71 lines
2.4 KiB
Python
71 lines
2.4 KiB
Python
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import random
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from multiprocessing import Pool
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import time
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def submatrix(matrix, row, col):
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return [[matrix[i][j] for j in range(len(matrix[i])) if j != col] for i in range(len(matrix)) if i != row]
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def determinant(matrix):
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size = len(matrix)
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# Base case: determinant of a 1x1 matrix is the only element in it
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if size == 1:
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return matrix[0][0]
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# Base case: determinant of a 2x2 matrix
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if size == 2:
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return matrix[0][0] * matrix[1][1] - matrix[0][1] * matrix[1][0]
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det = 0
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for col in range(size):
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det += ((-1) ** col) * matrix[0][col] * determinant(submatrix(matrix, 0, col))
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return det
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def generate_random_matrix(size, lower_limit, upper_limit):
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return [[random.uniform(lower_limit, upper_limit) for _ in range(size)] for _ in range(size)]
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def sequential_determinant_calculation(matrix_size, lower_limit, upper_limit):
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random_matrix = generate_random_matrix(matrix_size, lower_limit, upper_limit)
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start_time = time.time()
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result = determinant(random_matrix)
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end_time = time.time()
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print(f"Sequential determinant: {result}")
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print(f"Sequential time: {end_time - start_time} seconds")
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def parallel_determinant_calculation(matrix_size, lower_limit, upper_limit, num_processes):
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random_matrix = generate_random_matrix(matrix_size, lower_limit, upper_limit)
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matrices_to_process = [submatrix(random_matrix, 0, col) for col in range(matrix_size)]
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start_time = time.time()
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with Pool(processes=num_processes) as pool:
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determinants = pool.map(determinant, matrices_to_process)
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result = sum(((-1) ** col) * random_matrix[0][col] * det for col, det in enumerate(determinants))
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end_time = time.time()
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print(f"Parallel determinant: {result}")
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print(f"Parallel time: {end_time - start_time} seconds")
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if __name__ == "__main__":
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matrix_size = 10 # You can change this to the desired size of the matrix
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lower_limit = 10 # You can change this to the lower limit of the random numbers
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upper_limit = 1000 # You can change this to the upper limit of the random numbers
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num_processes = 8 # You can change this to the desired number of parallel processes
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# Sequential calculation
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sequential_determinant_calculation(matrix_size, lower_limit, upper_limit)
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# Parallel calculation
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parallel_determinant_calculation(matrix_size, lower_limit, upper_limit, num_processes)
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