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/ utils / _random.pypy3-71-x86_64-linux-gnu.so
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ÀIÄu¿éÿÿÿèùÿÿHÃëÉH5) H5Ï H8è>YÿÿéóþÿÿHèéùÿÿH) H5¬ H8èYÿÿéþÿÿ HìHÄà 'bool' 'char' 'signed char' 'unsigned char' 'short' 'unsigned short' 'int' 'unsigned int' 'long' 'unsigned long' 'long long' 'unsigned long long' 'float' 'double' 'long double' a struct Python object a pointer a string end unparseable format string 'complex long double' 'complex double' 'complex float' name '%U' is not defined sklearn/utils/_random.c %s (%s:%d) buffer dtype an integer is required sklearn/utils/_random.pyx exactly Missing type object at least at most float division sample_without_replacement %d.%d %s builtins cython_runtime __builtins__ __init__.pxd __pyx_capi__ type.pxd numpy flatiter broadcast ndarray ufunc numpy.core._multiarray_umath _ARRAY_API _ARRAY_API not found _ARRAY_API is NULL pointer numpy.import_array cannot import name %S init sklearn.utils._random _our_rand_r_py int_t `pÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿPpÿÿ qÿÿ qÿÿqÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿqÿÿpqÿÿ qÿÿ qÿÿ`qÿÿ qÿÿ qÿÿPqÿÿ@qÿÿ0qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿ qÿÿqÿÿqÿÿðpÿÿ qÿÿØpÿÿÀpÿÿ°pÿÿ pÿÿ qÿÿ qÿÿpÿÿ qÿÿ qÿÿ qÿÿpÿÿppÿÿ qÿÿpÿÿÐxÿÿ yÿÿ yÿÿÐxÿÿ yÿÿ yÿÿ yÿÿ yÿÿ yÿÿàxÿÿÀxÿÿ yÿÿ yÿÿ°xÿÿ yÿÿ yÿÿ°xÿÿ°xÿÿ°xÿÿ yÿÿ yÿÿ yÿÿ yÿÿ yÿÿ yÿÿ yÿÿ yÿÿ yÿÿ yÿÿ yÿÿ yÿÿ yÿÿ yÿÿ yÿÿ yÿÿÐxÿÿÐxÿÿ°xÿÿ yÿÿÀxÿÿðxÿÿàxÿÿÀxÿÿ yÿÿ yÿÿ°xÿÿ yÿÿ yÿÿ yÿÿÐxÿÿ°xÿÿ yÿÿÐxÿÿ¢|ÿÿ¬|ÿÿ¬|ÿÿ¢|ÿÿ¬|ÿÿ¬|ÿÿ¬|ÿÿ¬|ÿÿ¬|ÿÿ¢|ÿÿ¢|ÿÿ¬|ÿÿ¬|ÿÿ¢|ÿÿ¬|ÿÿ¬|ÿÿyÿÿyÿÿ¢|ÿÿ¬|ÿÿ¬|ÿÿ¬|ÿÿ¬|ÿÿ¬|ÿÿ¬|ÿÿ¬|ÿÿ¬|ÿÿ¬|ÿÿ¬|ÿÿ¬|ÿÿ¬|ÿÿ¬|ÿÿ¬|ÿÿ¬|ÿÿ¬|ÿÿ|ÿÿz|ÿÿ|ÿÿ¬|ÿÿ|ÿÿ|ÿÿ|ÿÿ|ÿÿ¬|ÿÿ¬|ÿÿ|ÿÿ¬|ÿÿ¬|ÿÿ¬|ÿÿ|ÿÿ|ÿÿ¬|ÿÿ|ÿÿh{ÿÿx{ÿÿx{ÿÿh{ÿÿx{ÿÿx{ÿÿx{ÿÿx{ÿÿx{ÿÿ(zÿÿX{ÿÿx{ÿÿx{ÿÿX{ÿÿx{ÿÿx{ÿÿyÿÿyÿÿyÿÿx{ÿÿx{ÿÿx{ÿÿx{ÿÿx{ÿÿx{ÿÿx{ÿÿx{ÿÿx{ÿÿx{ÿÿx{ÿÿx{ÿÿx{ÿÿx{ÿÿx{ÿÿx{ÿÿh{ÿÿh{ÿÿ¨zÿÿx{ÿÿzÿÿÀzÿÿ(zÿÿX{ÿÿx{ÿÿx{ÿÿX{ÿÿx{ÿÿx{ÿÿx{ÿÿh{ÿÿyÿÿx{ÿÿh{ÿÿT{ÿÿ¤zÿÿ¤zÿÿT{ÿÿ¤zÿÿ¤zÿÿ¤zÿÿ¤zÿÿ¤zÿÿD{ÿÿ|{ÿÿ¤zÿÿ¤zÿÿ4{ÿÿ¤zÿÿ¤zÿÿ4{ÿÿ4{ÿÿ4{ÿÿ¤zÿÿ¤zÿÿ¤zÿÿ¤zÿÿ¤zÿÿ¤zÿÿ¤zÿÿ¤zÿÿ¤zÿÿ¤zÿÿ¤zÿÿ¤zÿÿ¤zÿÿ¤zÿÿ¤zÿÿ¤zÿÿT{ÿÿT{ÿÿ{ÿÿ¤zÿÿ¤{ÿÿd{ÿÿD{ÿÿ|{ÿÿ¤zÿÿ¤zÿÿ4{ÿÿ¤zÿÿ¤zÿÿ¤zÿÿT{ÿÿ4{ÿÿ¤zÿÿT{ÿÿ}ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿ°|ÿÿÈ|ÿÿÈ|ÿÿ°|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿ°|ÿÿpÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ{ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿ ÿÿÈ|ÿÿp~ÿÿXÿÿpÿÿ@}ÿÿXÿÿÈ|ÿÿ@}ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿ@}ÿÿ@}ÿÿÈ|ÿÿÈ|ÿÿ@}ÿÿÈ|ÿÿÈ|ÿÿ@}ÿÿÈ|ÿÿ@}ÿÿÈ|ÿÿÈ|ÿÿ¸~ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿ~ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿXÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿ@}ÿÿ@}ÿÿ@}ÿÿÈ|ÿÿ@}ÿÿ@}ÿÿ@}ÿÿ@}ÿÿÈ|ÿÿÈ|ÿÿ@}ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿ@}ÿÿ@}ÿÿÈ|ÿÿð|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ}ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿÈ|ÿÿ~ÿÿDðÿÿ;ðÿÿ2ðÿÿ)ðÿÿ ðÿÿ Format string allocated too short. numpy.core.umath failed to import ndarray is not Fortran contiguous n_population should be greater than 0, got %s. n_population should be greater or equal than n_samples, got n_samples > n_population ( Non-native byte order not supported Format string allocated too short, see comment in numpy.pxd unknown dtype code in numpy.pxd (%d) numpy.core.multiarray failed to import
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
ndarray is not C contiguous sklearn/utils/_random.pyx Expected a method name in sklearn.utils._random tracking_selection reservoir_sampling cline_in_traceback check_random_state _our_rand_r_py random_state n_population RuntimeError DEFAULT_SEED permutation ImportError ValueError n_samples __pyx_capi__ randint my_seed method __import__ range numpy empty dtype __test__ seed pool __name__ __main__ auto int , got np . ) > Buffer dtype mismatch, expected %s%s%s but got %s Buffer 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 PyObject Does not understand character buffer dtype format string ('%c') %s() got multiple values for keyword argument '%U' %.200s() keywords must be strings %s() got an unexpected keyword argument '%U' Unexpected format string character: '%c' Expected a dimension of size %zu, got %zu Expected %d dimensions, got %d Python does not define a standard format string size for long double ('g').. Buffer dtype mismatch; next field is at offset %zd but %zd expected Big-endian buffer not supported on little-endian compiler Buffer acquisition: Expected '{' after 'T' Cannot handle repeated arrays in format string Expected a dimension of size %zu, got %d Expected a comma in format string, got '%c' Expected %d dimension(s), got %d Unexpected 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) __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) value too large to convert to npy_uint32 can't convert negative value to npy_uint32 calling %R should have returned an instance of BaseException, not %R raise: exception class must be a subclass of BaseException sklearn.utils._random._sample_without_replacement_check_input _sample_without_replacement_check_input %.200s() takes %.8s %zd positional argument%.1s (%zd given) Cannot convert %.200s to %.200s sklearn.utils._random._sample_without_replacement_with_reservoir_sampling _sample_without_replacement_with_reservoir_sampling sklearn.utils._random._our_rand_r_py sklearn.utils._random._sample_without_replacement_with_tracking_selection _sample_without_replacement_with_tracking_selection sklearn.utils._random._sample_without_replacement_with_pool _sample_without_replacement_with_pool sklearn.utils._random.sample_without_replacement compiletime version %s of module '%.100s' does not match runtime version %s __pyx_t_7sklearn_5utils_7_random_UINT32_t PyObject *(__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 object module compiled against ABI version 0x%x but this version of numpy is 0x%x module compiled against API version 0x%x but this version of numpy is 0x%x FATAL: module compiled as unknown endian FATAL: module compiled as little endian, but detected different endianness at runtime ð? {®Gáz?®Gáz®ï?É?; :ÿÿ ?ÿÿH (?ÿÿ° @ÿÿ Aÿÿà Wÿÿ` Xÿÿx Yÿÿ ¨YÿÿP Zÿÿh xZÿÿ 8_ÿÿ0 8aÿÿ Øaÿÿ° xgÿÿ mÿÿ 8oÿÿð ¸oÿÿ qÿÿ@ (rÿÿ¸ sÿÿà Øxÿÿ8 Xÿÿ¨ xÿÿø ؔÿÿh Hÿÿ¸ ¬ÿÿ è³ÿÿx ØÈÿÿÈ 8Ðÿÿ8 øÛÿÿ zR x $ à8ÿÿ FJ w ?;*3$" D È=ÿÿ \ Uÿÿx t Wÿÿy pWÿÿ DK
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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", "reservoir_sampling" or "pool"
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. 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.
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