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steminc / scikit-learn python

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scikit-learn / utils / sparsefuncs_fast.so
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GÿÿGÿÿøFÿÿïFÿÿæFÿÿÝFÿÿÔFÿÿËFÿÿÂFÿÿ¹Fÿÿ¢Fÿÿ×ÿÿ~×ÿÿu×ÿÿl×ÿÿc×ÿÿBuffer dtype mismatch, expected %s%s%s but got %sBuffer dtype mismatch, expected '%s' but got %s in '%s.%s'__%.4s__ returned non-%.4s (type %.200s)value too large to convert to int while calling a Python objectNULL result without error in PyObject_Callsklearn/utils/sparsefuncs_fast.cExpected a dimension of size %zu, got %zuExpected %d dimensions, got %dUnexpected format string character: '%c'Python does not define a standard format string size for long double ('g')..Buffer dtype mismatch; next field is at offset %zd but %zd expectedCannot convert %.200s to %.200scan't convert negative value to unsigned intvalue too large to convert to unsigned int%.200s.%.200s is not a type object%s.%s size changed, may indicate binary incompatibility%.200s.%.200s has the wrong size, try recompiling%.200s() takes %.8s %zd positional argument%.1s (%zd given)Big-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 iterabletoo 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.utils.sparsefuncs_fast.inplace_csr_row_normalize_l1sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l2'%.200s' object does not support slice %.10ssklearn.utils.sparsefuncs_fast.assign_rows_csrsklearn.utils.sparsefuncs_fast.csc_mean_variance_axis0sklearn.utils.sparsefuncs_fast.csr_row_normsBuffer acquisition failed on assignment; and then reacquiring the old buffer failed too!sklearn.utils.sparsefuncs_fast.csr_mean_variance_axis0sklearn.utils.sparsefuncs_fast.add_row_csr%s() got multiple values for keyword argument '%s'%.200s() keywords must be strings%.200s() got an unexpected keyword argument '%.200s'Argument '%.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 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.utils.sparsefuncs_fastÿÿÿÿÿÿÿ;\*Ð÷þÿx üþÿgýþÿpÕýþÿ .þþÿÈÿþÿ 0ÿÿ¸ÿÿÐðÿÿèÿÿ ÿÿ8 ÿÿhÐÿÿ pÿÿÈ ÿÿà`ÿÿ(0ÿÿp	ÿÿØ°ÿÿð ÿÿÿÿhÀÿÿ ÿÿ ÿÿ0Àÿÿ`ÿÿ¸p5ÿÿp=ÿÿh ?ÿÿÐKÿÿ  Kÿÿ8PWÿÿ`Wÿÿ  gÿÿðð|ÿÿ@	}ÿÿX	Зÿÿ¨	àÿÿÀ	;ÿÿ
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=P! Ð%ðà	þÿÿo ÿÿÿoðÿÿoÆùÿÿoHM!&-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/æ/ö/00&060F0V0f0v000¦0¶0Æ0Ö0æ0ö011&161F1V1f1v111¦1¶1Æ1Ö1R!Densify selected rows of a CSR matrix into a preallocated array.

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    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 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.pxdunknown dtype code in numpy.pxd (%d)/files/work/opensource/scikit-learn/sklearn/utils/sparsefuncs_fast.pyxsklearn.utils.sparsefuncs_fastinplace_csr_row_normalize_l2inplace_csr_row_normalize_l1ndarray is not C contiguouscannot assign %d rows to %dcsr_mean_variance_axis0csc_mean_variance_axis0__pyx_releasebufferassign_rows_csr__pyx_getbuffercsr_row_normsscipy.sparseRuntimeErrorzeros_liken_featuresValueErrorvariancesn_samplesX_indicesstartptrout_rowsnon_zeroX_indptrindicesfloat64col_indasarrayxrangeindptr__import__endptrcountsX_rowsX_datazerosshaperangenumpynormsmeansint32dtype__test____main__diffdatasum_outindsprXnp*ZgZfZdqljihgfdbXQOLIHBÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ l!l!àj!èj!Hl!àX!<@l! X!#8l! X!$0l!ëZ!
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