





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






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				ndarray     ):
		return np.dot(UpperCamelCase__       , UpperCamelCase__     )




class 	UpperCamelCase_							:


							def __init__(				self     :int				,					*,
							    __A     :float = np.inf				,					__A     :str = "linear"				,					__A     :float = 0.0				,					) -> None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   regularization
									SCREAMING_SNAKE_CASE__								=   gamma
									if kernel == "linear":
											SCREAMING_SNAKE_CASE__								=   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"""		)
											SCREAMING_SNAKE_CASE__								=   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:
											SCREAMING_SNAKE_CASE__								=   f'''Unknown kernel: {kernel}'''
											raise ValueError(__A		)


							def   _snake_case	(				self     :Optional[Any]				,					__A     :ndarray				,					__A     :ndarray		) -> float:



									"""simple docstring"""



									return np.dot(__A				,					__A		)


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :ndarray				,					__A     :ndarray		) -> float:



									"""simple docstring"""



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


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :list[ndarray]				,					__A     :ndarray		) -> None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   observations
									SCREAMING_SNAKE_CASE__								=   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

									((SCREAMING_SNAKE_CASE__)						,    )								=   np.shape(__A		)

									def to_minimize(__A     :ndarray		) -> float:
											SCREAMING_SNAKE_CASE__								=   0
											((SCREAMING_SNAKE_CASE__)						,    )								=   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		)

									SCREAMING_SNAKE_CASE__								=   LinearConstraint(__A				,					0				,					0		)
									SCREAMING_SNAKE_CASE__								=   Bounds(0				,					self.regularization		)

									SCREAMING_SNAKE_CASE__								=   minimize(
									    __A				,					np.ones(__A		)				,					bounds=__A				,					constraints=[ly_contraint]		).x
									SCREAMING_SNAKE_CASE__								=   l_star

									# calculating mean offset of separation plane to points
									SCREAMING_SNAKE_CASE__								=   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]		)
									SCREAMING_SNAKE_CASE__								=   s / n


							def   _snake_case	(				self     :Optional[int]				,					__A     :ndarray		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   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()






import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate




@dataclass(frozen=UpperCamelCase__     )
class 	UpperCamelCase_							(  UpperCamelCase__     ):
							# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
							lowerCamelCase_							     =       field(default="text-classification"					,	metadata={"include_in_asdict_even_if_is_default": True}     )
							lowerCamelCase_							     =       Features({"text": Value("string"     )}     )
							lowerCamelCase_							     =       Features({"labels": ClassLabel}     )
							lowerCamelCase_							     =       "text"
							lowerCamelCase_							     =       "labels"


							def   _snake_case	(				self     :Any				,					__A     :Dict		) -> Optional[Any]:



									"""simple docstring"""



									if self.label_column not in features:
											raise ValueError(f'''Column {self.label_column} is not present in features.'''		)
									if not isinstance(features[self.label_column]				,					__A		):
											raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''		)
									SCREAMING_SNAKE_CASE__								=   copy.deepcopy(self		)
									SCREAMING_SNAKE_CASE__								=   self.label_schema.copy()
									SCREAMING_SNAKE_CASE__								=   features[self.label_column]
									SCREAMING_SNAKE_CASE__								=   label_schema
									return task_template


							@property
							def   _snake_case	(				self     :str		) -> Dict[str, str]:



									"""simple docstring"""



									return {
									    self.text_column: "text",
									    self.label_column: "labels",
									}