


from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       ):
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[str]					=		coefficient_matrix.shape
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[Any]					=		constant_matrix.shape

						if rowsa != colsa:
												__SCREAMING_SNAKE_CASE						:     int					=		F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
												raise ValueError(lowercase__      )

						if colsa != 1:
												__SCREAMING_SNAKE_CASE						:     str					=		F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
												raise ValueError(lowercase__      )

						if rowsa != rowsa:
												__SCREAMING_SNAKE_CASE						:     List[str]					=		(
												    '''Coefficient and constant matrices dimensions must be nxn and nx1 but '''
												    F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
												)
												raise ValueError(lowercase__      )

						if len(lowercase__      ) != rowsa:
												__SCREAMING_SNAKE_CASE						:     Tuple					=		(
												    '''Number of initial values must be equal to number of rows in coefficient '''
												    F'''matrix but received {len(lowercase__      )} and {rowsa}'''
												)
												raise ValueError(lowercase__      )

						if iterations <= 0:
												raise ValueError('''Iterations must be at least 1'''      )

						__SCREAMING_SNAKE_CASE						:     NDArray[floataa]					=		np.concatenate(
						    (coefficient_matrix, constant_matrix)    ,       axis=1      )

						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[Any]					=		table.shape

						strictly_diagonally_dominant(lowercase__      )

						# Iterates the whole matrix for given number of times
						for _ in range(lowercase__      ):
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		[]
												for row in range(lowercase__      ):
																		__SCREAMING_SNAKE_CASE						:     int					=		0
																		for col in range(lowercase__      ):
																								if col == row:
																														__SCREAMING_SNAKE_CASE						:     List[Any]					=		table[row][col]
																								elif col == cols - 1:
																														__SCREAMING_SNAKE_CASE						:     int					=		table[row][col]
																								else:
																														temp += (-1) * table[row][col] * init_val[col]
																		__SCREAMING_SNAKE_CASE						:     str					=		(temp + val) / denom
																		new_val.append(lowercase__      )
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		new_val

						return [float(lowercase__      ) for i in new_val]


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[int]					=		table.shape

						__SCREAMING_SNAKE_CASE						:     Dict					=		True

						for i in range(0    ,       lowercase__      ):
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		0
												for j in range(0    ,       cols - 1      ):
																		if i == j:
																								continue
																		else:
																								total += table[i][j]

												if table[i][i] <= total:
																		raise ValueError('''Coefficient matrix is not strictly diagonally dominant'''      )

						return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
						import doctest

						doctest.testmod()




from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

__lowerCAmelCase	: List[Any]  				=datasets.load_iris()

__lowerCAmelCase	: Tuple  				=np.array(data['data'])
__lowerCAmelCase	: Dict  				=np.array(data['target'])
__lowerCAmelCase	: List[str]  				=data['target_names']

__lowerCAmelCase				,__lowerCAmelCase				,__lowerCAmelCase				,__lowerCAmelCase	: str  				=train_test_split(X, y)


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						return np.linalg.norm(np.array(lowercase__      ) - np.array(lowercase__      )      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__=5      ):
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		zip(lowercase__    ,       lowercase__      )
						# List of distances of all points from the point to be classified
						__SCREAMING_SNAKE_CASE						:     Dict					=		[]
						for data_point in data:
												__SCREAMING_SNAKE_CASE						:     Tuple					=		euclidean_distance(data_point[0]    ,       lowercase__      )
												distances.append((distance, data_point[1])      )
						# Choosing 'k' points with the least distances.
						__SCREAMING_SNAKE_CASE						:     int					=		[i[1] for i in sorted(lowercase__      )[:k]]
						# Most commonly occurring class among them
						# is the class into which the point is classified
						__SCREAMING_SNAKE_CASE						:     Any					=		Counter(lowercase__      ).most_common(1      )[0][0]
						return classes[result]


if __name__ == "__main__":
						print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
