


'''simple docstring'''


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







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				return np.dot(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )





class     lowercase__  :





							'''simple docstring'''


							def __init__(    self    ,					*,
							    __snake_case = np.inf    ,					__snake_case = "linear"    ,					__snake_case = 0.0    ,					):
											_SCREAMING_SNAKE_CASE :       str     		=      regularization
											_SCREAMING_SNAKE_CASE :       Dict     		=      gamma
											if kernel == "linear":
															_SCREAMING_SNAKE_CASE :       Optional[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"""  )
															_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[str]     		=      f"""Unknown kernel: {kernel}"""
															raise ValueError(__snake_case  )






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case  ):
											return np.dot(__snake_case    ,					__snake_case  )






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case  ):
											return np.exp(-(self.gamma * norm_squared(vectora - vectora  ))  )






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      observations
											_SCREAMING_SNAKE_CASE :       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

											((_SCREAMING_SNAKE_CASE)		,       ) :       List[Any]     		=      np.shape(__snake_case  )

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

											_SCREAMING_SNAKE_CASE :       Any     		=      LinearConstraint(__snake_case    ,					0    ,					0  )
											_SCREAMING_SNAKE_CASE :       str     		=      Bounds(0    ,					self.regularization  )

											_SCREAMING_SNAKE_CASE :       Any     		=      minimize(
											    __snake_case    ,					np.ones(__snake_case  )    ,					bounds=__snake_case    ,					constraints=[ly_contraint]  ).x
											_SCREAMING_SNAKE_CASE :       Tuple     		=      l_star

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






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       List[str]     		=      sum(
											    self.optimum[n]
											    * self.classes[n]
											    * self.kernel(self.observations[n]    ,					__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'''


from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Tuple     		=      []
				_SCREAMING_SNAKE_CASE :       Dict     		=      []
				_SCREAMING_SNAKE_CASE :       str     		=      []

				for rt in rc.restypes:
								_SCREAMING_SNAKE_CASE :       Optional[int]     		=      rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
								restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]     )
								_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      {name: i for i, name in enumerate(SCREAMING_SNAKE_CASE__     )}
								restype_atomaa_to_atomaa_list.append(
								    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]     )

								restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]     )

				# Add dummy mapping for restype 'UNK'
				restype_atomaa_to_atomaa_list.append([0] * 14     )
				restype_atomaa_to_atomaa_list.append([0] * 37     )
				restype_atomaa_mask_list.append([0.0] * 14     )

				_SCREAMING_SNAKE_CASE :       Dict     		=      torch.tensor(
				    SCREAMING_SNAKE_CASE__							,      dtype=torch.intaa							,      device=protein["""aatype"""].device							,      )
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      torch.tensor(
				    SCREAMING_SNAKE_CASE__							,      dtype=torch.intaa							,      device=protein["""aatype"""].device							,      )
				_SCREAMING_SNAKE_CASE :       str     		=      torch.tensor(
				    SCREAMING_SNAKE_CASE__							,      dtype=torch.floataa							,      device=protein["""aatype"""].device							,      )
				_SCREAMING_SNAKE_CASE :       int     		=      protein["""aatype"""].to(torch.long     )

				# create the mapping for (residx, atom14) --> atom37, i.e. an array
				# with shape (num_res, 14) containing the atom37 indices for this protein
				_SCREAMING_SNAKE_CASE :       List[str]     		=      restype_atomaa_to_atomaa[protein_aatype]
				_SCREAMING_SNAKE_CASE :       str     		=      restype_atomaa_mask[protein_aatype]

				_SCREAMING_SNAKE_CASE :       List[Any]     		=      residx_atomaa_mask
				_SCREAMING_SNAKE_CASE :       List[str]     		=      residx_atomaa_to_atomaa.long()

				# create the gather indices for mapping back
				_SCREAMING_SNAKE_CASE :       int     		=      restype_atomaa_to_atomaa[protein_aatype]
				_SCREAMING_SNAKE_CASE :       Dict     		=      residx_atomaa_to_atomaa.long()

				# create the corresponding mask
				_SCREAMING_SNAKE_CASE :       str     		=      torch.zeros([21, 37]							,      dtype=torch.floataa							,      device=protein["""aatype"""].device     )
				for restype, restype_letter in enumerate(rc.restypes     ):
								_SCREAMING_SNAKE_CASE :       int     		=      rc.restype_atoa[restype_letter]
								_SCREAMING_SNAKE_CASE :       Dict     		=      rc.residue_atoms[restype_name]
								for atom_name in atom_names:
												_SCREAMING_SNAKE_CASE :       List[Any]     		=      rc.atom_order[atom_name]
												_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      1

				_SCREAMING_SNAKE_CASE :       int     		=      restype_atomaa_mask[protein_aatype]
				_SCREAMING_SNAKE_CASE :       int     		=      residx_atomaa_mask

				return protein







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      tree_map(lambda SCREAMING_SNAKE_CASE__     : torch.tensor(SCREAMING_SNAKE_CASE__							,      device=batch["""aatype"""].device     )							,      SCREAMING_SNAKE_CASE__							,      np.ndarray     )
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      tensor_tree_map(lambda SCREAMING_SNAKE_CASE__     : np.array(SCREAMING_SNAKE_CASE__     )							,      make_atomaa_masks(SCREAMING_SNAKE_CASE__     )     )
				return out




