




def 			__magic_name__				( A		:					list[list[float]]   ):






			'''simple docstring'''







			a    =			[]
			for data in source_data:
						for i, el in enumerate(A   ):
									if len(A   ) < i + 1:
												data_lists.append([]   )
									data_lists[i].append(float(A   )   )
			return data_lists




def 			__magic_name__				( A		:					list[list[float]],  A		:					list[int]   ):






			'''simple docstring'''







			a    =			[]
			for dlist, weight in zip(A,  A   ):
						a    =			min(A   )
						a    =			max(A   )

						a    =			[]
						# for weight 0 score is 1 - actual score
						if weight == 0:
									for item in dlist:
												try:
															score.append(1 - ((item - mind) / (maxd - mind))   )
												except ZeroDivisionError:
															score.append(1   )

						elif weight == 1:
									for item in dlist:
												try:
															score.append((item - mind) / (maxd - mind)   )
												except ZeroDivisionError:
															score.append(0   )

        # weight not 0 or 1
						else:
									a    =			F"""Invalid weight of {weight:f} provided"""
									raise ValueError(A   )

						score_lists.append(A   )

			return score_lists




def 			__magic_name__				( A		:					list[list[float]]   ):






			'''simple docstring'''







			a    =			[0 for i in range(len(score_lists[0]   )   )]

			for slist in score_lists:
						for j, ele in enumerate(A   ):
									a    =			final_scores[j] + ele

			return final_scores






def 			__magic_name__				( A		:					list[list[float]],  A		:					list[int]   ):






			'''simple docstring'''







			a    =			get_data(A   )
			a    =			calculate_each_score(A,  A   )
			a    =			generate_final_scores(A   )

			# append scores to source data
			for i, ele in enumerate(A   ):
						source_data[i].append(A   )

			return source_data












from __future__ import annotations

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




def 			__magic_name__				( A		:					NDArray[floataa],  A		:					NDArray[floataa],  A		:					list[int],  A		:					int,  ):






			'''simple docstring'''







			a    ,		a    =			coefficient_matrix.shape
			a    ,		a    =			constant_matrix.shape

			if rowsa != colsa:
						a    =			F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
						raise ValueError(A   )

			if colsa != 1:
						a    =			F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
						raise ValueError(A   )

			if rowsa != rowsa:
						a    =			(
						    "Coefficient and constant matrices dimensions must be nxn and nx1 but "
						    F"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
						)
						raise ValueError(A   )

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

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

			a    =			np.concatenate(
			    (coefficient_matrix, constant_matrix),  axis=1   )

			a    ,		a    =			table.shape

			strictly_diagonally_dominant(A   )

			# Iterates the whole matrix for given number of times
			for _ in range(A   ):
						a    =			[]
						for row in range(A   ):
									a    =			0
									for col in range(A   ):
												if col == row:
															a    =			table[row][col]
												elif col == cols - 1:
															a    =			table[row][col]
												else:
															temp += (-1) * table[row][col] * init_val[col]
									a    =			(temp + val) / denom
									new_val.append(A   )
						a    =			new_val

			return [float(A   ) for i in new_val]




def 			__magic_name__				( A		:					NDArray[floataa]   ):






			'''simple docstring'''







			a    ,		a    =			table.shape

			a    =			True

			for i in range(0,  A   ):
						a    =			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()






