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NoOc³8ӮBoJH4( &                           &" FFe fcc77c XYLY?Y2Y%YYffffffsrrrrr Compute the necessary elements of a minimum spanning tree for computation of single linkage clustering. This represents the MST-LINKAGE-CORE algorithm (Figure 6) from :arxiv:`Daniel Mullner, "Modern hierarchical, agglomerative clustering algorithms" <1109.2378>`. In contrast to the scipy implementation is never computes a full distance matrix, generating distances only as they are needed and releasing them when no longer needed. Parameters ---------- raw_data: array of shape (n_samples, n_features) The array of feature data to be clustered. Must be C-aligned dist_metric: DistanceMetric64 A DistanceMetric64 object conforming to the API from ``sklearn.metrics._dist_metrics.pxd`` that will be used to compute distances. Returns ------- mst_core_data: array of shape (n_samples, 3) An array providing information from which one can either compute an MST, or the linkage hierarchy very efficiently. See :arxiv:`Daniel Mullner, "Modern hierarchical, agglomerative clustering algorithms" <1109.2378>` algorithm MST-LINKAGE-CORE for more details. Convert a linkage array or MST to a tree by labelling clusters at merges. This is done by using a Union find structure to keep track of merges efficiently. Parameters ---------- L: array of shape (n_samples - 1, 3) The linkage array or MST where each row specifies two samples to be merged and a distance or weight at which the merge occurs. This array is assumed to be sorted by the distance/weight. Returns ------- A tree in the format used by scipy.cluster.hierarchy. Convert a linkage array or MST to a tree by labelling clusters at merges. This is done by using a Union find structure to keep track of merges efficiently. This is the private version of the function that assumes that ``L`` has been properly validated. See ``single_linkage_label`` for the user facing version of this function. Parameters ---------- L: array of shape (n_samples - 1, 3) The linkage array or MST where each row specifies two samples to be merged and a distance or weight at which the merge occurs. This array is assumed to be sorted by the distance/weight. Returns ------- A tree in the format used by scipy.cluster.hierarchy. Merge two IntFloatDicts with the average strategy: when the same key is present in the two dicts, the weighted average of the two values is used. Parameters ========== a, b : IntFloatDict object The IntFloatDicts to merge mask : ndarray array of dtype integer and of dimension 1 a mask for keys to ignore: if not mask[key] the corresponding key is skipped in the output dictionary n_a, n_b : float n_a and n_b are weights for a and b for the merge strategy. They are used for a weighted mean. Returns ======= out : IntFloatDict object The IntFloatDict resulting from the merge Merge two IntFloatDicts with the max strategy: when the same key is present in the two dicts, the max of the two values is used. Parameters ========== a, b : IntFloatDict object The IntFloatDicts to merge mask : ndarray array of dtype integer and of dimension 1 a mask for keys to ignore: if not mask[key] the corresponding key is skipped in the output dictionary n_a, n_b : float n_a and n_b are weights for a and b for the merge strategy. They are not used in the case of a max merge. Returns ======= out : IntFloatDict object The IntFloatDict resulting from the merge Returns the heads of the given nodes, as defined by parents. Modifies 'heads' and 'not_visited' in-place. Parameters ---------- nodes : list of integers The nodes to start from heads : list of integers A list to hold the results (modified inplace) parents : array of integers The parent structure defining the tree not_visited The tree nodes to consider (modified inplace) Returns the heads of the forest, as defined by parents. Parameters ---------- parents : array of integers The parent structure defining the forest (ensemble of trees) copy : boolean If copy is False, the input 'parents' array is modified inplace Returns ------- heads : array of integers of same shape as parents The indices in the 'parents' of the tree heads Function returning all the descendent leaves of a set of nodes in the tree. Parameters ---------- node : integer The node for which we want the descendents. children : list of pairs, length 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 : integer Number of leaves. Returns ------- descendent : list of int next_label, parent, sizea, b, weightShared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompilingIncompatible checksums (0x%x vs (0x%x, 0x%x, 0x%x) = (%s))%.200s does not export expected C %.8s %.200sC %.8s %.200s.%.200s has wrong signature (expected %.500s, got %.500s)Interpreter change detected - this module can only be loaded into one interpreter per process.base class '%.200s' is not a heap typeextension type '%.200s' has no __dict__ slot, but base type '%.200s' has: either add 'cdef dict __dict__' to the extension type or add '__slots__ = [...]' to the base typeunbound method %.200S() needs an argument__annotations__ must be set to a dict object__kwdefaults__ must be set to a dict objectchanges to cyfunction.__kwdefaults__ will not currently affect the values used in function calls__defaults__ must be set to a tuple objectchanges to cyfunction.__defaults__ will not currently affect the values used in function calls__qualname__ must be set to a string object__name__ must be set to a string objectFailed to import '%.20s.decompress' - cannot initialise module strings. String compression was configured with the C macro 'CYTHON_COMPRESS_STRINGS=%d'.%.200s.%.200s is not a type object%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObjectAcquisition count is %d (line %d)invalid vtable found for imported type while calling a Python objectNULL result without error in PyObject_Callsklearn/cluster/_hierarchical_fast.pyxsklearn.cluster._hierarchical_fast.WeightedEdge.weight.__set__sklearn.cluster._hierarchical_fast.WeightedEdge.weight.__get__sklearn.cluster._hierarchical_fast.WeightedEdge.b.__get__sklearn.cluster._hierarchical_fast.WeightedEdge.a.__get__sklearn.cluster._hierarchical_fast.WeightedEdge.__repr____int__ returned non-int (type %.200s). The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python.__int__ returned non-int (type %.200s)sklearn.cluster._hierarchical_fast.WeightedEdge.b.__set__sklearn.cluster._hierarchical_fast.WeightedEdge.a.__set__sklearn.cluster._hierarchical_fast.__pyx_unpickle_WeightedEdge__set_state%.200s() takes %.8s %zd positional argument%.1s (%zd given)cannot pass None into a C function argument that is declared 'not None'sklearn.cluster._hierarchical_fast.WeightedEdge.__setstate_cython__sklearn.cluster._hierarchical_fast.__pyx_unpickle_WeightedEdgesklearn.cluster._hierarchical_fast.__pyx_unpickle_UnionFind__set_statesklearn.cluster._hierarchical_fast.UnionFind.__setstate_cython__sklearn.cluster._hierarchical_fast.__pyx_unpickle_UnionFindsklearn.cluster._hierarchical_fast._get_parentssklearn.cluster._hierarchical_fast.WeightedEdge.__init__sklearn.cluster._hierarchical_fast._hc_get_descendentsklearn.cluster._hierarchical_fast._single_linkage_labelsklearn.cluster._hierarchical_fast.hc_get_headssklearn.cluster._hierarchical_fast.compute_ward_dist'%.200s' object is not subscriptablesklearn.cluster._hierarchical_fast.max_mergeCannot convert %.200s to %.200ssklearn.cluster._hierarchical_fast.average_mergesklearn.cluster._hierarchical_fast.WeightedEdge.__reduce_cython__sklearn.cluster._hierarchical_fast.UnionFind.__reduce_cython__sklearn.cluster._hierarchical_fast.UnionFind.__init__'%.200s' object is unsliceablesklearn.cluster._hierarchical_fast.single_linkage_label.lambdacannot fit '%.200s' into an index-sized integersklearn.cluster._hierarchical_fast.single_linkage_labelsklearn.cluster._hierarchical_fast.mst_linkage_coreUnable to initialize pickling for %.200sModule '_hierarchical_fast' has already been imported. 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