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alkaline-ml / scipy python

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scipy / sparse / csgraph / _min_spanning_tree.pypy36-pp73-x86_64-linux-gnu.so
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    minimum_spanning_tree(csgraph, overwrite=False)

    Return a minimum spanning tree of an undirected graph

    A minimum spanning tree is a graph consisting of the subset of edges
    which together connect all connected nodes, while minimizing the total
    sum of weights on the edges.  This is computed using the Kruskal algorithm.

    .. versionadded:: 0.11.0

    Parameters
    ----------
    csgraph : array_like or sparse matrix, 2 dimensions
        The N x N matrix representing an undirected graph over N nodes
        (see notes below).
    overwrite : bool, optional
        if true, then parts of the input graph will be overwritten for
        efficiency.

    Returns
    -------
    span_tree : csr matrix
        The N x N compressed-sparse representation of the undirected minimum
        spanning tree over the input (see notes below).

    Notes
    -----
    This routine uses undirected graphs as input and output.  That is, if
    graph[i, j] and graph[j, i] are both zero, then nodes i and j do not
    have an edge connecting them.  If either is nonzero, then the two are
    connected by the minimum nonzero value of the two.

    Examples
    --------
    The following example shows the computation of a minimum spanning tree
    over a simple four-component graph::

         input graph             minimum spanning tree

             (0)                         (0)
            /   \                       /
           3     8                     3
          /       \                   /
        (3)---5---(1)               (3)---5---(1)
          \       /                           /
           6     2                           2
            \   /                           /
             (2)                         (2)

    It is easy to see from inspection that the minimum spanning tree involves
    removing the edges with weights 8 and 6.  In compressed sparse
    representation, the solution looks like this:

    >>> from scipy.sparse import csr_matrix
    >>> from scipy.sparse.csgraph import minimum_spanning_tree
    >>> X = csr_matrix([[0, 8, 0, 3],
    ...                 [0, 0, 2, 5],
    ...                 [0, 0, 0, 6],
    ...                 [0, 0, 0, 0]])
    >>> Tcsr = minimum_spanning_tree(X)
    >>> Tcsr.toarray().astype(int)
    array([[0, 0, 0, 3],
           [0, 0, 2, 5],
           [0, 0, 0, 0],
           [0, 0, 0, 0]])
    <strided and direct or indirect>unable to allocate array data.minimum_spanning_tree (line 15)ndarray is not C contiguousitemsize <= 0 for cython.arrayInvalid shape in axis %d: %d.Cannot index with type '%s'<contiguous and indirect><MemoryView of %r at 0x%x><MemoryView of %r object>minimum_spanning_tree_min_spanning_tree.pyx<contiguous and direct><strided and indirect><strided and direct>cline_in_traceback__pyx_unpickle_Enum__setstate_cython____pyx_PickleErroreliminate_zerosdtype_is_objectallocate_bufferView.MemoryViewvalidate_graphisspmatrix_csccopy_if_sparse__reduce_cython____pyx_getbufferstringsourcescipy.sparse__pyx_checksumpredecessorsdense_outputRuntimeErrorrow_indicesPickleErrorMemoryErrorImportError__pyx_vtable____pyx_resultisspmatrixcsr_matrixValueErrorIndexError__reduce_ex____pyx_stateoverwriteenumerateTypeError__setstate____pyx_typeitemsize__getstate__Ellipsissp_treememviewindicesfortranfloat64csgraphargsortupdateunpackstruct__reduce__pickle__name__indptr__import__i_sortformatencodeastypearangezerosstartshaperangenumpyint32flagserrordtype__class__ITYPEDTYPEASCII__test__stopstepsizerankpackndimnamemode__main____dict__databaseobj__new__npidcONBuffer dtype mismatch, expected %s%s%s but got %sBuffer dtype mismatch, expected '%s' but got %s in '%s.%s'memviewslice is already initialized!Unable to initialize pickling for %s%.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 PyObjectDoes not understand character buffer dtype format string ('%c')scipy/sparse/csgraph/_min_spanning_tree.cView.MemoryView.memoryview.__str__View.MemoryView.memoryview.size.__get__View.MemoryView.memoryview.nbytes.__get__View.MemoryView.memoryview.itemsize.__get__View.MemoryView.memoryview.ndim.__get__View.MemoryView.memoryview.suboffsets.__get__View.MemoryView.memoryview.shape.__get__View.MemoryView.memoryview.setitem_indexedView.MemoryView.array.memview.__get__View.MemoryView.array.__getattr__View.MemoryView.array.__getitem__View.MemoryView.array.__setitem__Subscript deletion not supported by %.200sView.MemoryView.memoryview.assign_item_from_objectView.MemoryView._memoryviewslice.assign_item_from_object%s() got multiple values for keyword argument '%U'%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'%.200s() takes %.8s %zd positional argument%.1s (%zd given)__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)Unexpected format string character: '%c'Expected a dimension of size %zu, got %zuExpected %d dimensions, got %dPython does not define a standard format string size for long double ('g')..Buffer dtype mismatch; next field is at offset %zd but %zd expectedBig-endian buffer not supported on little-endian compilerBuffer acquisition: Expected '{' after 'T'Cannot handle repeated arrays in format stringExpected a dimension of size %zu, got %dExpected a comma in format string, got '%c'Expected %d dimension(s), got %dUnexpected end of format string, expected ')'Acquisition count is %d (line %d)calling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseExceptionView.MemoryView.array.__reduce_cython__View.MemoryView.array.__setstate_cython__View.MemoryView.memoryview.convert_item_to_objectView.MemoryView._memoryviewslice.convert_item_to_objectView.MemoryView.memoryview.__reduce_cython__View.MemoryView.memoryview.__setstate_cython__View.MemoryView.memoryview.strides.__get__View.MemoryView._memoryviewslice.__reduce_cython__View.MemoryView._memoryviewslice.__setstate_cython__PyObject_GetBuffer: view==NULL argument is obsoleteView.MemoryView.array.__getbuffer__View.MemoryView.memoryview.__getbuffer__View.MemoryView.array.get_memviewView.MemoryView.memoryview_cwrapperBuffer has wrong number of dimensions (expected %d, got %d)Buffer exposes suboffsets but no stridesC-contiguous buffer is not indirect in dimension %dBuffer and memoryview are not contiguous in the same dimension.Buffer is not indirectly contiguous in dimension %d.Buffer is not indirectly accessible in dimension %d.Buffer not compatible with direct access in dimension %d.Item size of buffer (%zu byte%s) does not match size of '%s' (%zu byte%s)View.MemoryView.memoryview.is_sliceView.MemoryView.memoryview_fromsliceView.MemoryView.memoryview_copy_from_sliceView.MemoryView.Enum.__reduce_cython__value too large to convert to intView.MemoryView.memoryview.__repr__c-string too long to convert to PythonCannot convert %.200s to %.200sView.MemoryView.get_slice_from_memviewView.MemoryView.assert_direct_dimensionsView.MemoryView.memoryview.setitem_slice_assign_scalar'NoneType' object is not subscriptablehasattr(): attribute name must be stringView.MemoryView.__pyx_unpickle_Enum__set_stateView.MemoryView.Enum.__setstate_cython__Cannot transpose memoryview with indirect dimensionsView.MemoryView.transpose_memsliceView.MemoryView.memoryview_copyView.MemoryView.memoryview.T.__get__View.MemoryView.copy_data_to_tempView.MemoryView.memoryview_copy_contentsCannot copy memoryview slice with indirect dimensions (axis %d)View.MemoryView.array_cwrapperView.MemoryView.memoryview.copy_fortranView.MemoryView.memoryview.copyView.MemoryView.__pyx_unpickle_Enuminteger division or modulo by zerovalue too large to perform divisionView.MemoryView.pybuffer_indexView.MemoryView.memoryview.get_item_pointertoo many values to unpack (expected %zd)need more than %zd value%.1s to unpack'NoneType' object is not iterableView.MemoryView.memoryview.__setitem__View.MemoryView.array.__cinit__Argument '%.200s' must not be Noneobject of type 'NoneType' has no len()expected bytes, NoneType foundArgument '%.200s' has incorrect type (expected %.200s, got %.200s)View.MemoryView.memoryview.__cinit__View.MemoryView.memoryview.setitem_slice_assignmentAll dimensions preceding dimension %d must be indexed and not slicedView.MemoryView.slice_memviewsliceStep may not be zero (axis %d)local variable '%s' referenced before assignmentView.MemoryView.memoryview.__getitem__scipy.sparse.csgraph._min_spanning_tree.minimum_spanning_treescipy.sparse.csgraph._min_spanning_treecompiletime version %s of module '%.100s' does not match runtime version %sinit scipy.sparse.csgraph._min_spanning_treescipy.sparse.csgraph._min_spanning_tree._memoryviewsliceInternal class for passing memoryview slices to Pythonscipy.sparse.csgraph._min_spanning_tree.memoryviewscipy.sparse.csgraph._min_spanning_tree.Enumscipy.sparse.csgraph._min_spanning_tree.array;¼v(PþÿØ¸WþÿÈWþÿ XþÿPXþÿx[þÿÈ\þÿ xþÿþÿ0þÿH¸þÿ`xþÿxþÿ¨þÿ¨¸þÿÀ8þÿØ¨þÿðèþÿ(8þÿHøþÿx¸þÿÈøþÿà8þÿ؈þÿH8þÿxHþÿxþÿ°þÿØhþÿ`(þÿÀhþÿ(Xþÿ 	¸þÿ8	(þÿp	hþÿ¨	ؑþÿÀ	8þÿø	hþÿ
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    minimum_spanning_tree(csgraph, overwrite=False)

    Return a minimum spanning tree of an undirected graph

    A minimum spanning tree is a graph consisting of the subset of edges
    which together connect all connected nodes, while minimizing the total
    sum of weights on the edges.  This is computed using the Kruskal algorithm.

    .. versionadded:: 0.11.0

    Parameters
    ----------
    csgraph : array_like or sparse matrix, 2 dimensions
        The N x N matrix representing an undirected graph over N nodes
        (see notes below).
    overwrite : bool, optional
        if true, then parts of the input graph will be overwritten for
        efficiency.

    Returns
    -------
    span_tree : csr matrix
        The N x N compressed-sparse representation of the undirected minimum
        spanning tree over the input (see notes below).

    Notes
    -----
    This routine uses undirected graphs as input and output.  That is, if
    graph[i, j] and graph[j, i] are both zero, then nodes i and j do not
    have an edge connecting them.  If either is nonzero, then the two are
    connected by the minimum nonzero value of the two.

    Examples
    --------
    The following example shows the computation of a minimum spanning tree
    over a simple four-component graph::

         input graph             minimum spanning tree

             (0)                         (0)
            /   \                       /
           3     8                     3
          /       \                   /
        (3)---5---(1)               (3)---5---(1)
          \       /                           /
           6     2                           2
            \   /                           /
             (2)                         (2)

    It is easy to see from inspection that the minimum spanning tree involves
    removing the edges with weights 8 and 6.  In compressed sparse
    representation, the solution looks like this:

    >>> from scipy.sparse import csr_matrix
    >>> from scipy.sparse.csgraph import minimum_spanning_tree
    >>> X = csr_matrix([[0, 8, 0, 3],
    ...                 [0, 0, 2, 5],
    ...                 [0, 0, 0, 6],
    ...                 [0, 0, 0, 0]])
    >>> Tcsr = minimum_spanning_tree(X)
    >>> Tcsr.toarray().astype(int)
    array([[0, 0, 0, 3],
           [0, 0, 2, 5],
           [0, 0, 0, 0],
           [0, 0, 0, 0]])
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