumerate(connectivity.rows):
        A.append(row)
        # We keep only the upper triangular for the moments
        # Generator expressions are faster than arrays on the following
        row = [i for i in row if i < ind]
        coord_row.extend(len(row) * [ind, ])
        coord_col.extend(row)

    coord_row = np.array(coord_row, dtype=np.intp, order='C')
    coord_col = np.array(coord_col, dtype=np.intp, order='C')

    # build moments as a list
    moments_1 = np.zeros(n_nodes, order='C')
    moments_1[:n_samples] = 1
    moments_2 = np.zeros((n_nodes, n_features), order='C')
    moments_2[:n_samples] = X
    inertia = np.empty(len(coord_row), dtype=np.float, order='C')
    _hierarchical.compute_ward_dist(moments_1, moments_2, coord_row, coord_col,
                                    inertia)
    inertia = list(six.moves.zip(inertia, coord_row, coord_col))
    heapify(inertia)

    # prepare the main fields
    parent = np.arange(n_nodes, dtype=np.intp)
    heights = np.zeros(n_nodes)
    used_node = np.ones(n_nodes, dtype=bool)
    children = []

    not_visited = np.empty(n_nodes, dtype=np.int8, order='C')

    # recursive merge loop
    for k in range(n_samples, n_nodes):
        # identify the merge
        while True:
            inert, i, j = heappop(inertia)
            if used_node[i] and used_node[j]:
                break
        parent[i], parent[j], heights[k] = k, k, inert
        children.append([i, j])
        used_node[i] = used_node[j] = False

        # update the moments
        moments_1[k] = moments_1[i] + moments_1[j]
        moments_2[k] = moments_2[i] + moments_2[j]

        # update the structure matrix A and the inertia matrix
        coord_col = []
        not_visited.fill(1)
        not_visited[k] = 0
        _hierarchical._get_parents(A[i], coord_col, parent, not_visited)
        _hierarchical._get_parents(A[j], coord_col, parent, not_visited)
        # List comprehension is faster than a for loop
        [A[l].append(k) for l in coord_col]
        A.append(coord_col)
        coord_col = np.array(coord_col, dtype=np.intp, order='C')
        coord_row = np.empty(coord_col.shape, dtype=np.intp, order='C')
        coord_row.fill(k)
        n_additions = len(coord_row)
        ini = np.empty(n_additions, dtype=np.float, order='C')

        _hierarchical.compute_ward_dist(moments_1, moments_2,
                                        coord_row, coord_col, ini)
        # List comprehension is faster than a for loop
        [heappush(inertia, (ini[idx], k, coord_col[idx]))
            for idx in range(n_additions)]

    # Separate leaves in children (empty lists up to now)
    n_leaves = n_samples
    children = np.array(children)  # return numpy array for efficient caching

    return children, n_components, n_leaves, parent


###############################################################################
# For non fully-connected graphs

def _fix_connectivity(X, connectivity, n_components, labels):
    """
    Warning: modifies connectivity in place
    """
    for i in range(n_components):
        idx_i = np.where(labels == i)[0]
        Xi = X[idx_i]
        for j in range(i):
            idx_j = np.where(labels == j)[0]
            Xj = X[idx_j]
            D = euclidean_distances(Xi, Xj)
            ii, jj = np.where(D == np.min(D))
            ii = ii[0]
            jj = jj[0]
            connectivity[idx_i[ii], idx_j[jj]] = True
            connectivity[idx_j[jj], idx_i[ii]] = True
    return connectivity

###############################################################################
# Functions for cutting  hierarchical clustering tree


def _hc_cut(n_clusters, children, n_leaves):
    """Function cutting the ward tree for a given number of clusters.

    Parameters
    ----------
    n_clusters : int or ndarray
        The number of clusters to form.

    children : list of pairs. Length of n_nodes
        The children of each non-leaf node. Values less than `n_samples` refer
        to leaves of the tree. A greater value `i` indicates a node with
        children `children[i - n_samples]`.

    n_leaves : int
        Number of leaves of the tree.

    Returns
    -------
    labels : array [n_samples]
        cluster labels for each point

    """
    if n_clusters > n_leaves:
        raise ValueError('Cannot extract more clusters than samples: '
                         '%s clusters where given for a tree with %s leaves.'
                         % (n_clusters, n_leaves))
    # In this function, we store nodes as a heap to avoid recomputing
    # the max of the nodes: the first element is always the smallest
    # We use negated indices as heaps work on smallest elements, and we
    # are interested in largest elements
    # children[-1] is the root of the tree
    nodes = [-(max(children[-1]) + 1)]
    for i in range(n_clusters - 1):
        # As we have a heap, nodes[0] is the smallest element
        these_children = children[-nodes[0] - n_leaves]
        # Insert the 2 children and remove the largest node
        heappush(nodes, -these_children[0])
        heappushpop(nodes, -these_children[1])
    label = np.zeros(n_leaves, dtype=np.intp)
    for i, node in enumerate(nodes):
        label[_hierarchical._hc_get_descendent(-node, children, n_leaves)] = i
    return label


###############################################################################
# Class for Ward hierarchical clustering

class Ward(BaseEstimator, ClusterMixin):
    """Ward hierarchical clustering: constructs a tree and cuts it.

    Recursively merges the pair of clusters that minimally increases
    within-cluster variance.

    Parameters
    ----------
    n_clusters : int, default=2
        The number of clusters to find.

    connectivity : sparse matrix (optional)
        Connectivity matrix. Defines for each sample the neighboring
        samples following a given structure of the data.
        Default is None, i.e, the hierarchical clustering algorithm is
        unstructured.

    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.

    copy : bool, default=True
        Copy the connectivity matrix or work in-place.

    n_components : int (optional)
        The number of connected components in the graph defined by the \
        connectivity matrix. If not set, it is estimated.

    compute_full_tree: bool or 'auto' (optional)
        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 cluster and using caching, it may
        be advantageous to compute the full tree.


    Attributes
    ----------
    `children_` : array-like, shape = [n_nodes, 2]
        The children of each non-leaf node. Values less than `n_samples` refer
        to leaves of the tree. A greater value `i` indicates a node with
        children `children_[i - n_samples]`.

    `labels_` : array [n_samples]
        cluster labels for each point

    `n_leaves_` : int
        Number of leaves in the hierarchical tree.

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

    """

    def __init__(self, n_clusters=2, memory=Memory(cachedir=None, verbose=0),
                 connectivity=None, copy=True, n_components=None,
                 compute_full_tree='auto'):
        self.n_clusters = n_clusters
        self.memory = memory
        self.copy = copy
        self.n_components = n_components
        self.connectivity = connectivity
        self.compute_full_tree = compute_full_tree

    def fit(self, X):
        """Fit the hierarchical clustering on the data

        Parameters
        ----------
        X : array-like, shape = [n_samples, n_features]
            The samples a.k.a. observations.

        Returns
        -------
        self
        """
        memory = self.memory
        X = array2d(X)
        if isinstance(memory, six.string_types):
            memory = Memory(cachedir=memory, verbose=0)

        if not self.connectivity is None:
            if not sparse.issparse(self.connectivity):
                raise TypeError("`connectivity` should be a sparse matrix or "
                                "None, got: %r" % type(self.connectivity))

            if (self.connectivity.shape[0] != X.shape[0] or
                    self.connectivity.shape[1] != X.shape[0]):
                raise ValueError("`connectivity` does not have shape "
                                 "(n_samples, n_samples)")

        n_samples = len(X)
        compute_full_tree = self.compute_full_tree
        if self.connectivity is None:
            compute_full_tree = True
        if compute_full_tree == 'auto':
            # Early stopping is likely to give a speed up only for
            # a large number of clusters. The actual threshold
            # implemented here is heuristic
            compute_full_tree = self.n_clusters > max(100, .02 * n_samples)
        n_clusters = self.n_clusters
        if compute_full_tree:
            n_clusters = None

        # Construct the tree
        self.children_, self.n_components_, self.n_leaves_, parents = \
            memory.cache(ward_tree)(X, self.connectivity,
                                    n_components=self.n_components,
                                    copy=self.copy, n_clusters=n_clusters)
        # Cut the tree
        if compute_full_tree:
            self.labels_ = _hc_cut(self.n_clusters, self.children_,
                                   self.n_leaves_)
        else:
            labels = _hierarchical.hc_get_heads(parents, copy=False)
            # copy to avoid holding a reference on the original array
            labels = np.copy(labels[:n_samples])
            # Reasign cluster numbers
            self.labels_ = np.searchsorted(np.unique(labels), labels)
        return self


###############################################################################
# Ward-based feature agglomeration

class WardAgglomeration(AgglomerationTransform, Ward):
    """Feature agglomeration based on Ward hierarchical clustering

    Parameters
    ----------
    n_clusters : int, default=2
        The number of clusters.

    connectivity : sparse matrix (optional)
        connectivity matrix. Defines for each feature the neighboring
        features following a given structure of the data.
        Default is None, i.e, the hierarchical agglomeration algorithm is
        unstructured.

    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.

    copy : bool, default=True
        Copy the connectivity matrix or work in-place.

    n_components : int (optional)
        The number of connected components in the graph defined by the
        connectivity matrix. If not set, it is estimated.

    compute_full_tree: bool or 'auto' (optional)
        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 cluster and using caching, it may
        be advantageous to compute the full tree.


    Attributes
    ----------
    `children_` : array-like, shape = [n_nodes, 2]
        The children of each non-leaf node. Values less than `n_samples` refer
        to leaves of the tree. A greater value `i` indicates a node with
        children `children_[i - n_samples]`.

    `labels_` : array [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.
    """

    def fit(self, X, y=None, **params):
        """Fit the hierarchical clustering on the data

        Parameters
        ----------
        X : array-like, shape = [n_samples, n_features]
            The data

        Returns
        -------
        self
        """
        return Ward.fit(self, X.T, **params)

<filename>resources/usr/local/lib/python2.7/dist-packages/sklearn/cluster/hierarchical.py
"""Hierarchical Agglomerative Clustering

These routines perform some hierarchical agglomerative clustering of some
input data. Currently, only Ward's algorithm is implemented.

Authors : <NAME>, <NAME>, <NAME>,
          <NAME>
License: BSD 3 clause
"""
from heapq import heapify, heappop, heappush, heappushpop
import warnings

import numpy as np
from scipy import sparse
from scipy.cluster import hierarchy

from ..base import BaseEstimator, ClusterMixin
from ..externals.joblib import Memory
from ..externals import six
from ..metrics import euclidean_distances
from ..utils import array2d
from ..utils.sparsetools import connected_components

from . import _hierarchical
from ._feature_agglomeration import AgglomerationTransform


###############################################################################
# Ward's algorithm

def ward_tree(X, connectivity=None, n_components=None, copy=True,
              n_clusters=None):
    """Ward clustering based on a Feature matrix.

    Recursively merges the pair of clusters that minimally increases
    within-cluster variance.

    The inertia matrix uses a Heapq-based representation.

    This is the structured version, that takes into account some topological
    structure between samples.

    Parameters
    ----------
    X : array of shape (n_samples, n_features)
        feature matrix  representing n_samples samples to be clustered

    connectivity : sparse matrix.
        connectivity matrix. Defines for each sample the neighboring samples
        following a given structure of the data. The matrix is assumed to
        be symmetric and only the upper triangular half is used.
        Default is None, i.e, the Ward algorithm is unstructured.

    n_components : int (optional)
        Number of connected components. If None the number of connected
        components is estimated from the connectivity matrix.

    copy : bool (optional)
        Make a copy of connectivity or work inplace. If connectivity
        is not of LIL type there will be a copy in any case.

    n_clusters : int (optional)
        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. In this case, the
        complete tree is not computed, thus the 'children' output is of
        limited use, and the 'parents' output should rather be used.
        This option is valid only when specifying a connectivity matrix.

    Returns
    -------
    children : 2D array, shape (n_nodes, 2)
        The children of each non-leaf node. Values less than `n_samples` refer
        to leaves of the tree. A greater value `i` indicates a node with
        children `children[i - n_samples]`.

    n_components : int
        The number of connected components in the graph.

    n_leaves : int
        The number of leaves in the tree

    parents : 1D array, shape (n_nodes, ) or None
        The parent of each node. Only returned when a connectivity matrix
        is specified, elsewhere 'None' is returned.
    """
    X = np.asarray(X)
    if X.ndim == 1:
        X = np.reshape(X, (-1, 1))
    n_samples, n_features = X.shape

    if connectivity is None:
        if n_clusters is not None:
            warnings.warn('Early stopping is implemented only for '
                          'structured Ward clustering (i.e. with '
                          'explicit connectivity.', stacklevel=2)
        out = hierarchy.ward(X)
        children_ = out[:, :2].astype(np.intp)
        return children_, 1, n_samples, None

    # Compute the number of nodes
    if n_components is None:
        n_components, labels = connected_components(connectivity)

    # Convert connectivity matrix to LIL with a copy if needed
    if sparse.isspmatrix_lil(connectivity) and copy:
        connectivity = connectivity.copy()
    elif not sparse.isspmatrix(connectivity):
        connectivity = sparse.lil_matrix(connectivity)
    else:
        connectivity = connectivity.tolil()

    if n_components > 1:
        warnings.warn("the number of connected components of the "
                      "connectivity matrix is %d > 1. Completing it to avoid "
                      "stopping the tree early." % n_components)
        connectivity = _fix_connectivity(X, connectivity, n_components, labels)
        n_components = 1

    if n_clusters is None:
        n_nodes = 2 * n_samples - n_components
    else:
        assert n_clusters <= n_samples
        n_nodes = 2 * n_samples - n_clusters

    if (connectivity.shape[0] != n_samples
            or connectivity.shape[1] != n_samples):
        raise ValueError('Wrong shape for connectivity matrix: %s '
                         'when X is %s' % (connectivity.shape, X.shape))

    # create inertia matrix
    coord_row = []
    coord_col = []
    A = []
    for ind, row in enumerate(connectivity.rows):
        A.append(row)
        # We keep only the upper triangular for the moments
        # Generator expressions are faster than arrays on the following
        row = [i for i in row if i < ind]
        coord_row.extend(len(row) * [ind, ])
        coord_col.extend(row)

    coord_row = np.array(coord_row, dtype=np.intp, order='C')
    coord_col = np.array(coord_col, dtype=np.intp, order='C')

    # build moments as a list
    moments_1 = np.zeros(n_nodes, order='C')
    moments_1[:n_samples] = 1
    moments_2 = np.zeros((n_nodes, n_features), order='C')
    moments_2[:n_samples] = X
    inertia = np.empty(len(coord_row), dtype=np.float, order='C')
    _hierarchical.compute_ward_dist(moments_1, moments_2, coord_row, coord_col,
                                    inertia)
    inertia = list(six.moves.zip(inertia, coord_row, coord_col))
    heapify(inertia)

    # prepare the main fields
    parent = np.arange(n_nodes, dtype=np.intp)
    heights = np.zeros(n_nodes)
    used_node = np.ones(n_nodes, dtype=bool)
    children = []

    not_visited = np.empty(n_nodes, dtype=np.int8, order='C')

    # recursive merge loop
    for k in range(n_samples, n_nodes):
        # identify the merge
        while True:
            inert, i, j = heappop(inertia)
            if used_node[i] and used_node[j]:
                break
        parent[i], parent[j], heights[k] = k, k, inert
        children.append([i, j])
        used_node[i] = used_node[j] = False

        # update the moments
        moments_1[k] = moments_1[i] + moments_1[j]
        moments_2[k] = moments_2[i] + moments_2[j]

        # update the structure matrix A and the inertia matrix
        coord_col = []
        not_visited.fill(1)
        not_visited[k] = 0
        _hierarchical._get_parents(A[i], coord_col, parent, not_visited)
        _hierarchical._get_parents(A[j], coord_col, parent, not_visited)
        # List comprehension is faster than a for loop
        [A[l].append(k) for l in coord_col]
        A.append(coord_col)
        coord_col = np.array(coord_col, dtype=np.intp, order='C')
        coord_row = np.empty(coord_col.shape, dtype=np.intp, order='C')
        coord_row.fill(k)
        n_additions = len(coord_row)
        ini = np.empty(n_additions, dtype=np.float, order='C')

        _hierarchical.compute_ward_dist(moments_1, moments_2,
                                        coord_row, coord_col, ini)
        # List comprehension is faster than a for loop
        [heappush(inertia, (ini[idx], k, coord_col[idx]))
            for idx in range(n_additions)]

    # Separate leaves in children (empty lists up to now)
    n_leaves = n_samples
    children = np.array(children)  # return numpy array for efficient caching

    return children, n_components, n_leaves, parent


###############################################################################
# For non fully-connected graphs

def _fix_connectivity(X, connectivity, n_components, labels):
    """
    Warning: modifies connectivity in place
    """
    for i in range(n_components):
        idx_i = np.where(labels == i)[0]
        Xi = X[idx_i]
        for j in range(i):
            idx_j = np.where(labels == j)[0]
            Xj = X[idx_j]
            D = euclidean_distances(Xi, Xj)
            ii, jj = np.where(D == np.min(D))
            ii = ii[0]
            jj = jj[0]
            connectivity[idx_i[ii], idx_j[jj]] = True
            connectivity[idx_j[jj], idx_i[ii]] = True
    return connectivity

###############################################################################
# Functions for cutting  hierarchical clustering tree


def _hc_cut(n_clusters, children, n_leaves):
    """Function cutting the ward tree for a given number of clusters.

    Parameters
    ----------
    n_clusters : int or ndarray
        The number of clusters to form.

    children : list of pairs. Length of n_nodes
        The children of each non-leaf node. Values less than `n_samples` refer
        to leaves of the tree. A greater value `i` indicates a node with
        children `children[i - n_samples]`.

    n_leaves : int
        Number of leaves of the tree.

    Returns
    -------
    labels : array [n_samples]
        cluster labels for each point

    """
    if n_clusters > n_leaves:
        raise ValueError('Cannot extract more clusters than samples: '
                         '%s clusters where given for a tree with %s leaves.'
                         % (n_clusters, n_leaves))
    # In this function, we store nodes as a heap to avoid recomputing
    # the max of the nodes: the first element is always the smallest
    # We use negated indices as heaps work on smallest elements, and we
    # are interested in largest elements
    # children[-1] is the root of the tree
    nodes = [-(max(children[-1]) + 1)]
    for i in range(n_clusters - 1):
        # As we have a heap, nodes[0] is the smallest element
        these_children = children[-nodes[0] - n_leaves]
        # Insert the 2 children and remove the largest node
        heappush(nodes, -these_children[0])
        heappushpop(nodes, -these_children[1])
    label = np.zeros(n_leaves, dtype=np.intp)
    for i, node in enumerate(nodes):
        label[_hierarchical._hc_get_descendent(-node, children, n_leaves)] = i
    return label


###############################################################################
# Class for Ward hierarchical clustering

class Ward(BaseEstimator, ClusterMixin):
    """Ward hierarchical clustering: constructs a tree and cuts it.

    Recursively merges the pair of clusters that minimally increases
    within-cluster variance.

    Parameters
    ----------
    n_clusters : int, default=2
        The number of clusters to find.

    connectivity : sparse matrix (optional)
        Connectivity matrix. Defines for each sample the neighboring
        samples following a given structure of the data.
        Default is None, i.e, the hierarchical clustering algorithm is
        unstructured.

    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.

    copy : bool, default=True
        Copy the connectivity matrix or work in-place.

    n_components : int (optional)
        The number of connected components in the graph defined by the \
        connectivity matrix. If not set, it is estimated.

    compute_full_tree: bool or 'auto' (optional)
        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 cluster and using caching, it may
        be advantageous to compute the full tree.


    Attributes
    ----------
    `children_` : array-like, shape = [n_nodes, 2]
        The children of each non-leaf node. Values less than `n_samples` refer
        to leaves of the tree. A greater value `i` indicates a node with
        children `children_[i - n_samples]`.

    `labels_` : array [n_samples]
        cluster labels for each point

    `n_leaves_` : int
        Number of leaves in the hierarchical tree.

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

    """

    def __init__(self, n_clusters=2, memory=Memory(cachedir=None, verbose=0),
                 connectivity=None, copy=True, n_components=None,
                 compute_full_tree='auto'):
        self.n_clusters = n_clusters
        self.memory = memory
        self.copy = copy
        self.n_components = n_components
        self.connectivity = connectivity
        self.compute_full_tree = compute_full_tree

    def fit(self, X):
        """Fit the hierarchical clustering on the data

        Parameters
        ----------
        X : array-like, shape = [n_samples, n_features]
            The samples a.k.a. observations.

        Returns
        -------
        self
        """
        memory = self.memory
        X = array2d(X)
        if isinstance(memory, six.string_types):
            memory = Memory(cachedir=memory, verbose=0)

        if not self.connectivity is None:
            if not sparse.issparse(self.connectivity):
                raise TypeError("`connectivity` should be a sparse matrix or "
                                "None, got: %r" % type(self.connectivity))

            if (self.connectivity.shape[0] != X.shape[0] or
                    self.connectivity.shape[1] != X.shape[0]):
                raise ValueError("`connectivity` does not have shape "
                                 "(n_samples, n_samples)")

        n_samples = len(X)
        compute_full_tree = self.compute_full_tree
        if self.connectivity is None:
            compute_full_tree = True
        if compute_full_tree == 'auto':
            # Early stopping is likely to give a speed up only for
            # a large number of clusters. The actual threshold
            # implemented here is heuristic
            compute_full_tree = self.n_clusters > max(100, .02 * n_samples)
        n_clusters = self.n_clusters
        if compute_full_tree:
            n_clusters = None

        # Construct the tree
        self.children_, self.n_components_, self.n_leaves_, parents = \
            memory.cache(ward_tree)(X, self.connectivity,
                                    n_components=self.n_components,
                                    copy=self.copy, n_clusters=n_clusters)
        # Cut the tree
        if compute_full_tree:
            self.labels_ = _hc_cut(self.n_clusters, self.children_,
                                   self.n_leaves_)
        else:
            labels = _hierarchical.hc_get_heads(parents, copy=False)
            # copy to avoid holding a reference on the original array
            labels = np.copy(labels[:n_samples])
            # Reasign cluster numbers
            self.labels_ = np.searchsorted(np.unique(labels), labels)
        return self


###############################################################################
# Ward-based feature agglomeration

class WardAgglomeration(AgglomerationTransform, Ward):
    """Feature agglomeration based on Ward hierarchical clustering

    Parameters
    ----------
    n_clusters : int, default=2
        The number of clusters.

    connectivity : sparse matrix (optional)
        connectivity matrix. Defines for each feature the neighboring
        features following a given structure of the data.
        Default is None, i.e, the hierarchical agglomeration algorithm is
        unstructured.

    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.

    copy : bool, default=True
        Copy the connectivity matrix or work in-place.

    n_components : int (optional)
        The number of connected components in the graph defined by the
        connectivity matrix. If not set, it is estimated.

    compute_full_tree: bool or 'auto' (optional)
        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 cluster and using caching, it may
        be advantageous to compute the full tree.


    Attributes
    ----------
    `children_` : array-like, shape = [n_nodes, 2]
        The children of each non-leaf node. Values less than `n_samples` refer
        to leaves of the tree. A greater value `i` indicates a node with
        children `children_[i - n_samples]`.

    `labels_` : array [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.
    """

    def fit(self, X, y=None, **params):
        """Fit the hierarchical clustering on the data

        Parameters
        ----------
        X : array-like, shape = [n_samples, n_features]
            The data

        Returns
        -------
        self
        """
        return Ward.fit(self, X.T, **params)

Hierarchical Agglomerative Clustering  These routines perform some hierarchical agglomerative clustering of some input data. Currently, only Ward's algorithm is implemented.  Authors : <NAME>, <NAME>, <NAME>,           <NAME> License: BSD 3 clause ############################################################################### # Ward's algorithm Ward clustering based on a Feature matrix.      Recursively merges the pair of clusters that minimally increases     within-cluster variance.      The inertia matrix uses a Heapq-based representation.      This is the structured version, that takes into account some topological     structure between samples.      Parameters     ----------     X : array of shape (n_samples, n_features)         feature matrix  representing n_samples samples to be clustered      connectivity : sparse matrix.         connectivity matrix. Defines for each sample the neighboring samples         following a given structure of the data. The matrix is assumed to         be symmetric and only the upper triangular half is used.         Default is None, i.e, the Ward algorithm is unstructured.      n_components : int (optional)         Number of connected components. If None the number of connected         components is estimated from the connectivity matrix.      copy : bool (optional)         Make a copy of connectivity or work inplace. If connectivity         is not of LIL type there will be a copy in any case.      n_clusters : int (optional)         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. In this case, the         complete tree is not computed, thus the 'children' output is of         limited use, and the 'parents' output should rather be used.         This option is valid only when specifying a connectivity matrix.      Returns     -------     children : 2D array, shape (n_nodes, 2)         The children of each non-leaf node. Values less than `n_samples` refer         to leaves of the tree. A greater value `i` indicates a node with         children `children[i - n_samples]`.      n_components : int         The number of connected components in the graph.      n_leaves : int         The number of leaves in the tree      parents : 1D array, shape (n_nodes, ) or None         The parent of each node. Only returned when a connectivity matrix         is specified, elsewhere 'None' is returned. # Compute the number of nodes # Convert connectivity matrix to LIL with a copy if needed # create inertia matrix # We keep only the upper triangular for the moments # Generator expressions are faster than arrays on the following # build moments as a list # prepare the main fields # recursive merge loop # identify the merge # update the moments # update the structure matrix A and the inertia matrix # List comprehension is faster than a for loop # List comprehension is faster than a for loop # Separate leaves in children (empty lists up to now) # return numpy array for efficient caching ############################################################################### # For non fully-connected graphs Warning: modifies connectivity in place ############################################################################### # Functions for cutting  hierarchical clustering tree Function cutting the ward tree for a given number of clusters.      Parameters     ----------     n_clusters : int or ndarray         The number of clusters to form.      children : list of pairs. Length of n_nodes         The children of each non-leaf node. Values less than `n_samples` refer         to leaves of the tree. A greater value `i` indicates a node with         children `children[i - n_samples]`.      n_leaves : int         Number of leaves of the tree.      Returns     -------     labels : array [n_samples]         cluster labels for each point # In this function, we store nodes as a heap to avoid recomputing # the max of the nodes: the first element is always the smallest # We use negated indices as heaps work on smallest elements, and we # are interested in largest elements # children[-1] is the root of the tree # As we have a heap, nodes[0] is the smallest element # Insert the 2 children and remove the largest node ############################################################################### # Class for Ward hierarchical clustering Ward hierarchical clustering: constructs a tree and cuts it.      Recursively merges the pair of clusters that minimally increases     within-cluster variance.      Parameters     ----------     n_clusters : int, default=2         The number of clusters to find.      connectivity : sparse matrix (optional)         Connectivity matrix. Defines for each sample the neighboring         samples following a given structure of the data.         Default is None, i.e, the hierarchical clustering algorithm is         unstructured.      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.      copy : bool, default=True         Copy the connectivity matrix or work in-place.      n_components : int (optional)         The number of connected components in the graph defined by the \         connectivity matrix. If not set, it is estimated.      compute_full_tree: bool or 'auto' (optional)         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 cluster and using caching, it may         be advantageous to compute the full tree.       Attributes     ----------     `children_` : array-like, shape = [n_nodes, 2]         The children of each non-leaf node. Values less than `n_samples` refer         to leaves of the tree. A greater value `i` indicates a node with         children `children_[i - n_samples]`.      `labels_` : array [n_samples]         cluster labels for each point      `n_leaves_` : int         Number of leaves in the hierarchical tree.      `n_components_` : int         The estimated number of connected components in the graph. Fit the hierarchical clustering on the data          Parameters         ----------         X : array-like, shape = [n_samples, n_features]             The samples a.k.a. observations.          Returns         -------         self # Early stopping is likely to give a speed up only for # a large number of clusters. The actual threshold # implemented here is heuristic # Construct the tree # Cut the tree # copy to avoid holding a reference on the original array # Reasign cluster numbers ############################################################################### # Ward-based feature agglomeration Feature agglomeration based on Ward hierarchical clustering      Parameters     ----------     n_clusters : int, default=2         The number of clusters.      connectivity : sparse matrix (optional)         connectivity matrix. Defines for each feature the neighboring         features following a given structure of the data.         Default is None, i.e, the hierarchical agglomeration algorithm is         unstructured.      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.      copy : bool, default=True         Copy the connectivity matrix or work in-place.      n_components : int (optional)         The number of connected components in the graph defined by the         connectivity matrix. If not set, it is estimated.      compute_full_tree: bool or 'auto' (optional)         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 cluster and using caching, it may         be advantageous to compute the full tree.       Attributes     ----------     `children_` : array-like, shape = [n_nodes, 2]         The children of each non-leaf node. Values less than `n_samples` refer         to leaves of the tree. A greater value `i` indicates a node with         children `children_[i - n_samples]`.      `labels_` : array [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. Fit the hierarchical clustering on the data          Parameters         ----------         X : array-like, shape = [n_samples, n_features]             The data          Returns         -------         self