forked from Alexey/DAS_2024_1
118 lines
3.1 KiB
Python
118 lines
3.1 KiB
Python
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import random
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import time
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import copy
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from multiprocessing import Pool
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import concurrent.futures
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from copy import deepcopy
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class Matrix:
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def __init__(self) -> None:
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self.matrix_100 = [[0] * 100 for _ in range(100)]
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self.matrix_300 = [[0] * 300 for _ in range(300)]
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self.matrix_500 = [[0] * 500 for _ in range(500)]
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def str_matrix(self, type_list: str):
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_str = ""
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current_matrix = getattr(self, type_list)
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for i in range(len(current_matrix)):
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_str += "[ "
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for j in range(len(current_matrix[0])):
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_str += str(current_matrix[i][j]) + " "
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_str += " ]\n"
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return _str
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def init_matrix(matrix: Matrix, size: int):
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for i in range(size):
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for j in range(size):
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matrix.__dict__[f"matrix_{size}"][i][j] = random.randint(0, 5)
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def parallel_det(matrix, num_threads=1):
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n = len(matrix)
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m = deepcopy(matrix)
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det_value = 1
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for i in range(n):
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if m[i][i] == 0:
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for j in range(i + 1, n):
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if m[j][i] != 0:
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m[i], m[j] = m[j], m[i]
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det_value *= -1
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break
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else:
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return 0
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with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:
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futures = [executor.submit(process_row, i, j, m, n) for j in range(i + 1, n)]
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concurrent.futures.wait(futures)
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det_value *= m[i][i]
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m = [list(row) for row in m] # Обновляем строки матрицы
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return det_value
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def process_row(i, j, m, n):
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factor = m[j][i] / m[i][i]
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for k in range(i, n):
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m[j][k] -= factor * m[i][k]
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return m[j]
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def det(matrix):
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n = len(matrix)
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m = [row[:] for row in matrix]
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det_value = 1
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for i in range(n):
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if m[i][i] == 0:
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for j in range(i + 1, n):
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if m[j][i] != 0:
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m[i], m[j] = m[j], m[i]
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det_value *= -1
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break
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else:
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return 0
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for j in range(i + 1, n):
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factor = m[j][i] / m[i][i]
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for k in range(i, n):
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m[j][k] -= factor * m[i][k]
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det_value *= m[i][i]
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return det_value
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def benchmark():
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matrix = Matrix()
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init_matrix(matrix, 100)
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init_matrix(matrix, 300)
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init_matrix(matrix, 500)
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sizes = [100, 300, 500]
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for size in sizes:
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current_matrix = getattr(matrix, f'matrix_{size}')
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start_time = time.time()
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seq_result = det(current_matrix)
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seq_time = time.time() - start_time
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print(f"Последовательный детерминант {size}x{size}: {seq_result}, Время: {seq_time:.6f}с")
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start_time = time.time()
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par_result = parallel_det(current_matrix, num_threads=4) # Измените число потоков по необходимости
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par_time = time.time() - start_time
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print(f"Параллельный детерминант {size}x{size}: {par_result}, Время: {par_time:.6f}с")
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if __name__ == "__main__":
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benchmark()
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