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Random utility function
=======================
This module complements missing features of ``numpy.random``.

The module contains:
    * Several algorithms to sample integers without replacement.
    * Fast rand_r alternative based on xor shifts
sklearn/utils/_random.pyxExpected a method name in sklearn.utils._randomtracking_selectionreservoir_samplingcline_in_tracebackcheck_random_state_our_rand_r_pyrandom_staten_populationDEFAULT_SEEDpermutationImportErrorValueErrorn_samples__pyx_capi__randintmy_seedmethod__import__rangenumpyemptydtype__test__seedpool__name____main__auto, got np. ) > Buffer dtype mismatch, expected %s%s%s but got %sBuffer dtype mismatch, expected '%s' but got %s in '%s.%s'%.200s.%.200s is not a type object%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject%s.%s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObjectInterpreter change detected - this module can only be loaded into one interpreter per process.Unexpected format string character: '%c'%s() got multiple values for keyword argument '%U'%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'join() result is too long for a Python string while calling a Python objectNULL result without error in PyObject_Call__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)can't convert negative value to npy_uint32value too large to convert to npy_uint32calling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseExceptionsklearn.utils._random._our_rand_r_pysklearn.utils._random._sample_without_replacement_check_input_sample_without_replacement_check_input%.200s() takes %.8s %zd positional argument%.1s (%zd given)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 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 ')'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)Cannot convert %.200s to %.200ssklearn.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_replacement'%.200s' object is unsliceableModule '_random' has already been imported. Re-initialisation is not supported.compiletime version %s of module '%.100s' does not match runtime version %s__pyx_t_7sklearn_5utils_7_random_UINT32_tPyObject *(__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)_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 runtimeð?{®Gáz„?®Gáz®ï?š™™™™™É?
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XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX@
60F0V0f0v0†0–0¦0¶0Æ0Ö0æ0ö011&161F1V1f1v1†1–1¦1¶1Æ1Ö1æ1ö122&262F2V2f2v2†2–2¦2¶2Æ2Ö2æ2ö233&363F3V3f3v3†3–3¦3¶3Æ3Ö3æ3ö344&464F4V4f4v4†4–4¦4¶4Æ4Ö4æ4ö455&565F5V5f5v5†5ÿÿÿÿÿÿÿÿPython utils to test the our_rand_r functionSample 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, 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", "reservoir_sampling", "pool"},             default='auto'
        If method == "auto", the ratio of n_samples / n_population is used
        to determine which algorithm to use:
        If ratio is between 0 and 0.01, tracking selection is used.
        If ratio is between 0.01 and 0.99, numpy.random.permutation is used.
        If ratio is greater than 0.99, reservoir sampling is used.
        The order of the selected integers is undefined. If a random order is
        desired, the selected subset should be shuffled.

        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 order of the selected integers is undefined. If a random order is
        desired, the selected subset should be shuffled.

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

    Returns
    -------
    out : ndarray of shape (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, 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 : ndarray of shape (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, 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 : ndarray of shape (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, 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 : ndarray of shape (n_samples,)
        The sampled subsets of integer.
     Check that input are consistent for sample_without_replacementÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿXC
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