
'''simple docstring'''







import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str							):



						"""simple docstring"""





						return np.dot(__snake_case      ,      __snake_case							)






class  lowerCAmelCase__   :






					def __init__(   self				,							*,
					    __SCREAMING_SNAKE_CASE = np.inf				,							__SCREAMING_SNAKE_CASE = "linear"				,							__SCREAMING_SNAKE_CASE = 0.0				,							):


											"""simple docstring"""





											lowercase_	:     Any													=       regularization
											lowercase_	:     str													=       gamma
											if kernel == "linear":
																	lowercase_	:     List[str]													=       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'''		)
																	lowercase_	:     Dict													=       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:
																	lowercase_	:     List[Any]													=       F'''Unknown kernel: {kernel}'''
																	raise ValueError(_SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return np.dot(_SCREAMING_SNAKE_CASE				,							_SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return np.exp(-(self.gamma * norm_squared(vectora - vectora		))		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       observations
											lowercase_	:     List[str]													=       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

											(lowercase_	)	:     Dict													=       np.shape(_SCREAMING_SNAKE_CASE		)

											def to_minimize(__SCREAMING_SNAKE_CASE		) -> float:
																	lowercase_	:     Optional[Any]													=       0
																	(lowercase_	)	:     List[str]													=       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		)

											lowercase_	:     Tuple													=       LinearConstraint(_SCREAMING_SNAKE_CASE				,							0				,							0		)
											lowercase_	:     List[Any]													=       Bounds(0				,							self.regularization		)

											lowercase_	:     Optional[int]													=       minimize(
											    _SCREAMING_SNAKE_CASE				,							np.ones(_SCREAMING_SNAKE_CASE		)				,							bounds=_SCREAMING_SNAKE_CASE				,							constraints=[ly_contraint]		).x
											lowercase_	:     str													=       l_star

											# calculating mean offset of separation plane to points
											lowercase_	:     str													=       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]		)
											lowercase_	:     Dict													=       s / n






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Dict													=       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"""






import os
from math import logaa


def 			_lowercase				(  __snake_case = "base_exp.txt"	)						->   int:
			__lowerCAmelCase      :  float												=       0
			__lowerCAmelCase      :  Any												=       0
			for i, line in enumerate(open(os.path.join(os.path.dirname(__snake_case	)					,__snake_case	)	)	):
						__lowerCAmelCase						,	__lowerCAmelCase      :  List[str]												=       list(map(__snake_case					,line.split(","	)	)	)
						if x * logaa(__snake_case	) > largest:
									__lowerCAmelCase      :  Tuple												=       x * logaa(__snake_case	)
									__lowerCAmelCase      :  Optional[Any]												=       i + 1
			return result


if __name__ == "__main__":
	print(solution())