






'''simple docstring'''

import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize



def 							snake_case					(     a_							: ndarray	)       ->				Dict:





      """simple docstring"""

      return np.dot(__a    ,   __a	)




class     A    :






 """simple docstring"""





 def __init__(							self			,     *,
     __lowerCAmelCase = np.inf			,     __lowerCAmelCase = "linear"			,     __lowerCAmelCase = 0.0			,     ):
       UpperCamelCase_				:   Optional[Any]					    =							regularization
       UpperCamelCase_				:   Dict					    =							gamma
       if kernel == "linear":
             UpperCamelCase_				:   Optional[Any]					    =							self.__linear
       elif kernel == "rbf":
             if self.gamma == 0:
                   raise ValueError("""rbf kernel requires gamma""" )
             if not isinstance(self.gamma			,     (float, int) ):
                   raise ValueError("""gamma must be float or int""" )
             if not self.gamma > 0:
                   raise ValueError("""gamma must be > 0""" )
             UpperCamelCase_				:   List[Any]					    =							self.__rbf
             # in the future, there could be a default value like in sklearn
             # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
             # previously it was 1/(n_features)
       else:
             UpperCamelCase_				:   int					    =							F"Unknown kernel: {kernel}"
             raise ValueError(SCREAMING_SNAKE_CASE_ )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       return np.dot(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       return np.exp(-(self.gamma * norm_squared(vectora - vectora )) )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Tuple					    =							observations
       UpperCamelCase_				:   Optional[int]					    =							classes

       # using Wolfe's Dual to calculate w.
       # Primal problem: minimize 1/2*norm_squared(w)
       #   constraint: yn(w . xn + b) >= 1
       #
       # With l a vector
       # Dual problem: maximize sum_n(ln) -
       #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
       #   constraint: self.C >= ln >= 0
       #           and sum_n(ln*yn) = 0
       # Then we get w using w = sum_n(ln*yn*xn)
       # At the end we can get b ~= mean(yn - w . xn)
       #
       # Since we use kernels, we only need l_star to calculate b
       # and to classify observations

       ((UpperCamelCase_ )    ,			 )				:   Optional[Any]					    =							np.shape(SCREAMING_SNAKE_CASE_ )

       def to_minimize(__lowerCAmelCase ) -> float:
             UpperCamelCase_				:   Dict					    =							0
             ((UpperCamelCase_ )    ,			 )				:   Optional[Any]					    =							np.shape(SCREAMING_SNAKE_CASE_ )
             for i in range(SCREAMING_SNAKE_CASE_ ):
                   for j in range(SCREAMING_SNAKE_CASE_ ):
                         s += (
                             candidate[i]
                             * candidate[j]
                             * classes[i]
                             * classes[j]
                             * self.kernel(observations[i]			,     observations[j] )
                         )
             return 1 / 2 * s - sum(SCREAMING_SNAKE_CASE_ )

       UpperCamelCase_				:   Tuple					    =							LinearConstraint(SCREAMING_SNAKE_CASE_			,     0			,     0 )
       UpperCamelCase_				:   Optional[int]					    =							Bounds(0			,     self.regularization )

       UpperCamelCase_				:   Optional[int]					    =							minimize(
           SCREAMING_SNAKE_CASE_			,     np.ones(SCREAMING_SNAKE_CASE_ )			,     bounds=SCREAMING_SNAKE_CASE_			,     constraints=[ly_contraint] ).x
       UpperCamelCase_				:   Optional[Any]					    =							l_star

       # calculating mean offset of separation plane to points
       UpperCamelCase_				:   int					    =							0
       for i in range(SCREAMING_SNAKE_CASE_ ):
             for j in range(SCREAMING_SNAKE_CASE_ ):
                   s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
                       observations[i]			,     observations[j] )
       UpperCamelCase_				:   Union[str, Any]					    =							s / n





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   int					    =							sum(
           self.optimum[n]
           * self.classes[n]
           * self.kernel(self.observations[n]			,     SCREAMING_SNAKE_CASE_ )
           for n in range(len(self.classes ) ) )
       return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
    import doctest

    doctest.testmod()














'''simple docstring'''

from __future__ import annotations

import os
from typing import Any

import requests

UpperCamelCase							="https://api.github.com"

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
UpperCamelCase							=BASE_URL + "/user"

# https://github.com/settings/tokens
UpperCamelCase							=os.environ.get("USER_TOKEN", "")



def 							snake_case					(     a_							: str	)       ->				dict[Any, Any]:





      """simple docstring"""

      UpperCamelCase_				:   Tuple					    =							{
          """Authorization""": f"token {auth_token}",
          """Accept""": """application/vnd.github.v3+json""",
      }
      return requests.get(a_    ,   headers=a_	).json()


if __name__ == "__main__":  # pragma: no cover
      if USER_TOKEN:
            for key, value in fetch_github_info(USER_TOKEN).items():
                  print(f"{key}: {value}")
      else:
            raise ValueError("'USER_TOKEN' field cannot be empty.")






