# encoding: utf-8 # module scipy.cluster._hierarchy # from C:\Programs\Python\Python313\Lib\site-packages\scipy\cluster\_hierarchy.cp313-win_amd64.pyd # by generator 1.147 # no doc # imports import builtins as __builtins__ # import numpy as np # C:\Programs\Python\Python313\Lib\site-packages\numpy\__init__.py # functions def calculate_cluster_sizes(*args, **kwargs): # real signature unknown """ Calculate the size of each cluster. The result is the fourth column of the linkage matrix. Parameters ---------- Z : ndarray The linkage matrix. The fourth column can be empty. cs : ndarray The array to store the sizes. n : ndarray The number of observations. """ pass def cluster_dist(*args, **kwargs): # real signature unknown """ Form flat clusters by distance criterion. Parameters ---------- Z : ndarray The linkage matrix. T : ndarray The array to store the cluster numbers. The i'th observation belongs to cluster ``T[i]``. cutoff : double Clusters are formed when distances are less than or equal to `cutoff`. n : int The number of observations. """ pass def cluster_in(*args, **kwargs): # real signature unknown """ Form flat clusters by inconsistent criterion. Parameters ---------- Z : ndarray The linkage matrix. R : ndarray The inconsistent matrix. T : ndarray The array to store the cluster numbers. The i'th observation belongs to cluster ``T[i]``. cutoff : double Clusters are formed when the inconsistent values are less than or or equal to `cutoff`. n : int The number of observations. """ pass def cluster_maxclust_dist(*args, **kwargs): # real signature unknown """ Form flat clusters by maxclust criterion. Parameters ---------- Z : ndarray The linkage matrix. T : ndarray The array to store the cluster numbers. The i'th observation belongs to cluster ``T[i]``. n : int The number of observations. mc : int The maximum number of clusters. """ pass def cluster_maxclust_monocrit(*args, **kwargs): # real signature unknown """ Form flat clusters by maxclust_monocrit criterion. Parameters ---------- Z : ndarray The linkage matrix. MC : ndarray The monotonic criterion array. T : ndarray The array to store the cluster numbers. The i'th observation belongs to cluster ``T[i]``. n : int The number of observations. max_nc : int The maximum number of clusters. """ pass def cluster_monocrit(*args, **kwargs): # real signature unknown """ Form flat clusters by monocrit criterion. Parameters ---------- Z : ndarray The linkage matrix. MC : ndarray The monotonic criterion array. T : ndarray The array to store the cluster numbers. The i'th observation belongs to cluster ``T[i]``. cutoff : double Clusters are formed when the MC values are less than or equal to `cutoff`. n : int The number of observations. """ pass def cophenetic_distances(*args, **kwargs): # real signature unknown """ Calculate the cophenetic distances between each observation Parameters ---------- Z : ndarray The linkage matrix. d : ndarray The condensed matrix to store the cophenetic distances. n : int The number of observations. """ pass def fast_linkage(*args, **kwargs): # real signature unknown """ Perform hierarchy clustering. It implements "Generic Clustering Algorithm" from [1]. The worst case time complexity is O(N^3), but the best case time complexity is O(N^2) and it usually works quite close to the best case. Parameters ---------- dists : ndarray A condensed matrix stores the pairwise distances of the observations. n : int The number of observations. method : int The linkage method. 0: single 1: complete 2: average 3: centroid 4: median 5: ward 6: weighted Returns ------- Z : ndarray, shape (n - 1, 4) Computed linkage matrix. References ---------- .. [1] Daniel Mullner, "Modern hierarchical, agglomerative clustering algorithms", :arXiv:`1109.2378v1`. """ pass def get_max_dist_for_each_cluster(*args, **kwargs): # real signature unknown """ Get the maximum inconsistency coefficient for each non-singleton cluster. Parameters ---------- Z : ndarray The linkage matrix. MD : ndarray The array to store the result (hence this input array gets modified in-place). n : int The number of observations. """ pass def get_max_Rfield_for_each_cluster(*args, **kwargs): # real signature unknown """ Get the maximum statistic for each non-singleton cluster. For the i'th non-singleton cluster, max_rfs[i] = max{R[j, rf] j is a descendent of i}. Parameters ---------- Z : ndarray The linkage matrix. R : ndarray The R matrix. max_rfs : ndarray The array to store the result. Note that this input arrays gets modified in-place. n : int The number of observations. rf : int Indicate which column of `R` is used. """ pass def inconsistent(*args, **kwargs): # real signature unknown """ Calculate the inconsistency statistics. Parameters ---------- Z : ndarray The linkage matrix. R : ndarray A (n - 1) x 4 matrix to store the result (hence this input array is modified in-place). The inconsistency statistics ``R[i]`` are calculated over `d` levels below cluster ``i``. ``R[i, 0]`` is the mean of distances. ``R[i, 1]`` is the standard deviation of distances. ``R[i, 2]`` is the number of clusters included. ``R[i, 3]`` is the inconsistency coefficient. .. math:: \frac{\mathtt{Z[i,2]}-\mathtt{R[i,0]}} {R[i,1]} n : int The number of observations. d : int The number of levels included in calculation below a node. """ pass def leaders(*args, **kwargs): # real signature unknown """ Find the leader (root) of each flat cluster. Parameters ---------- Z : ndarray The linkage matrix. T : ndarray The flat clusters assignment returned by `fcluster` or `fclusterdata`. L, M : ndarray `L` and `M` store the result (i.e., these inputs are modified in-place). The leader of flat cluster ``L[i]`` is node ``M[i]``. nc : int The number of flat clusters. n : int The number of observations. Returns ------- err_node : int Found that `T` is invalid when examining node `err_node`. `-1` indicates success. """ pass def linkage(*args, **kwargs): # real signature unknown """ Perform hierarchy clustering. Parameters ---------- dists : ndarray A condensed matrix stores the pairwise distances of the observations. n : int The number of observations. method : int The linkage method. 0: single 1: complete 2: average 3: centroid 4: median 5: ward 6: weighted Returns ------- Z : ndarray, shape (n - 1, 4) Computed linkage matrix. """ pass def mst_single_linkage(*args, **kwargs): # real signature unknown """ Perform hierarchy clustering using MST algorithm for single linkage. Parameters ---------- dists : ndarray A condensed matrix stores the pairwise distances of the observations. n : int The number of observations. Returns ------- Z : ndarray, shape (n - 1, 4) Computed linkage matrix. """ pass def nn_chain(*args, **kwargs): # real signature unknown """ Perform hierarchy clustering using nearest-neighbor chain algorithm. Parameters ---------- dists : ndarray A condensed matrix stores the pairwise distances of the observations. n : int The number of observations. method : int The linkage method. 0: single 1: complete 2: average 3: centroid 4: median 5: ward 6: weighted Returns ------- Z : ndarray, shape (n - 1, 4) Computed linkage matrix. """ pass def prelist(*args, **kwargs): # real signature unknown """ Perform a pre-order traversal on the linkage tree and get a list of ids of the leaves. Parameters ---------- Z : ndarray The linkage matrix. members : ndarray The array to store the result. Note that this input array will be modified in-place. n : int The number of observations. """ pass def __pyx_unpickle_Enum(*args, **kwargs): # real signature unknown pass def __pyx_unpickle_Heap(*args, **kwargs): # real signature unknown pass def __pyx_unpickle_LinkageUnionFind(*args, **kwargs): # real signature unknown pass # classes class Heap(object): """ Binary heap. Heap stores values and keys. Values are passed explicitly, whereas keys are assigned implicitly to natural numbers (from 0 to n - 1). The supported operations (all have O(log n) time complexity): * Return the current minimum value and the corresponding key. * Remove the current minimum value. * Change the value of the given key. Note that the key must be still in the heap. The heap is stored as an array, where children of parent i have indices 2 * i + 1 and 2 * i + 2. All public methods are based on `sift_down` and `sift_up` methods, which restore the heap property by moving an element down or up in the heap. """ def change_value(self, *args, **kwargs): # real signature unknown pass def get_min(self, *args, **kwargs): # real signature unknown pass def remove_min(self, *args, **kwargs): # real signature unknown pass def __init__(self, *args, **kwargs): # real signature unknown pass @staticmethod # known case of __new__ def __new__(*args, **kwargs): # real signature unknown """ Create and return a new object. See help(type) for accurate signature. """ pass def __reduce_cython__(self, *args, **kwargs): # real signature unknown pass def __reduce__(self, *args, **kwargs): # real signature unknown pass def __setstate_cython__(self, *args, **kwargs): # real signature unknown pass def __setstate__(self, *args, **kwargs): # real signature unknown pass __pyx_vtable__ = None # (!) real value is '' class LinkageUnionFind(object): """ Structure for fast cluster labeling in unsorted dendrogram. """ def __init__(self, *args, **kwargs): # real signature unknown pass @staticmethod # known case of __new__ def __new__(*args, **kwargs): # real signature unknown """ Create and return a new object. See help(type) for accurate signature. """ pass def __reduce_cython__(self, *args, **kwargs): # real signature unknown pass def __reduce__(self, *args, **kwargs): # real signature unknown pass def __setstate_cython__(self, *args, **kwargs): # real signature unknown pass def __setstate__(self, *args, **kwargs): # real signature unknown pass __pyx_vtable__ = None # (!) real value is '' # variables with complex values __loader__ = None # (!) real value is '<_frozen_importlib_external.ExtensionFileLoader object at 0x000001B735FF1D90>' __spec__ = None # (!) real value is "ModuleSpec(name='scipy.cluster._hierarchy', loader=<_frozen_importlib_external.ExtensionFileLoader object at 0x000001B735FF1D90>, origin='C:\\\\Programs\\\\Python\\\\Python313\\\\Lib\\\\site-packages\\\\scipy\\\\cluster\\\\_hierarchy.cp313-win_amd64.pyd')" __test__ = {}