<commit_before># Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)


from .kern import Kern
from ...core.parameterization import Param
from ...core.parameterization.transformations import Logexp
from ...util.linalg import tdot
from ... import util
import numpy as np
from scipy import integrate
from ...util.config import config # for assesing whether to use cython
from ...util.caching import Cache_this

try:
    import stationary_cython
except ImportError:
    print('warning: failed to import cython module: falling back to numpy')
    config.set('cython', 'working', 'false')


class Stationary(Kern):
    """
    Stationary kernels (covariance functions).

    Stationary covariance fucntion depend only on r, where r is defined as

      r = \sqrt{ \sum_{q=1}^Q (x_q - x'_q)^2 }

    The covariance function k(x, x' can then be written k(r).

    In this implementation, r is scaled by the lengthscales parameter(s):

      r = \sqrt{ \sum_{q=1}^Q \frac{(x_q - x'_q)^2}{\ell_q^2} }.

    By default, there's only one lengthscale: seaprate lengthscales for each
    dimension can be enables by setting ARD=True.
<commit_msg>[cython import] error notices where it happens<commit_after># Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)


from .kern import Kern
from ...core.parameterization import Param
from ...core.parameterization.transformations import Logexp
from ...util.linalg import tdot
from ... import util
import numpy as np
from scipy import integrate
from ...util.config import config # for assesing whether to use cython
from ...util.caching import Cache_this

try:
    import stationary_cython
except ImportError:
    print('warning in sationary: failed to import cython module: falling back to numpy')
    config.set('cython', 'working', 'false')


class Stationary(Kern):
    """
    Stationary kernels (covariance functions).

    Stationary covariance fucntion depend only on r, where r is defined as

      r = \sqrt{ \sum_{q=1}^Q (x_q - x'_q)^2 }

    The covariance function k(x, x' can then be written k(r).

    In this implementation, r is scaled by the lengthscales parameter(s):

      r = \sqrt{ \sum_{q=1}^Q \frac{(x_q - x'_q)^2}{\ell_q^2} }.

    By default, there's only one lengthscale: seaprate lengthscales for each
    dimension can be enables by setting ARD=True.

{'fraction_non_alphanumeric': 0.1013179571663921, 'fraction_numerical': 0.008237232289950576, 'mean_word_length': 3.6801541425818884, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '744647', 'n_tokens_mistral': 765, 'n_tokens_neox': 714, 'n_words': 288}