

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

def       a__					(		UpperCAmelCase							:      ndarray      )  ->						float:
  return np.dot(UpperCAmelCase    ,			UpperCAmelCase      )





class  __UpperCAmelCase					:



   def __init__(							self							:					Tuple,		*,
       __A							:					float = np.inf,		__A							:					str = "linear",		__A							:					float = 0.0,		):
     UpperCAmelCase	:							Union[str, Any]						=			regularization
     UpperCAmelCase	:							Optional[int]						=			gamma
     if kernel == "linear":
       UpperCAmelCase	:							Union[str, 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[str]						=			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	:							Any						=			F'''Unknown kernel: {kernel}'''
       raise ValueError(__A						)



   def 	__magic_name__   (							self							:					Optional[int],		__A							:					ndarray,		__A							:					ndarray						):
     return np.dot(__A,		__A						)



   def 	__magic_name__   (							self							:					Tuple,		__A							:					ndarray,		__A							:					ndarray						):
     return np.exp(-(self.gamma * norm_squared(vectora - vectora						))						)



   def 	__magic_name__   (							self							:					Optional[Any],		__A							:					list[ndarray],		__A							:					ndarray						):
     UpperCAmelCase	:							str						=			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)			,						)	:							str						=			np.shape(__A						)

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

     UpperCAmelCase	:							List[str]						=			LinearConstraint(__A,		0,		0						)
     UpperCAmelCase	:							Any						=			Bounds(0,		self.regularization						)

     UpperCAmelCase	:							str						=			minimize(
         __A,		np.ones(__A						),		bounds=__A,		constraints=[ly_contraint]						).x
     UpperCAmelCase	:							List[str]						=			l_star

     # calculating mean offset of separation plane to points
     UpperCAmelCase	:							Tuple						=			0
     for i in range(__A						):
       for j in range(__A						):
         s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
             observations[i],		observations[j]						)
     UpperCAmelCase	:							List[str]						=			s / n





   def 	__magic_name__   (							self							:					str,		__A							:					ndarray						):
     UpperCAmelCase	:							str						=			sum(
         self.optimum[n]
         * self.classes[n]
         * self.kernel(self.observations[n],		__A						)
         for n in range(len(self.classes						)						)						)
     return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
     import doctest

     doctest.testmod()








from math import log

from scipy.constants import Boltzmann, physical_constants

_lowerCamelCase    :       Tuple        =							3_0_0  # TEMPERATURE (unit = K)

def       a__					(		UpperCAmelCase							:      float    ,			UpperCAmelCase							:      float    ,			UpperCAmelCase							:      float    ,			)  ->						float:

  if donor_conc <= 0:
    raise ValueError('''Donor concentration should be positive'''      )
  elif acceptor_conc <= 0:
    raise ValueError('''Acceptor concentration should be positive'''      )
  elif intrinsic_conc <= 0:
    raise ValueError('''Intrinsic concentration should be positive'''      )
  elif donor_conc <= intrinsic_conc:
    raise ValueError(
        '''Donor concentration should be greater than intrinsic concentration'''      )
  elif acceptor_conc <= intrinsic_conc:
    raise ValueError(
        '''Acceptor concentration should be greater than intrinsic concentration'''      )
  else:
    return (
        Boltzmann
        * T
        * log((donor_conc * acceptor_conc) / intrinsic_conc**2      )
        / physical_constants["electron volt"][0]
    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





