66 lines
1.9 KiB
Python
66 lines
1.9 KiB
Python
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import threading
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#fix
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import time
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import random
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import numpy as np
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from concurrent.futures import ThreadPoolExecutor
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def gaussian_determinant(matrix):
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n = len(matrix)
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mat = [row[:] for row in matrix]
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for i in range(n):
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max_row = max(range(i, n), key=lambda r: abs(mat[r][i]))
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mat[i], mat[max_row] = mat[max_row], mat[i]
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if mat[i][i] == 0:
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return 0
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for j in range(i + 1, n):
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factor = mat[j][i] / mat[i][i]
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for k in range(i, n):
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mat[j][k] -= mat[i][k] * factor
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det = 1
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for i in range(n):
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det *= mat[i][i]
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return det
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def parallel_determinant(matrix, num_threads=4):
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n = len(matrix)
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result = []
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def worker(start_row, end_row):
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partial_det = 1
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for i in range(start_row, end_row):
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partial_det *= matrix[i][i]
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result.append(partial_det)
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with ThreadPoolExecutor(max_workers=num_threads) as executor:
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rows_per_thread = n // num_threads
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futures = [executor.submit(worker, i * rows_per_thread, (i + 1) * rows_per_thread) for i in range(num_threads)]
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for future in futures:
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future.result()
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return sum(result)
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def generate_matrix(size):
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return [[random.randint(1, 10) for _ in range(size)] for _ in range(size)]
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matrix_sizes = [100, 300, 500]
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num_threads = 4
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for size in matrix_sizes:
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print(f"\nБенчмарки для матрицы {size}x{size}:")
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matrix = generate_matrix(size)
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start = time.time()
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det_seq = gaussian_determinant(matrix)
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end = time.time()
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print(f"Детерминант (последовательно, метод Гаусса): {det_seq}, время: {end - start:.5f} сек")
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start = time.time()
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det_par = parallel_determinant(matrix, num_threads=num_threads)
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end = time.time()
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print(f"Детерминант (параллельно): {det_par}, время: {end - start:.5f} сек")
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