forked from Alexey/DAS_2024_1
77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
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import numpy as np
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from concurrent.futures import ProcessPoolExecutor
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import time
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# Функция последовательного поиска детерминанта
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def det_sequential(matrix):
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n = len(matrix)
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det = 1
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for i in range(n):
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if matrix[i][i] == 0:
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for j in range(i + 1, n):
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if matrix[j][i] != 0:
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matrix[i], matrix[j] = matrix[j], matrix[i]
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det *= -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 = matrix[j][i] / matrix[i][i]
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for k in range(i, n):
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matrix[j][k] -= factor * matrix[i][k]
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det *= matrix[i][i]
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return det
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# Функция поиска детерминанта с numpy
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def det_numpy(A):
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return np.linalg.det(A)
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# Функция параллельного поиска детерминанта
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def det_parallel(A, num_threads):
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n = len(A)
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C = []
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step = n // num_threads
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with ProcessPoolExecutor(max_workers=num_threads) as executor:
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futures = []
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for i in range(num_threads):
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start_row = i * step
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end_row = (i + 1) * step if i != num_threads - 1 else n
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a_slice = A[start_row:end_row, start_row:end_row]
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futures.append(executor.submit(det_sequential, a_slice))
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for future in futures:
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C.append(future.result())
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return np.prod(C)
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# Пример использования
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if __name__ == "__main__":
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matrix_sizes = [100, 300, 500]
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num_threads = [2, 4, 5, 10]
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for n in matrix_sizes:
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A = np.random.rand(n, n)
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# Поиск с numpy
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start_np = time.time()
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nump = det_numpy(A)
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end_np = time.time()
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print(f'Детерминант матрицы {n}x{n} с numpy: {(end_np - start_np):.3f} с.')
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# Последовательное умножение
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start_seq = time.time()
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sequential = det_sequential(A)
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end_seq = time.time()
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print(f'Детерминант матрицы {n}x{n} последовательно: {(end_seq - start_seq):.3f} с.')
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# Параллельное умножение
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for thread in num_threads:
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start_par = time.time()
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parallel = det_parallel(A, thread)
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end_par = time.time()
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print(f'Детерминант матрицы {n}x{n} параллельно для {thread} потоков: {(end_par - start_par):.3f} с.')
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print('')
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