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œÿÿœÿÿû›ÿÿò›ÿÿé›ÿÿà›ÿÿכÿÿΛÿÿśÿÿ¼›ÿÿ+ãÿÿ"ãÿÿãÿÿãÿÿãÿÿBuffer dtype mismatch, expected %s%s%s but got %sBuffer dtype mismatch, expected '%s' but got %s in '%s.%s'__%s__ returned non-%s (type %.200s)Expected 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 expectedDoes not understand character buffer dtype format string ('%c')Big-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 ')'Cannot convert %.200s to %.200s%s.%s size changed, may indicate binary incompatibility%s.%s has the wrong size, try recompiling%s() got an unexpected keyword argument '%s'%s() got multiple values for keyword argument '%s'%s() takes %s %zd positional argument%s (%zd given)raise: arg 3 must be a traceback or Noneinstance exception may not have a separate valueraise: exception class must be a subclass of BaseExceptionsklearn.utils._random._sample_without_replacement_check_input_sample_without_replacement_check_input'NoneType' object is not iterabletoo many values to unpack (expected %zd)need more than %zd value%s to unpack'%100s' 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._random._sample_without_replacement_with_reservoir_sampling_sample_without_replacement_with_reservoir_samplingsklearn.utils._random._sample_without_replacement_with_pool_sample_without_replacement_with_poolsklearn.utils._random._sample_without_replacement_with_tracking_selection_sample_without_replacement_with_tracking_selectionsklearn.utils._random.sample_without_replacementcompiletime version %s of module '%.100s' does not match runtime version %sPyObject *(__pyx_t_5numpy_int_t, __pyx_t_5numpy_int_t, int __pyx_skip_dispatch, struct __pyx_opt_args_7sklearn_5utils_7_random_sample_without_replacement *__pyx_optional_args)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 runtimeš™™™™™É?
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+ð 0Ø8 	þÿÿoøÿÿÿo
ðÿÿo<ùÿÿof€í 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&''&'6'F'V'f'v'†'–'¦'¶'Æ'Ö'æ'ö'((&(6(F(V(@ò Sample integers without replacement.

    Select n_samples integers from the set [0, n_population) without
    replacement.


    Parameters
    ----------
    n_population : int,
        The size of the set to sample from.

    n_samples : int,
        The number of integer to sample.

    random_state : int, RandomState instance or None, optional (default=None)
        If int, random_state is the seed used by the random number generator;
        If RandomState instance, random_state is the random number generator;
        If None, the random number generator is the RandomState instance used
        by `np.random`.

    method : "auto", "tracking_selection" or "reservoir_sampling"
        If method == "auto", an algorithm is automatically selected.
        The subset of selected integer is not randomized.

        If method =="tracking_selection", a set based implementation is used
        which is suitable for `n_samples` <<< `n_population`.

        If method == "reservoir_sampling", a reservoir sampling algorithm is
        used which is suitable for high memory constraint or when
        O(`n_samples`) ~ O(`n_population`).
        The subset of selected integer is not randomized.

        If method == "pool", a pool based algorithm is particularly fast, even
        faster than the tracking selection method. Hovewer, a vector containing
        the entire population has to be initialized.
        If n_samples ~ n_population, the reservoir sampling method is faster.

    Returns
    -------
    out : array of size (n_samples, )
        The sampled subsets of integer. The subset of selected integer might
        not be randomized, see the method argument.
    Sample integers without replacement.

    Select n_samples integers from the set [0, n_population) without
    replacement.

    Time complexity of
        O((n_population - n_samples) * O(np.random.randint) + n_samples)
    Space complexity of O(n_samples)


    Parameters
    ----------
    n_population : int,
        The size of the set to sample from.

    n_samples : int,
         The number of integer to sample.

    random_state : int, RandomState instance or None, optional (default=None)
        If int, random_state is the seed used by the random number generator;
        If RandomState instance, random_state is the random number generator;
        If None, the random number generator is the RandomState instance used
        by `np.random`.

    Returns
    -------
    out : array of size (n_samples, )
        The sampled subsets of integer. The order of the items is not
        necessarily random. Use a random permutation of the array if the order
        of the items has to be randomized.
    Sample integers without replacement.

    Select n_samples integers from the set [0, n_population) without
    replacement.

    Time complexity: O(n_population +  O(np.random.randint) * n_samples)

    Space complexity of O(n_population + n_samples).


    Parameters
    ----------
    n_population : int,
        The size of the set to sample from.

    n_samples : int,
        The number of integer to sample.

    random_state : int, RandomState instance or None, optional (default=None)
        If int, random_state is the seed used by the random number generator;
        If RandomState instance, random_state is the random number generator;
        If None, the random number generator is the RandomState instance used
        by `np.random`.

    Returns
    -------
    out : array of size (n_samples, )
        The sampled subsets of integer.
    Sample integers without replacement.

    Select n_samples integers from the set [0, n_population) without
    replacement.

    Time complexity:
        - Worst-case: unbounded
        - Average-case:
            O(O(np.random.randint) * \sum_{i=1}^n_samples 1 /
                                              (1 - i / n_population)))
            <= O(O(np.random.randint) *
                   n_population * ln((n_population - 2)
                                     /(n_population - 1 - n_samples)))
            <= O(O(np.random.randint) *
                 n_population * 1 / (1 - n_samples / n_population))

    Space complexity of O(n_samples) in a python set.


    Parameters
    ----------
    n_population : int,
        The size of the set to sample from.

    n_samples : int,
        The number of integer to sample.

    random_state : int, RandomState instance or None, optional (default=None)
        If int, random_state is the seed used by the random number generator;
        If RandomState instance, random_state is the random number generator;
        If None, the random number generator is the RandomState instance used
        by `np.random`.

    Returns
    -------
    out : array of size (n_samples, )
        The sampled subsets of integer.
     Check that input are consistent for sample_without_replacement__pyx_releasebuffertracking_selectionreservoir_samplingcheck_random_state__pyx_getbufferrandom_staten_populationRuntimeError__import__ValueErrorn_samples__test____main__randintxrangemethodrangenumpyemptydtypepoolautointnpZgZfZdqlihgfdbQOLIHBsklearn.utils
Random utility function
=======================
This module complements missing features of ``numpy.random``.

The module contains:
    * Several algorithms to sample integers without replacement.

Format string allocated too short.Format string allocated too short, see comment in numpy.pxdunknown dtype code in numpy.pxd (%d)Non-native byte order not supportedndarray is not Fortran contiguousndarray is not C contiguousExpected a method name in %s, got %s. n_population should be greater or equal than n_samples, got n_samples > n_population (%s > %s)n_population should be greater than 0, got %s.ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ !˜! !˜!p! !˜!p! !˜!p! !˜!¨!p!X!à	!/
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