





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





def    __UpperCamelCase      (    a) ->float:
					return np.dot(a,     a)

class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					def __init__( self	,  *,
					    _lowerCAmelCase = np.inf	,  _lowerCAmelCase = "linear"	,  _lowerCAmelCase = 0.0	,  ):
										lowerCamelCase__       =       regularization
										lowerCamelCase__       =       gamma
										if kernel == "linear":
															lowerCamelCase__       =       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"	)
															lowerCamelCase__       =       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:
															lowerCamelCase__       =       F"Unknown kernel: {kernel}"
															raise ValueError(_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										return np.dot(_lowerCAmelCase	,  _lowerCAmelCase	)





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





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       observations
										lowerCamelCase__       =       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

										((lowerCamelCase__)    ,		)       =       np.shape(_lowerCAmelCase	)

										def to_minimize(_lowerCAmelCase	) -> float:
															lowerCamelCase__       =       0
															((lowerCamelCase__)    ,		)       =       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	)

										lowerCamelCase__       =       LinearConstraint(_lowerCAmelCase	,  0	,  0	)
										lowerCamelCase__       =       Bounds(0	,  self.regularization	)

										lowerCamelCase__       =       minimize(
										    _lowerCAmelCase	,  np.ones(_lowerCAmelCase	)	,  bounds=_lowerCAmelCase	,  constraints=[ly_contraint]	).x
										lowerCamelCase__       =       l_star

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






					def 					__magic_name__ ( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       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()








def    __UpperCamelCase      (    a,     a) ->Dict:
					print("\nThe shortest path matrix using Floyd Warshall algorithm\n")
					for i in range(a):
										for j in range(a):
															if dist[i][j] != float("inf"):
																				print(int(dist[i][j]),     end="\t")
															else:
																				print("INF",     end="\t")
										print()





def    __UpperCamelCase      (    a,     a) ->Optional[int]:
					lowerCamelCase__       =       [[float("inf") for _ in range(a)] for _ in range(a)]

					for i in range(a):
										for j in range(a):
															lowerCamelCase__       =       graph[i][j]

															# check vertex k against all other vertices (i, j)
					for k in range(a):
										# looping through rows of graph array
										for i in range(a):
															# looping through columns of graph array
															for j in range(a):
																				if (
																				    dist[i][k] != float("inf")
																				    and dist[k][j] != float("inf")
																				    and dist[i][k] + dist[k][j] < dist[i][j]
																				):
																									lowerCamelCase__       =       dist[i][k] + dist[k][j]

					_print_dist(a,     a)
					return dist, v


if __name__ == "__main__":
						A_												=     int(input("Enter number of vertices: "))
						A_												=     int(input("Enter number of edges: "))

						A_												=     [[float("inf") for i in range(v)] for j in range(v)]

						for i in range(v):
												A_												=     0.0

												# src and dst are indices that must be within the array size graph[e][v]
												# failure to follow this will result in an error
						for i in range(e):
												print("\nEdge ", i + 1)
												A_												=     int(input("Enter source:"))
												A_												=     int(input("Enter destination:"))
												A_												=     float(input("Enter weight:"))
												A_												=     weight

						floyd_warshall(graph, v)

						# Example Input
						# Enter number of vertices: 3
						# Enter number of edges: 2

						# # generated graph from vertex and edge inputs
						# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
						# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

						# specify source, destination and weight for edge #1
						# Edge  1
						# Enter source:1
						# Enter destination:2
						# Enter weight:2

						# specify source, destination and weight for edge #2
						# Edge  2
						# Enter source:2
						# Enter destination:1
						# Enter weight:1

						# # Expected Output from the vertice, edge and src, dst, weight inputs!!
						# 0		INF	INF
						# INF	0	2
						# INF	1	0

