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scikit-learn / cluster / _k_means_elkan.pypy36-pp73-x86_64-linux-gnu.so
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:ÿÿþ9ÿÿò9ÿÿæ9ÿÿÚ9ÿÿÎ9ÿÿÂ9ÿÿ7ÿÿ:ÿÿ7:ÿÿ[:ÿÿ:ÿÿ£:ÿÿîJÿÿâJÿÿÖJÿÿÊJÿÿ¾Jÿÿ²Jÿÿ¦JÿÿnHÿÿ÷JÿÿKÿÿ?KÿÿcKÿÿKÿÿdÿÿxdÿÿldÿÿ`dÿÿTdÿÿHdÿÿ<dÿÿpaÿÿdÿÿ´dÿÿØdÿÿüdÿÿ eÿÿÿÿ´ÿÿ¨ÿÿÿÿÿÿÿÿxÿÿ¬ÿÿ̌ÿÿðÿÿÿÿ8ÿÿ\ÿÿFormat string allocated too short.unable to allocate shape and strides.sklearn/cluster/_k_means_elkan.pyxnumpy.core.umath failed to importno default __reduce__ due to non-trivial __cinit__got differing extents in dimension %d (got %d and %d)Unable to convert item to objectOut of bounds on buffer access (axis %d)Non-native byte order not supportedInvalid mode, expected 'c' or 'fortran', got %sIndirect dimensions not supportedIncompatible checksums (%s vs 0xb068931 = (name))Function call with ambiguous argument typesFormat string allocated too short, see comment in numpy.pxdExpected at least %d argument%s, got %dEmpty shape tuple for cython.arrayCannot create writable memory view from read-only memoryviewCannot assign to read-only memoryviewCan only create a buffer that is contiguous in memory.Buffer view does not expose stridesunknown dtype code in numpy.pxd (%d)numpy.core.multiarray failed to import<strided and direct or indirect>sklearn.cluster._k_means_elkanunable to allocate array data.itemsize <= 0 for cython.arrayNo matching signature foundelkan_iter_chunked_sparseInvalid shape in axis %d: %d.weight_in_clusters_chunkelkan_iter_chunked_denseCannot index with type '%s'<contiguous and indirect><MemoryView of %r at 0x%x><MemoryView of %r object><contiguous and direct>centers_squared_normscenter_half_distances<strided and indirect>distance_next_centern_samples_chunk_effweight_in_clusters<strided and direct>init_bounds_sparsecline_in_traceback__pyx_unpickle_Enuminit_bounds_densecenters_new_chunk__setstate_cython____pyx_PickleErrorn_samples_chunkdtype_is_objectallocate_bufferView.MemoryViewupdate_centersutils.extmathsample_weight__reduce_cython____pyx_getbuffern_samples_remupper_boundsstringsource__pyx_checksumlower_boundscenter_shiftbest_clusterRuntimeErrorcenters_oldcenters_newPickleErrorMemoryErrorImportErrorsignatures__pyx_vtable____pyx_resultn_featuresn_clustersValueErrorIndexErrorrow_norms__reduce_ex____pyx_staten_threadsn_samplesenumeratechunk_idxX_indicesTypeError__setstate____pyx_typen_chunksmin_distitemsize__getstate__defaultsX_indptrEllipsissquaredmemviewindicesfortrancentersupdateunpackstruct__reduce__pickle__name__labelskwargsindptr__import__formatencodedoubleX_datastripstartsplitshaperangenumpyfloatflagserrordtype__class__ASCII__test__stopstepsizepackndimnamemode__main__kinddist__dict__databaseargsobj__new__endnpid|skjicXO()Buffer dtype mismatch, expected %s%s%s but got %sBuffer dtype mismatch, expected '%s' but got %s in '%s.%s'memviewslice is already initialized!%.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%.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 PyObjectShared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompilingUnable to initialize pickling for %s%.200s does not export expected C function %.200sC function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)__annotations__ must be set to a dict object__kwdefaults__ must be set to a dict object__defaults__ must be set to a tuple objectfunction's dictionary may not be deletedsetting function's dictionary to a non-dict__qualname__ must be set to a string object__name__ must be set to a string objectDoes not understand character buffer dtype format string ('%c')sklearn/cluster/_k_means_elkan.pyxArgument '%.200s' has incorrect type (expected %.200s, got %.200s)Cannot convert %.200s to %.200sord() expected string of length 1, but %.200s foundord() expected a character, but string of length %zd foundNeed at least one argument, 0 given.First argument should be of type %.200s, got %.200s.sklearn/cluster/_k_means_elkan.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.assign_item_from_objectView.MemoryView._memoryviewslice.assign_item_from_objectView.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 %.200s%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)calling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseException_ARRAY_API is not PyCapsule objectmodule compiled against ABI version 0x%x but this version of numpy is 0x%xmodule compiled against API version 0x%x but this version of numpy is 0x%xFATAL: module compiled as unknown endianFATAL: module compiled as little endian, but detected different endianness at runtimeView.MemoryView.array.__reduce_cython__View.MemoryView.array.__setstate_cython__View.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____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 ')'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)sklearn.cluster._k_means_elkan._update_chunk_sparseView.MemoryView.memoryview.convert_item_to_objectView.MemoryView._memoryviewslice.convert_item_to_objectAcquisition count is %d (line %d)sklearn.cluster._k_means_elkan.__defaults__View.MemoryView.array.get_memviewView.MemoryView.memoryview_cwrapperItem 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 %dC-contiguous buffer is not indirect in dimension %dBuffer exposes suboffsets but no stridesBuffer not compatible with direct access in dimension %d.Buffer is not indirectly accessible in dimension %d.Buffer not fortran contiguous.View.MemoryView.memoryview.is_sliceView.MemoryView.Enum.__reduce_cython__value too large to convert to intsklearn.cluster._k_means_elkan.elkan_iter_chunked_denseView.MemoryView.memoryview.__repr__View.MemoryView.array.__cinit__Argument '%.200s' must not be Noneobject of type 'NoneType' has no len()expected bytes, NoneType foundc-string too long to convert to PythonStep may not be zero (axis %d)All dimensions preceding dimension %d must be indexed and not slicedView.MemoryView.slice_memviewsliceView.MemoryView.get_slice_from_memviewView.MemoryView.memoryview.is_f_contigView.MemoryView.memoryview.is_c_contigView.MemoryView.assert_direct_dimensionsView.MemoryView.memoryview.setitem_slice_assign_scalarView.MemoryView.memoryview_fromsliceView.MemoryView.memoryview_copy_from_sliceinteger 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.memoryview.__cinit__'NoneType' object is not subscriptablehasattr(): attribute name must be stringView.MemoryView.__pyx_unpickle_Enum__set_stateView.MemoryView.Enum.__setstate_cython__only single character unicode strings can be converted to Py_UCS4, got length %zdsklearn.cluster._k_means_elkan.__pyx_fused_cpdeflocal variable '%s' referenced before assignmentView.MemoryView.memoryview.__getitem__sklearn.cluster._k_means_elkan.elkan_iter_chunked_sparseCannot 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.memoryview.setitem_slice_assignmentView.MemoryView.__pyx_unpickle_Enumsklearn.cluster._k_means_elkan.init_bounds_densesklearn.cluster._k_means_elkan.init_bounds_sparsesklearn.cluster._k_means_elkancompiletime version %s of module '%.100s' does not match runtime version %sfloat (float *, float *, int, int)__pyx_fuse_0_euclidean_dense_densedouble (double *, double *, int, int)__pyx_fuse_1_euclidean_dense_densefloat (__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, float, int)__pyx_fuse_0_euclidean_sparse_densedouble (__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, double, int)__pyx_fuse_1_euclidean_sparse_densevoid (PyArrayObject *, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch)__pyx_fuse_0_relocate_empty_clusters_dense__pyx_fuse_1_relocate_empty_clusters_densevoid (__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch)__pyx_fuse_0_relocate_empty_clusters_sparse__pyx_fuse_1_relocate_empty_clusters_sparsevoid (__Pyx_memviewslice, __Pyx_memviewslice)void (__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice)init sklearn.cluster._k_means_elkansklearn.cluster._k_means_elkan._memoryviewsliceInternal class for passing memoryview slices to Pythonsklearn.cluster._k_means_elkan.memoryviewsklearn.cluster._k_means_elkan.Enumsklearn.cluster._k_means_elkan.array__pyx_fuse_1elkan_iter_chunked_sparse__pyx_fuse_0elkan_iter_chunked_sparse__pyx_fuse_1elkan_iter_chunked_dense__pyx_fuse_0elkan_iter_chunked_dense__pyx_fuse_1init_bounds_sparse__pyx_fuse_0init_bounds_sparseð¿¿;`ËÔLüÿ|tVüÿ¤VüÿÌ	ØVüÿt
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8A0A(B BBBAèBðBøEEMID iàèBðBøEEMID fà©°WXÐuàèEðBøEBMID iàpèEðBøEBMID fà°^_Ðuà<=ÇüÿÅ5BBB B(A0A8GÀ`È]ÐKØFàFèFðFøFFFIÀ~ÈQÐBØFàFèAðAøAAANÀEÈ]ÐKØFàFèFðFøFFFFF F¨F°IÀ~ÈQÐBØFàFèAðAøAAANÀwÈFÐFØFàFèHðFøKFFFF F¨F°F¸FÀFÈFÐiØFàFèFðFøFLÀ~ÈQÐEØFàFèAðAøAAANÀwÈFÐFØFàFèHðFøKFFFF F¨F°F¸FÀFÈFÐiØFàFèFðFøFFFFF LÀ~ÈQÐEØFàFèAðAøAAANÀþÈQÐBØFàFèAðAøAAANÀdÈMÐXÈBÀÈMÐXÈAÀÈMÐXÈBÀðÈMÐWÈBÀÈMÐWÈAÀÈMÐWÈAÀÈYÐNÈDÀ1ÈYÐKÈHÀ1ÈYÐKÈHÀÈMÐWÈBÀÈMÐWÈAÀÈMÐWÈAÀ=	8D0A(B BBBÀÁÁìö(t
LãÀ|$È|$õþÿoðè@
Y$XÐe@&?	þÿÿoÐ%ÿÿÿoðÿÿoB$ùÿÿoØ|$Vtftvttt¦t¶tÆtÖtætötuu&u6uFuVufuvuuu¦u¶uÆuÖuæuöuvv&v6vFvVvfvvvvv¦v¶vÆvÖvævövww&w6wFwVwfwvwww¦w¶wÆwÖwæwöwxx&x6xFxVxfxvxxx¦x¶xÆxÖxæxöxyy&y6yFyVyfyvyyy¦y¶yÆyÖyæyöyzz&z6zFzVzfzvzzz¦z¶zÆzÖzæzöz{{&{6{F{V{f{v{{{¦{¶{Æ{Ö{æ{ö{||&|6|F|V|f|v|||¦|¶|Æ|Ö|æ|ö|}}&}6}F}V}f}v}}}¦}¶}Æ}Ö}à$Single iteration of K-means Elkan algorithm with sparse input.

    Update labels and centers (inplace), for one iteration, distributed
    over data chunks.

    Parameters
    ----------
    X : sparse matrix of shape (n_samples, n_features)
        The observations to cluster. Must be in CSR format.

    sample_weight : ndarray of shape (n_samples,), dtype=floating
        The weights for each observation in X.

    centers_old : ndarray of shape (n_clusters, n_features), dtype=floating
        Centers before previous iteration, placeholder for the centers after
        previous iteration.

    centers_new : ndarray of shape (n_clusters, n_features), dtype=floating
        Centers after previous iteration, placeholder for the new centers
        computed during this iteration.

    weight_in_clusters : ndarray of shape (n_clusters,), dtype=floating
        Placeholder for the sums of the weights of every observation assigned
        to each center.

    center_half_distances : ndarray of shape (n_clusters, n_clusters),             dtype=floating
        Half pairwise distances between centers.

    distance_next_center : ndarray of shape (n_clusters,), dtype=floating
        Distance between each center its closest center.

    upper_bounds : ndarray of shape (n_samples,), dtype=floating
        Upper bound for the distance between each sample and its center,
        updated inplace.

    lower_bounds : ndarray of shape (n_samples, n_clusters), dtype=floating
        Lower bound for the distance between each sample and each center,
        updated inplace.

    labels : ndarray of shape (n_samples,), dtype=int
        labels assignment.

    center_shift : ndarray of shape (n_clusters,), dtype=floating
        Distance between old and new centers.

    n_threads : int
        The number of threads to be used by openmp.

    update_centers : bool
        - If True, the labels and the new centers will be computed, i.e. runs
          the E-step and the M-step of the algorithm.
        - If False, only the labels will be computed, i.e runs the E-step of
          the algorithm. This is useful especially when calling predict on a
          fitted model.
    Single iteration of K-means Elkan algorithm with dense input.

    Update labels and centers (inplace), for one iteration, distributed
    over data chunks.

    Parameters
    ----------
    X : ndarray of shape (n_samples, n_features), dtype=floating
        The observations to cluster.

    sample_weight : ndarray of shape (n_samples,), dtype=floating
        The weights for each observation in X.

    centers_old : ndarray of shape (n_clusters, n_features), dtype=floating
        Centers before previous iteration, placeholder for the centers after
        previous iteration.

    centers_new : ndarray of shape (n_clusters, n_features), dtype=floating
        Centers after previous iteration, placeholder for the new centers
        computed during this iteration.

    weight_in_clusters : ndarray of shape (n_clusters,), dtype=floating
        Placeholder for the sums of the weights of every observation assigned
        to each center.

    center_half_distances : ndarray of shape (n_clusters, n_clusters),             dtype=floating
        Half pairwise distances between centers.

    distance_next_center : ndarray of shape (n_clusters,), dtype=floating
        Distance between each center its closest center.

    upper_bounds : ndarray of shape (n_samples,), dtype=floating
        Upper bound for the distance between each sample and its center,
        updated inplace.

    lower_bounds : ndarray of shape (n_samples, n_clusters), dtype=floating
        Lower bound for the distance between each sample and each center,
        updated inplace.

    labels : ndarray of shape (n_samples,), dtype=int
        labels assignment.

    center_shift : ndarray of shape (n_clusters,), dtype=floating
        Distance between old and new centers.

    n_threads : int
        The number of threads to be used by openmp.

    update_centers : bool
        - If True, the labels and the new centers will be computed, i.e. runs
          the E-step and the M-step of the algorithm.
        - If False, only the labels will be computed, i.e runs the E-step of
          the algorithm. This is useful especially when calling predict on a
          fitted model.
    Initialize upper and lower bounds for each sample for sparse input data.

    Given X, centers and the pairwise distances divided by 2.0 between the
    centers this calculates the upper bounds and lower bounds for each sample.
    The upper bound for each sample is set to the distance between the sample
    and the closest center.

    The lower bound for each sample is a one-dimensional array of n_clusters.
    For each sample i assume that the previously assigned cluster is c1 and the
    previous closest distance is dist, for a new cluster c2, the
    lower_bound[i][c2] is set to distance between the sample and this new
    cluster, if and only if dist > center_half_distances[c1][c2]. This prevents
    computation of unnecessary distances for each sample to the clusters that
    it is unlikely to be assigned to.

    Parameters
    ----------
    X : sparse matrix of shape (n_samples, n_features), dtype=floating
        The input data. Must be in CSR format.

    centers : ndarray of shape (n_clusters, n_features), dtype=floating
        The cluster centers.

    center_half_distances : ndarray of shape (n_clusters, n_clusters),             dtype=floating
        The half of the distance between any 2 clusters centers.

    labels : ndarray of shape(n_samples), dtype=int
        The label for each sample. This array is modified in place.

    upper_bounds : ndarray of shape(n_samples,), dtype=floating
        The upper bound on the distance between each sample and its closest
        cluster center. This array is modified in place.

    lower_bounds : ndarray of shape(n_samples, n_clusters), dtype=floating
        The lower bound on the distance between each sample and each cluster
        center. This array is modified in place.
    Initialize upper and lower bounds for each sample for dense input data.

    Given X, centers and the pairwise distances divided by 2.0 between the
    centers this calculates the upper bounds and lower bounds for each sample.
    The upper bound for each sample is set to the distance between the sample
    and the closest center.

    The lower bound for each sample is a one-dimensional array of n_clusters.
    For each sample i assume that the previously assigned cluster is c1 and the
    previous closest distance is dist, for a new cluster c2, the
    lower_bound[i][c2] is set to distance between the sample and this new
    cluster, if and only if dist > center_half_distances[c1][c2]. This prevents
    computation of unnecessary distances for each sample to the clusters that
    it is unlikely to be assigned to.

    Parameters
    ----------
    X : ndarray of shape (n_samples, n_features), dtype=floating
        The input data.

    centers : ndarray of shape (n_clusters, n_features), dtype=floating
        The cluster centers.

    center_half_distances : ndarray of shape (n_clusters, n_clusters),             dtype=floating
        The half of the distance between any 2 clusters centers.

    labels : ndarray of shape(n_samples), dtype=int
        The label for each sample. This array is modified in place.

    upper_bounds : ndarray of shape(n_samples,), dtype=floating
        The upper bound on the distance between each sample and its closest
        cluster center. This array is modified in place.

    lower_bounds : ndarray, of shape(n_samples, n_clusters), dtype=floating
        The lower bound on the distance between each sample and each cluster
        center. This array is modified in place.
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