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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')Argument '%.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_lloyd.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__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)%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)'NoneType' object is not iterableneed more than %zd value%.1s to unpacktoo many values to unpack (expected %zd)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.__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__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)View.MemoryView.memoryview.convert_item_to_objectView.MemoryView._memoryviewslice.convert_item_to_objectAcquisition count is %d (line %d)sklearn/cluster/_k_means_lloyd.pyxsklearn.cluster._k_means_lloyd.__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 intView.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_sliceonly single character unicode strings can be converted to Py_UCS4, got length %zdsklearn.cluster._k_means_lloyd.__pyx_fused_cpdef'NoneType' object is not subscriptablehasattr(): attribute name must be stringView.MemoryView.__pyx_unpickle_Enum__set_stateView.MemoryView.Enum.__setstate_cython__View.MemoryView.memoryview.__cinit__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_EnumView.MemoryView.memoryview.setitem_slice_assignmentinteger division or modulo by zerovalue too large to perform divisionView.MemoryView.pybuffer_indexView.MemoryView.memoryview.get_item_pointersklearn.cluster._k_means_lloyd.lloyd_iter_chunked_denselocal variable '%s' referenced before assignmentsklearn.cluster._k_means_lloyd.lloyd_iter_chunked_sparseView.MemoryView.memoryview.__setitem__View.MemoryView.memoryview.__getitem__sklearn.cluster._k_means_lloydcompiletime version %s of module '%.100s' does not match runtime version %svoid (enum __pyx_t_7sklearn_5utils_12_cython_blas_BLAS_Order, enum __pyx_t_7sklearn_5utils_12_cython_blas_BLAS_Trans, enum __pyx_t_7sklearn_5utils_12_cython_blas_BLAS_Trans, int, int, int, float, float *, int, float *, int, float, float *, int)void (enum __pyx_t_7sklearn_5utils_12_cython_blas_BLAS_Order, enum __pyx_t_7sklearn_5utils_12_cython_blas_BLAS_Trans, enum __pyx_t_7sklearn_5utils_12_cython_blas_BLAS_Trans, int, int, int, double, double *, int, double *, int, double, double *, int)void (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)_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 runtimeinit sklearn.cluster._k_means_lloydsklearn.cluster._k_means_lloyd._memoryviewsliceInternal class for passing memoryview slices to Pythonsklearn.cluster._k_means_lloyd.memoryviewsklearn.cluster._k_means_lloyd.Enumsklearn.cluster._k_means_lloyd.array__pyx_fuse_1lloyd_iter_chunked_sparse__pyx_fuse_0lloyd_iter_chunked_sparse__pyx_fuse_1lloyd_iter_chunked_dense__pyx_fuse_0lloyd_iter_chunked_denseð¿Àð?ÿÿÿÿÿÿ€?Àÿÿ
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1€#(ø]è%8	þÿÿox%ÿÿÿoðÿÿoê#ùÿÿo/Ø|#Vlflvl†l–l¦l¶lÆlÖlælölmm&m6mFmVmfmvm†m–m¦m¶mÆmÖmæmömnn&n6nFnVnfnvn†n–n¦n¶nÆnÖnænönoo&o6oFoVofovo†o–o¦o¶oÆoÖoæoöopp&p6pFpVpfpvp†p–p¦p¶pÆpÖpæpöpqq&q6qFqVqfqvq†q–q¦q¶qÆqÖqæqöqrr&r6rFrVrfrvr†r–r¦r¶rÆrÖrærörss&s6sFsVsfsvs†s–s¦s¶sÆsÖsæsöstt&t6tFtVtftvt†t–t¦t¶tÆtÖtætötuu&u6uFuVufuvu†u–u¦u¶uà„#Single iteration of K-means lloyd algorithm with sparse input.

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    ----------
    X : sparse matrix of shape (n_samples, n_features), dtype=floating
        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.

    x_squared_norms : ndarray of shape (n_samples,), dtype=floating
        Squared L2 norm of 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.

    centers_squared_norms : ndarray of shape (n_clusters,), dtype=floating
        Squared L2 norm of the centers.

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

    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 lloyd 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.

    x_squared_norms : ndarray of shape (n_samples,), dtype=floating
        Squared L2 norm of 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.

    centers_squared_norms : ndarray of shape (n_clusters,), dtype=floating
        Squared L2 norm of the centers.

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

    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.
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