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Þÿÿ§ÜÿÿÜÿÿÜÿÿÜÿÿÑÜÿÿöÜÿÿÝÿÿ@ÝÿÿeÝÿÿ sklearn/cluster/_hierarchical_fast.pyx Incompatible checksums (0x%x vs (0xb068931, 0x82a3537, 0x6ae9995) = (name)) Incompatible checksums (0x%x vs (0xce49147, 0x8382372, 0xc3d9697) = (next_label, parent, size)) unable to allocate shape and strides. sklearn.cluster._hierarchical_fast single_linkage_label.<locals>.<lambda> numpy.core.umath failed to import no default __reduce__ due to non-trivial __cinit__ got differing extents in dimension %d (got %d and %d) Unable to convert item to object Out of bounds on buffer access (axis %d) Invalid mode, expected 'c' or 'fortran', got %s Input MST array must be sorted by weight Input MST array is not a validly formatted MST array Indirect dimensions not supported Incompatible checksums (0x%x vs (0x43ddba2, 0xaf067fb, 0x213e29a) = (a, b, weight)) Empty shape tuple for cython.array Cannot create writable memory view from read-only memoryview Cannot assign to read-only memoryview Can only create a buffer that is contiguous in memory. Buffer view does not expose strides numpy.core.multiarray failed to import <strided and direct or indirect> unable to allocate array data. itemsize <= 0 for cython.array __pyx_unpickle_WeightedEdge Invalid shape in axis %d: %d. Cannot index with type '%s' <contiguous and indirect> <MemoryView of %r at 0x%x> __pyx_unpickle_UnionFind <MemoryView of %r object> <contiguous and direct> <strided and indirect> single_linkage_label <strided and direct> %s(weight=%f, a=%i, b=%i) cline_in_traceback __pyx_unpickle_Enum _hc_get_descendent current_distances compute_ward_dist mst_linkage_core __setstate_cython__ __pyx_PickleError dtype_is_object allocate_buffer View.MemoryView __reduce_cython__ __pyx_getbuffer average_merge stringsource __pyx_checksum num_features new_distance label_filter hc_get_heads current_node WeightedEdge right_value not_visited _get_parents dist_metric PickleError MemoryError ImportError __pyx_vtable__ __pyx_result n_features left_value descendent ValueError IndexError __reduce_ex__ __pyx_state n_samples n_indices max_merge is_sorted enumerate coord_row coord_col UnionFind TypeError size_max __setstate__ raw_data __pyx_type new_node n_leaves itemsize __getstate__ children Ellipsis parents out_obj out_end memview in_tree fortran float64 weight update unpack struct result __reduce__ pickle parent out_it name __import__ hstack format encode append zeros value start shape range order numpy nodes node0 n_out heads flags error dtype __class__ b_end array a_end ITYPE DTYPE ASCII __test__ stop step size pack ones node ndim __name__ mode mask __main__ intp int8 full __dict__ copy base b_it a_it row res pop obj __new__ n_b n_a min max m_2 m_1 key ind col all pa np id n j i c b a O N L C %.200s() takes %.8s %zd positional argument%.1s (%zd given) Buffer dtype mismatch, expected %s%s%s but got %s Buffer dtype mismatch, expected '%s' but got %s in '%s.%s' %.200s() takes no arguments (%zd given) %.200s() takes exactly one argument (%zd given) Bad call flags in __Pyx_CyFunction_Call. METH_OLDARGS is no longer supported! %.200s() takes no keyword arguments while calling a Python object NULL result without error in PyObject_Call memviewslice is already initialized! %.200s.%.200s is not a type object %.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject %s.%s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject invalid vtable found for imported type Interpreter change detected - this module can only be loaded into one interpreter per process. Acquisition count is %d (line %d) %s() got multiple values for keyword argument '%U' %.200s() keywords must be strings %s() got an unexpected keyword argument '%U' Unexpected format string character: '%c' Expected a dimension of size %zu, got %zu Expected %d dimensions, got %d Python does not define a standard format string size for long double ('g').. Buffer dtype mismatch; next field is at offset %zd but %zd expected Big-endian buffer not supported on little-endian compiler Buffer acquisition: Expected '{' after 'T' Cannot handle repeated arrays in format string Does not understand character buffer dtype format string ('%c') Expected a dimension of size %zu, got %d Expected a comma in format string, got '%c' Expected %d dimension(s), got %d Unexpected end of format string, expected ')' Cannot convert %.200s to %.200s __defaults__ must be set to a tuple object __kwdefaults__ must be set to a dict object __annotations__ must be set to a dict object __qualname__ must be set to a string object __name__ must be set to a string object function's dictionary may not be deleted setting function's dictionary to a non-dict Argument '%.200s' has incorrect type (expected %.200s, got %.200s) Buffer has wrong number of dimensions (expected %d, got %d) Item size of buffer (%zd byte%s) does not match size of '%s' (%zd byte%s) __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. __%.4s__ returned non-%.4s (type %.200s) Unable to initialize pickling for %s value too large to convert to int unbound method %.200S() needs an argument raise: exception class must be a subclass of BaseException calling %R should have returned an instance of BaseException, not %R '%.200s' object is not subscriptable cannot fit '%.200s' into an index-sized integer sklearn/cluster/_hierarchical_fast.cpp View.MemoryView.memoryview.itemsize.__get__ View.MemoryView.memoryview.ndim.__get__ View.MemoryView.get_slice_from_memview View.MemoryView.memoryview.is_f_contig View.MemoryView.memoryview.is_c_contig View.MemoryView.array.memview.__get__ sklearn/cluster/_hierarchical_fast.pyx sklearn.cluster._hierarchical_fast.WeightedEdge.weight.__set__ sklearn.cluster._hierarchical_fast.WeightedEdge.b.__set__ sklearn.cluster._hierarchical_fast.WeightedEdge.a.__set__ View.MemoryView.memoryview_cwrapper Item size of buffer (%zu byte%s) does not match size of '%s' (%zu byte%s) Buffer is not indirectly contiguous in dimension %d. Buffer and memoryview are not contiguous in the same dimension. C-contiguous buffer is not contiguous in dimension %d C-contiguous buffer is not indirect in dimension %d Buffer exposes suboffsets but no strides Buffer not compatible with direct access in dimension %d. Buffer is not indirectly accessible in dimension %d. View.MemoryView.memoryview.size.__get__ View.MemoryView.memoryview.nbytes.__get__ View.MemoryView.memoryview.suboffsets.__get__ View.MemoryView.memoryview.shape.__get__ View.MemoryView.memoryview.__str__ View.MemoryView.memoryview.__repr__ View.MemoryView.memoryview.setitem_indexed View.MemoryView.assert_direct_dimensions View.MemoryView.memoryview.setitem_slice_assign_scalar View.MemoryView.array.__getattr__ View.MemoryView.array.__setitem__ Subscript deletion not supported by %.200s View.MemoryView.array.get_memview sklearn.cluster._hierarchical_fast.WeightedEdge.__reduce_cython__ sklearn.cluster._hierarchical_fast.WeightedEdge.__repr__ _ARRAY_API is not PyCapsule object module compiled against ABI version 0x%x but this version of numpy is 0x%x module compiled against API version 0x%x but this version of numpy is 0x%x FATAL: module compiled as unknown endian FATAL: module compiled as little endian, but detected different endianness at runtime Module '_hierarchical_fast' has already been imported. Re-initialisation is not supported. sklearn.cluster._hierarchical_fast compiletime version %s of module '%.100s' does not match runtime version %s Shared Cython type %.200s is not a type object Shared Cython type %.200s has the wrong size, try recompiling init sklearn.cluster._hierarchical_fast View.MemoryView.memoryview_fromslice View.MemoryView.memoryview_copy_from_slice sklearn.cluster._hierarchical_fast.UnionFind.__reduce_cython__ View.MemoryView.array.__getitem__ View.MemoryView._memoryviewslice.__setstate_cython__ View.MemoryView._memoryviewslice.__reduce_cython__ View.MemoryView.memoryview.__setstate_cython__ View.MemoryView.memoryview.__reduce_cython__ View.MemoryView.array.__setstate_cython__ View.MemoryView.array.__reduce_cython__ '%.200s' object is unsliceable sklearn.cluster._hierarchical_fast.single_linkage_label.lambda sklearn.cluster._hierarchical_fast.compute_ward_dist sklearn.cluster._hierarchical_fast.WeightedEdge.weight.__get__ sklearn.cluster._hierarchical_fast.WeightedEdge.b.__get__ sklearn.cluster._hierarchical_fast.WeightedEdge.a.__get__ View.MemoryView.memoryview.__cinit__ sklearn.cluster._hierarchical_fast.WeightedEdge.__init__ integer division or modulo by zero value too large to perform division View.MemoryView.pybuffer_index View.MemoryView.memoryview.get_item_pointer View.MemoryView.memoryview.strides.__get__ View.MemoryView.memoryview.assign_item_from_object View.MemoryView._memoryviewslice.assign_item_from_object sklearn.cluster._hierarchical_fast._hc_get_descendent PyObject_GetBuffer: view==NULL argument is obsolete View.MemoryView.memoryview.__getbuffer__ 'NoneType' object is not subscriptable hasattr(): attribute name must be string sklearn.cluster._hierarchical_fast.__pyx_unpickle_WeightedEdge__set_state sklearn.cluster._hierarchical_fast.WeightedEdge.__setstate_cython__ View.MemoryView.memoryview.convert_item_to_object View.MemoryView._memoryviewslice.convert_item_to_object sklearn.cluster._hierarchical_fast._single_linkage_label View.MemoryView.array.__getbuffer__ sklearn.cluster._hierarchical_fast.hc_get_heads Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too! sklearn.cluster._hierarchical_fast.__pyx_unpickle_UnionFind__set_state sklearn.cluster._hierarchical_fast.UnionFind.__setstate_cython__ View.MemoryView.memoryview.is_slice sklearn.cluster._hierarchical_fast.__pyx_unpickle_WeightedEdge sklearn.cluster._hierarchical_fast.__pyx_unpickle_UnionFind sklearn.cluster._hierarchical_fast.mst_linkage_core View.MemoryView.__pyx_unpickle_Enum__set_state View.MemoryView.Enum.__setstate_cython__ View.MemoryView.__pyx_unpickle_Enum All dimensions preceding dimension %d must be indexed and not sliced View.MemoryView.slice_memviewslice Step may not be zero (axis %d) local variable '%s' referenced before assignment too many values to unpack (expected %zd) need more than %zd value%.1s to unpack 'NoneType' object is not iterable View.MemoryView.memoryview.__getitem__ View.MemoryView.Enum.__reduce_cython__ View.MemoryView.memoryview.__setitem__ View.MemoryView.array.__cinit__ Argument '%.200s' must not be None object of type 'NoneType' has no len() expected bytes, NoneType found Cannot transpose memoryview with indirect dimensions View.MemoryView.transpose_memslice View.MemoryView.memoryview_copy View.MemoryView.memoryview.T.__get__ View.MemoryView.copy_data_to_temp View.MemoryView.memoryview_copy_contents Cannot copy memoryview slice with indirect dimensions (axis %d) View.MemoryView.array_cwrapper View.MemoryView.memoryview.copy_fortran View.MemoryView.memoryview.copy View.MemoryView.memoryview.setitem_slice_assignment sklearn.cluster._hierarchical_fast.single_linkage_label sklearn.cluster._hierarchical_fast._get_parents '%.50s' object has no attribute '%U' sklearn.cluster._hierarchical_fast.UnionFind.__init__ sklearn.cluster._hierarchical_fast.average_merge sklearn.cluster._hierarchical_fast.max_merge sklearn.cluster._hierarchical_fast._memoryviewslice Internal class for passing memoryview slices to Python sklearn.cluster._hierarchical_fast.memoryview sklearn.cluster._hierarchical_fast.Enum sklearn.cluster._hierarchical_fast.array sklearn.cluster._hierarchical_fast.UnionFind sklearn.cluster._hierarchical_fast.WeightedEdge ð¿ ð? ð; Ò pZýÿ¸ `cýÿ° ±cýÿØ dýÿ, çdýÿT Deýÿ| ¼eýÿ¸
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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: DistanceMetric
A DistanceMetric 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 an 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 an 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
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