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scikit-learn / utils / sparsefuncs_fast.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'%.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)Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!sklearn.utils.sparsefuncs_fast.incr_mean_variance_axis0sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l1sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l2sklearn.utils.sparsefuncs_fast.assign_rows_csrsklearn.utils.sparsefuncs_fast.csr_mean_variance_axis0sklearn.utils.sparsefuncs_fast.csc_mean_variance_axis0sklearn.utils.sparsefuncs_fast.csr_row_normssklearn.utils.sparsefuncs_fast.add_row_csrArgument '%.200s' has incorrect type (expected %.200s, got %.200s)sklearn.utils.sparsefuncs_fastcompiletime version %s of module '%.100s' does not match runtime version %svoid (PyArrayObject *, PyArrayObject *, PyArrayObject *, int, PyArrayObject *)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 runtimeinit sklearn.utils.sparsefuncs_fast¸Ôþÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ¨Ôþÿ×þÿ×þÿ×þÿ×þÿ×þÿÔþÿÔþÿ×þÿ×þÿxÔþÿ×þÿ×þÿhÔþÿXÔþÿHÔþÿ×þÿ×þÿ8Ôþÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿÔþÿxÕþÿXÕþÿ×þÿ8ÕþÿÕþÿÕþÿøÔþÿ×þÿ×þÿèÔþÿ×þÿ×þÿ×þÿØÔþÿÈÔþÿ×þÿØÔþÿhÔþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿxÔþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿÔþÿÔþÿHÕþÿHÕþÿÈÔþÿHÕþÿHÕþÿØÔþÿèÔþÿøÔþÿHÕþÿHÕþÿ8ÔþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿHÕþÿ¸ÓþÿÕþÿÕþÿHÕþÿHÔþÿèÓþÿÔþÿÔþÿHÕþÿHÕþÿ(ÔþÿHÕþÿHÕþÿHÕþÿ¨Ôþÿ¸ÔþÿHÕþÿ¨Ôþÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿàÔþÿàÔþÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿ×þÿõÖþÿêÖþÿ×þÿ×þÿ×þÿ×þÿõÖþÿõÖþÿ×þÿ×þÿõÖþÿ×þÿ×þÿ×þÿõÖþÿõÖþÿ×þÿõÖþÿ¸ÕþÿàÕþÿàÕþÿàÕþÿàÕþÿàÕþÿ@Õþÿ¨ÕþÿàÕþÿàÕþÿ`ÔþÿàÕþÿàÕþÿ`Ôþÿ`Ôþÿ`ÔþÿàÕþÿàÕþÿàÕþÿàÕþÿàÕþÿàÕþÿàÕþÿàÕþÿàÕþÿàÕþÿàÕþÿàÕþÿàÕþÿàÕþÿàÕþÿàÕþÿ¸Õþÿ¸ÕþÿxÕþÿàÕþÿÕþÿÈÕþÿ@Õþÿ¨ÕþÿàÕþÿàÕþÿ`ÔþÿàÕþÿàÕþÿàÕþÿ¸Õþÿ`ÔþÿàÕþÿ¸ÕþÿÈÖþÿ@×þÿ@×þÿÈÖþÿ@×þÿ@×þÿ@×þÿ@×þÿ@×þÿ×þÿøÖþÿ@×þÿ@×þÿ¨Öþÿ@×þÿ@×þÿ¨Öþÿ¨Öþÿ¨Öþÿ@×þÿ@×þÿ@×þÿ@×þÿ@×þÿ@×þÿ@×þÿ@×þÿ@×þÿ@×þÿ@×þÿ@×þÿ@×þÿ@×þÿ@×þÿ@×þÿÈÖþÿÈÖþÿ¨Öþÿ@×þÿøÖþÿ(×þÿ×þÿøÖþÿ@×þÿ@×þÿ¨Öþÿ@×þÿ@×þÿ@×þÿÈÖþÿ¨Öþÿ@×þÿÈÖþÿÔþÿDÔþÿDÔþÿÔþÿDÔþÿDÔþÿDÔþÿDÔþÿDÔþÿ¤ÓþÿÔþÿDÔþÿDÔþÿÔþÿDÔþÿDÔþÿÄÒþÿÄÒþÿÄÒþÿDÔþÿDÔþÿDÔþÿDÔþÿDÔþÿDÔþÿDÔþÿDÔþÿDÔþÿDÔþÿDÔþÿDÔþÿDÔþÿDÔþÿDÔþÿDÔþÿÔþÿÔþÿÜÓþÿDÔþÿôÓþÿÕþÿ¤ÓþÿÔþÿDÔþÿDÔþÿÔþÿDÔþÿDÔþÿDÔþÿÔþÿÄÒþÿDÔþÿÔþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿüÕþÿÕþÿÖþÿÖþÿ`ÕþÿÖþÿÖþÿ`Õþÿ`Õþÿ`ÕþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿ`ÕþÿÖþÿÕþÿÖþÿüÕþÿÕþÿÖþÿÖþÿ`ÕþÿÖþÿÖþÿÖþÿÖþÿ`ÕþÿÖþÿÖþÿ<ÙþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿúÕþÿÖþÿÖþÿúÕþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿúÕþÿÙþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿØþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿÜ×þÿÖþÿôÕþÿÄ×þÿÙþÿÖþÿÄ×þÿÖþÿ¼ÖþÿÖþÿÖþÿÖþÿÖþÿÖþÿ¼Öþÿ¼ÖþÿÖþÿÖþÿ¼ÖþÿÖþÿÖþÿ¼ÖþÿÖþÿ¼ÖþÿÖþÿÖþÿ,×þÿÖþÿÖþÿÖþÿÖþÿÖþÿüÖþÿÖþÿÖþÿÖþÿÄ×þÿÖþÿÖþÿÖþÿ¼Öþÿ¼Öþÿ¼ÖþÿÖþÿ¼Öþÿ¼Öþÿ¼Öþÿ¼ÖþÿÖþÿÖþÿ¼ÖþÿÖþÿÖþÿÖþÿ¼Öþÿ¼ÖþÿÖþÿlÖþÿÖþÿÖþÿÖþÿÖþÿÜÙþÿÖþÿÖþÿÖþÿÖþÿÙþÿÜúþÿÐúþÿÄúþÿ¸úþÿ¬úþÿ úþÿúþÿúþÿúþÿyúþÿpúþÿgúþÿ^úþÿUúþÿLúþÿCúþÿ:úþÿúþÿÇ4ÿÿ¾4ÿÿµ4ÿÿ¬4ÿÿ£4ÿÿ,Èÿÿ#ÈÿÿÈÿÿÈÿÿÈÿÿð¿ÿÿÿÿÿÿÿ;1à±þÿ°·þÿ(1·þÿXø·þÿH¸þÿ¹þÿ°"¹þÿÈ{¹þÿðֹþÿHºþÿàs¼þÿ` ½þÿØ@¾þÿðоþÿ@¿þÿx ¿þÿ пþÿ¸Àþÿ`Áþÿ PÂþÿh°ÂþÿÄþÿ0ÐÄþÿH ÅþÿPÉþÿàðÌþÿø0ÓþÿÙþÿ(°Úþÿx0Ûþÿ¨ÐÝþÿà°õþÿ0ýþÿ0ÿþÿØ3ÿÿ(	P6ÿÿx	0BÿÿÈ	@Bÿÿà	PNÿÿ0
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¼!È)Ðø	þÿÿoÿÿÿoðÿÿoùÿÿoºH!1¦1¶1Æ1Ö1æ1ö122&262F2V2f2v222¦2¶2Æ2Ö2æ2ö233&363F3V3f3v333¦3¶3Æ3Ö3æ3ö344&464F4V4f4v444¦4¶4Æ4Ö4æ4ö455&565F5V5f5v555¦5¶5Æ5Ö5æ5ö566&666F6V6f6v666 !Densify selected rows of a CSR matrix into a preallocated array.

    Like out[out_rows] = X[X_rows].toarray() but without copying.
    Only supported for dtype=np.float64.

    Parameters
    ----------
    X : scipy.sparse.csr_matrix, shape=(n_samples, n_features)
    X_rows : array, dtype=np.intp, shape=n_rows
    out_rows : array, dtype=np.intp, shape=n_rows
    out : array, shape=(arbitrary, n_features)
    Inplace row normalize using the l2 normInplace row normalize using the l1 normCompute mean and variance along axis 0 on a CSR or CSC matrix.

    last_mean, last_var are the statistics computed at the last step by this
    function. Both must be initilized to 0.0. last_n is the
    number of samples encountered until now and is initialized at 0.

    Parameters
    ----------
    X: CSR or CSC sparse matrix, shape (n_samples, n_features)
      Input data.

    last_mean: float array with shape (n_features,)
      Array of feature-wise means to update with the new data X.

    last_var: float array with shape (n_features,)
      Array of feature-wise var to update with the new data X.

    last_n: int
      Number of samples seen so far, before X.

    Returns
    -------

    updated_mean: float array with shape (n_features,)
      Feature-wise means

    updated_variance: float array with shape (n_features,)
      Feature-wise variances

    updated_n : int
      Updated number of samples seen

    References
    ----------

    T. Chan, G. Golub, R. LeVeque. Algorithms for computing the sample
      variance: recommendations, The American Statistician, Vol. 37, No. 3,
      pp. 242-247

    Also, see the non-sparse implementation of this in
    `utils.extmath._batch_mean_variance_update`.

    Compute mean and variance along axis 0 on a CSC matrix

    Parameters
    ----------
    X: CSC sparse matrix, shape (n_samples, n_features)
        Input data.

    Returns
    -------

    means: float array with shape (n_features,)
        Feature-wise means

    variances: float array with shape (n_features,)
        Feature-wise variances

    Compute mean and variance along axis 0 on a CSR matrix

    Parameters
    ----------
    X: CSR sparse matrix, shape (n_samples, n_features)
        Input data.

    Returns
    -------

    means: float array with shape (n_features,)
        Feature-wise means

    variances: float array with shape (n_features,)
        Feature-wise variances

    L2 norm of each row in CSR matrix X.Format string allocated too short.ndarray is not Fortran contiguousNon-native byte order not supportedFormat string allocated too short, see comment in numpy.pxd/volatile/gpatrini/dev/scikit-learn/sklearn/utils/sparsefuncs_fast.pyxunknown dtype code in numpy.pxd (%d)sklearn.utils.sparsefuncs_fastinplace_csr_row_normalize_l2inplace_csr_row_normalize_l1ndarray is not C contiguouscannot assign %d rows to %dincr_mean_variance_axis0csr_mean_variance_axis0csc_mean_variance_axis0__pyx_releasebufferlast_over_new_nassign_rows_csr__pyx_getbuffercsr_row_normsupdated_meanscipy.sparseRuntimeErrorupdated_varzeros_liken_featurescsr_matrixcsc_matrixValueErrorvariancesupdated_nn_sampleslast_meanX_indicesstartptrout_rowsnon_zeronew_meanlast_varX_indptrnew_varindicesfloat64col_indasarrayxrangelast_nindptr__import__endptrcountsX_rowsX_datazerosshaperangenumpynormsnew_nmeansint32dtype__test____main__diffdatasum_outindsprXnp*ZgZfZdqljihgfdbXQOLIHBÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ6!à´!°´!д!6!¶!3!г!·!à!<·! !#ð¶! !$à¶!( !
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