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stream / scikit-learn python

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scikit-learn / cluster / _k_means.so
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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 stringDoes not understand character buffer dtype format string ('%c')Expected 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 ')'raise: arg 3 must be a traceback or Noneinstance exception may not have a separate valueraise: exception class must be a subclass of BaseException'NoneType' object is not iterable'NoneType' object is not subscriptabletoo many values to unpack (expected %zd)need more than %zd value%.1s to unpack'%.200s' does not have the buffer interfaceBuffer 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._assign_labels_csr'%.200s' object does not support slice %.10ssklearn.cluster._k_means._mini_batch_update_csrsklearn.cluster._k_means._assign_labels_arrayBuffer acquisition failed on assignment; and then reacquiring the old buffer failed too!sklearn.cluster._k_means._centers_sparseOut of bounds on buffer access (axis %d)sklearn.cluster._k_means._centers_densecompiletime version %s of module '%.100s' does not match runtime version %ssklearn.utils.sparsefuncs_fast%.200s does not export expected C function %.200svoid (PyArrayObject *, PyArrayObject *, PyArrayObject *, int, PyArrayObject *)C function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)numpy.core.multiarray failed to import_ARRAY_API is not PyCObject objectmodule compiled against ABI version %x but this version of numpy is %xmodule compiled against API version %x but this version of numpy is %xFATAL: module compiled as unknown endianFATAL: module compiled as little endian, but detected different endianness at runtimeintlongan integer is requiredMissing type objectname '%.200s' is not defined__init__.pxd__releasebuffer__exactlysklearn/cluster/_k_means.c%s (%s:%d)end'char''signed char''unsigned char''short''unsigned short''int''unsigned int''long''unsigned long''long long''unsigned long long''float''double''long double'a structPython objecta pointera stringunparseable format string'complex long double''complex double''complex float'tupleExpected %.16s, got %.200snumpy._util_dtypestringnumpy.ndarray.__getbuffer__buffer dtypesklearn/cluster/_k_means.pyx_assign_labels_csrx_squared_normscenterslabelsdistances_mini_batch_update_csrassignmentcountsnearest_centerold_center_assign_labels_arrayX_centers_sparse_centers_densetype.pxd%d.%d%s__builtin____builtins____name__numpyflatiterbroadcastndarrayufunc__pyx_capi__add_row_csrcannot import name %.230snumpy.core.multiarray_ARRAY_API_ARRAY_API not found_ARRAY_API is NULL pointerinit sklearn.cluster._k_meansnpy_intpINTDOUBLE<ÑþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿ,ÑþÿÓþÿÓþÿÓþÿÓþÿÓþÿÑþÿÑþÿÓþÿÓþÿüÐþÿÓþÿÓþÿìÐþÿÜÐþÿÌÐþÿÓþÿÓþÿ¼ÐþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÓþÿÐþÿüÑþÿÜÑþÿÓþÿ¼ÑþÿÑþÿÑþÿ|ÑþÿÓþÿÓþÿlÑþÿÓþÿÓþÿÓþÿ\ÑþÿLÑþÿÓþÿ\ÑþÿìÐþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿüÐþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÑþÿÑþÿÌÑþÿÌÑþÿLÑþÿÌÑþÿÌÑþÿ\ÑþÿlÑþÿ|ÑþÿÌÑþÿÌÑþÿ¼ÐþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿÌÑþÿ<ÐþÿÑþÿÑþÿÌÑþÿÌÐþÿlÐþÿÐþÿÐþÿÌÑþÿÌÑþÿ¬ÐþÿÌÑþÿÌÑþÿÌÑþÿ,Ñþÿ<ÑþÿÌÑþÿ,ÑþÿÓþÿ¢Óþÿ¢Óþÿ¢Óþÿ¢Óþÿ¢ÓþÿÓþÿÓþÿ¢Óþÿ¢ÓþÿÓþÿ¢Óþÿ¢ÓþÿdÑþÿdÑþÿÓþÿ¢Óþÿ¢Óþÿ¢Óþÿ¢Óþÿ¢Óþÿ¢Óþÿ¢Óþÿ¢Óþÿ¢Óþÿ¢Óþÿ¢Óþÿ¢Óþÿ¢Óþÿ¢Óþÿ¢Óþÿ¢ÓþÿyÓþÿnÓþÿÓþÿ¢ÓþÿÓþÿÓþÿyÓþÿyÓþÿ¢Óþÿ¢ÓþÿyÓþÿ¢Óþÿ¢Óþÿ¢ÓþÿyÓþÿyÓþÿ¢ÓþÿyÓþÿ<ÒþÿdÒþÿdÒþÿdÒþÿdÒþÿdÒþÿÄÑþÿ,ÒþÿdÒþÿdÒþÿäÐþÿdÒþÿdÒþÿäÐþÿäÐþÿäÐþÿdÒþÿdÒþÿdÒþÿdÒþÿdÒþÿdÒþÿdÒþÿdÒþÿdÒþÿdÒþÿdÒþÿdÒþÿdÒþÿdÒþÿdÒþÿdÒþÿ<Òþÿ<ÒþÿüÑþÿdÒþÿÒþÿLÒþÿÄÑþÿ,ÒþÿdÒþÿdÒþÿäÐþÿdÒþÿdÒþÿdÒþÿ<ÒþÿäÐþÿdÒþÿ<ÒþÿLÓþÿÄÓþÿÄÓþÿLÓþÿÄÓþÿÄÓþÿÄÓþÿÄÓþÿÄÓþÿÓþÿ|ÓþÿÄÓþÿÄÓþÿ,ÓþÿÄÓþÿÄÓþÿ,Óþÿ,Óþÿ,ÓþÿÄÓþÿÄÓþÿÄÓþÿÄÓþÿÄÓþÿÄÓþÿÄÓþÿÄÓþÿÄÓþÿÄÓþÿÄÓþÿÄÓþÿÄÓþÿÄÓþÿÄÓþÿÄÓþÿLÓþÿLÓþÿ,ÓþÿÄÓþÿ|Óþÿ¬ÓþÿÓþÿ|ÓþÿÄÓþÿÄÓþÿ,ÓþÿÄÓþÿÄÓþÿÄÓþÿLÓþÿ,ÓþÿÄÓþÿLÓþÿ ÐþÿÈÐþÿÈÐþÿ ÐþÿÈÐþÿÈÐþÿÈÐþÿÈÐþÿÈÐþÿ(ÐþÿÐþÿÈÐþÿÈÐþÿÐþÿÈÐþÿÈÐþÿHÏþÿHÏþÿHÏþÿÈÐþÿÈÐþÿÈÐþÿÈÐþÿÈÐþÿÈÐþÿÈÐþÿÈÐþÿÈÐþÿÈÐþÿÈÐþÿÈÐþÿÈÐþÿÈÐþÿÈÐþÿÈÐþÿ Ðþÿ Ðþÿ`ÐþÿÈÐþÿxÐþÿÑþÿ(ÐþÿÐþÿÈÐþÿÈÐþÿÐþÿÈÐþÿÈÐþÿÈÐþÿ ÐþÿHÏþÿÈÐþÿ ÐþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿäÑþÿÒþÿÒþÿäÑþÿäÑþÿäÑþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿäÑþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿäÑþÿÒþÿÒþÿÒþÿÒþÿäÑþÿÒþÿÒþÿÀÕþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿ~ÒþÿÒþÿÒþÿ~ÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿ~ÒþÿÕþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÔþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿÒþÿ`ÔþÿÒþÿxÒþÿHÔþÿÕþÿÒþÿHÔþÿÒþÿ@ÓþÿÒþÿÒþÿÒþÿÒþÿÒþÿ@Óþÿ@ÓþÿÒþÿÒþÿ@ÓþÿÒþÿÒþÿ@ÓþÿÒþÿ@ÓþÿÒþÿÒþÿ°ÓþÿÒþÿÒþÿÒþÿÒþÿÒþÿÓþÿÒþÿÒþÿÒþÿHÔþÿÒþÿÒþÿÒþÿ@Óþÿ@Óþÿ@ÓþÿÒþÿ@Óþÿ@Óþÿ@Óþÿ@ÓþÿÒþÿÒþÿ@ÓþÿÒþÿÒþÿÒþÿ@Óþÿ@ÓþÿÒþÿðÒþÿÒþÿÒþÿÒþÿÒþÿ`ÖþÿÒþÿÒþÿÒþÿÒþÿÖþÿ0øþÿ÷þÿd÷þÿ÷þÿ©÷þÿ ÷þÿ$øþÿøþÿøþÿøþÿô÷þÿè÷þÿß÷þÿÖ÷þÿÍ÷þÿÄ÷þÿ»÷þÿ²÷þÿ*ÿÿz*ÿÿq*ÿÿh*ÿÿ_*ÿÿV*ÿÿx)ÿÿI+ÿÿj+ÿÿ+ÿÿ¬+ÿÿcFÿÿZFÿÿQFÿÿHFÿÿ?Fÿÿ6Fÿÿ-Fÿÿ$Fÿÿ:EÿÿhFÿÿFÿÿªFÿÿËFÿÿìFÿÿ
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 !Èè*¨@	þÿÿoxÿÿÿoðÿÿoùÿÿo¹(!æ2ö233&363F3V3f3v333¦3¶3Æ3Ö3æ3ö344&464F4V4f4v444¦4¶4Æ4Ö4æ4ö455&565F5V5f5v555¦5¶5Æ5Ö5æ5ö566&666F6V6f6v666¦6¶6Æ6Ö6æ6ö677&767F7V7f7v777¦7¶7Æ7Ö7æ7ö78"!M step of the K-means EM algorithm

    Computation of cluster centers / means.

    Parameters
    ----------
    X: scipy.sparse.csr_matrix, shape (n_samples, n_features)

    labels: array of integers, shape (n_samples)
        Current label assignment

    n_clusters: int
        Number of desired clusters

    distances: array-like, shape (n_samples)
        Distance to closest cluster for each sample.

    Returns
    -------
    centers: array, shape (n_clusters, n_features)
        The resulting centers
    M step of the K-means EM algorithm

    Computation of cluster centers / means.

    Parameters
    ----------
    X: array-like, shape (n_samples, n_features)

    labels: array of integers, shape (n_samples)
        Current label assignment

    n_clusters: int
        Number of desired clusters

    distances: array-like, shape (n_samples)
        Distance to closest cluster for each sample.

    Returns
    -------
    centers: array, shape (n_clusters, n_features)
        The resulting centers
    Incremental update of the centers for sparse MiniBatchKMeans.

    Parameters
    ----------

    X: CSR matrix, dtype float64
        The complete (pre allocated) training set as a CSR matrix.

    centers: array, shape (n_clusters, n_features)
        The cluster centers

    counts: array, shape (n_clusters,)
         The vector in which we keep track of the numbers of elements in a
         cluster

    Returns
    -------
    inertia: float
        The inertia of the batch prior to centers update, i.e. the sum
        distances to the closest center for each sample. This is the objective
        function being minimized by the k-means algorithm.

    squared_diff: float
        The sum of squared update (squared norm of the centers position
        change). If compute_squared_diff is 0, this computation is skipped and
        0.0 is returned instead.

    Both squared diff and inertia are commonly used to monitor the convergence
    of the algorithm.
    Compute label assignment and inertia for a CSR input

    Return the inertia (sum of squared distances to the centers).
    Compute label assignment and inertia for a dense array

    Return the inertia (sum of squared distances to the centers).
    Format string allocated too short.ndarray is not Fortran contiguousNon-native byte order not supportedFormat string allocated too short, see comment in numpy.pxdunknown dtype code in numpy.pxd (%d)/home/omer/Documents/Projects/scikit-learn/sklearn/cluster/_k_means.pyxndarray is not C contiguoussklearn.cluster._k_means_mini_batch_update_csrn_samples_in_clustercompute_squared_diffsklearn.utils.fixesfar_from_centersx_squared_normsnearest_centerempty_clusters_centers_sparseutils.extmath_centers_densesquared_diffscipy.sparseRuntimeErrorfeature_idxcenter_diffsample_idxold_centernew_centern_featuresn_clusterscluster_idcenter_idxValueErrorold_countnew_countn_samplesminlengthenumeratedistancesX_indicesbincountX_indptrnewaxisindicesfloat64centersargsortlabelsindptr__import__countsX_datazeroswhereshaperangenumpydtype__test__norm__main__data*spnpZgZfZdqlkjihgfdcbXQOLIHBÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿðÄ!PÁ!`Ä!Ã!ðÃ!ðÄ!PÁ!`Ä!Ã!ðÃ!ðÄ!PÁ!`Ä!Ä!pÂ!Â! Ä!ðÄ!Ã!ÐÂ!ðÃ!ðÄ!Ã!ÐÂ!ðÃ!@Å! ¬!<0Å!à«!# Å!`¬!$Å!Ǯ!
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