106 lines
2.9 KiB
Python
106 lines
2.9 KiB
Python
"""
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This code is supported by the website: https://www.guanjihuan.com
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The newest version of this code is on the web page: https://www.guanjihuan.com/archives/45275
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"""
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import numpy as np
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import time
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import sys
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from numba import jit
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n_array = np.concatenate((np.arange(1000, 10000, 1000), np.arange(10000, 40000, 10000)))
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print(f'n_array={n_array}\n')
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@jit
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def numba_test(C, n):
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for i0 in range(n):
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for j0 in range(n):
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C[i0, j0] = np.random.rand()
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return C
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for n in n_array:
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print(f'n={n}')
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A = np.random.rand(n, n)
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B = np.random.rand(n, n)
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C = np.random.rand(n, n)
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# 矩阵占用内存
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size = sys.getsizeof(C)
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print(f'矩阵占用内存: {size/(1024*1024):.2f} MB')
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# 矩阵的迹
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start_time = time.time()
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trace_A = np.trace(A)
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trace_time = time.time() - start_time
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print(f"矩阵的迹时间: {trace_time:.3f} 秒")
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# 矩阵转置
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start_time = time.time()
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A_T = A.T
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transpose_time = time.time() - start_time
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print(f"矩阵转置时间: {transpose_time:.3f} 秒")
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# 矩阵加法
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start_time = time.time()
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C = A + B
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add_time = time.time() - start_time
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print(f"矩阵加法时间: {add_time:.3f} 秒")
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# numba for 循环赋值
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start_time = time.time()
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numba_test(C, n)
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create_time = time.time() - start_time
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print(f"numba for 循环赋值时间: {create_time:.3f} 秒")
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# 矩阵创建
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start_time = time.time()
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C = np.random.rand(n, n)
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create_time = time.time() - start_time
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print(f"矩阵创建时间: {create_time:.3f} 秒")
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# for 循环赋值
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start_time = time.time()
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for i0 in range(n):
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for j0 in range(n):
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C[i0, j0] = np.random.rand()
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create_time = time.time() - start_time
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print(f"for 循环赋值时间: {create_time:.3f} 秒")
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# 矩阵行列式
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start_time = time.time()
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det_A = np.linalg.det(A)
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det_time = time.time() - start_time
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print(f"矩阵行列式时间: {det_time:.3f} 秒")
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# 矩阵乘法
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start_time = time.time()
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C = np.dot(A, B)
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multiply_time = time.time() - start_time
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print(f"矩阵乘法时间: {multiply_time:.3f} 秒")
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# 矩阵求逆
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start_time = time.time()
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inv_A = np.linalg.inv(A)
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inv_time = time.time() - start_time
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print(f"矩阵求逆时间: {inv_time:.3f} 秒")
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# 矩阵的秩
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start_time = time.time()
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rank_A = np.linalg.matrix_rank(A)
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rank_time = time.time() - start_time
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print(f"矩阵的秩时间: {rank_time:.3f} 秒")
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# 矩阵的特征值
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start_time = time.time()
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eigenvalues_A = np.linalg.eigvals(A)
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eigen_time = time.time() - start_time
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print(f"矩阵特征值时间: {eigen_time:.3f} 秒")
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# 矩阵的特征值和特征向量
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start_time = time.time()
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eigenvalues_A, eigenvector_A = np.linalg.eig(A)
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eigen_time = time.time() - start_time
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print(f"矩阵特征值和特征向量时间: {eigen_time:.3f} 秒")
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print() |