



'''simple docstring'''



import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def 					UpperCAmelCase   (						A  :				ndarray				):
							return np.dot(A					,		A				)
class 	lowerCamelCase_							:







						def __init__(  self   :      List[Any]				,    *,
						    lowerCAmelCase__   :      float = np.inf				,    lowerCAmelCase__   :      str = "linear"				,    lowerCAmelCase__   :      float = 0.0				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			regularization
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			gamma
													if kernel == "linear":
																				SCREAMING_SNAKE_CASE					:      int     					=			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					:      Optional[int]     					=			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					:      List[Any]     					=			F"""Unknown kernel: {kernel}"""
																				raise ValueError(lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				,    lowerCAmelCase__   :      ndarray				,    lowerCAmelCase__   :      ndarray				):

													"""simple docstring"""
													return np.dot(lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      ndarray				,    lowerCAmelCase__   :      ndarray				):

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







						def 					__lowercase    (  self   :      List[Any]				,    lowerCAmelCase__   :      list[ndarray]				,    lowerCAmelCase__   :      ndarray				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			observations
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			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)					:      str     					=			np.shape(lowerCAmelCase__				)

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

													SCREAMING_SNAKE_CASE					:      int     					=			LinearConstraint(lowerCAmelCase__				,    0				,    0				)
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			Bounds(0				,    self.regularization				)

													SCREAMING_SNAKE_CASE					:      int     					=			minimize(
													    lowerCAmelCase__				,    np.ones(lowerCAmelCase__				)				,    bounds=lowerCAmelCase__				,    constraints=[ly_contraint]				).x
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			l_star

													# calculating mean offset of separation plane to points
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			0
													for i in range(lowerCAmelCase__				):
																				for j in range(lowerCAmelCase__				):
																											s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
																											    observations[i]				,    observations[j]				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			s / n







						def 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      ndarray				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			sum(
													    self.optimum[n]
													    * self.classes[n]
													    * self.kernel(self.observations[n]				,    lowerCAmelCase__				)
													    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 urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id'''					,		['''canonical_dataset_name''', '''org-name/dataset-name''']				)
@pytest.mark.parametrize('''path'''					,		['''filename.csv''', '''filename with blanks.csv''']				)
@pytest.mark.parametrize('''revision'''					,		[None, '''v2''']				)
def 					UpperCAmelCase   (						A  :				int					,		A  :				Tuple					,		A  :				Any				):
							SCREAMING_SNAKE_CASE					:      int     					=			hf_hub_url(repo_id=A					,		path=A					,		revision=A				)
							assert url == F"""https://huggingface.co/datasets/{repo_id}/resolve/{revision or "main"}/{quote(A				)}"""




