哪裏可以找到一些Numpy方法的效率O（n）？

Question

我正在做一個學校項目，他們問了一些Numpy方法的效率O（n），我找不到它們。誰能告訴我哪裏可以找到那些？

實例的方法，如：

numpy.linspace(x,y,z) numpy.meshgrid(x,y) numpy.zeroes(x,y)

Answer 1

你可以簡單地測量不同問題的執行時間大小得到的時間複雜度的估計，

• numpy.zeros(n)：non-deterministic
• numpy.meshgrid(x,y)：O(n**2)
• numpy.linspace(0, 1, n)：O(n**1.6)

例如，下面是測量numpy.meshgrid(x,y)的時間複雜度的編碼，其可以被用於其它numpy的功能以及，

In [1]: import numpy as np 
    ...: from time import time 
    ...: import matplotlib.pyplot as plt 
    ...: from scipy.optimize import curve_fit 
    ...: %matplotlib inline 
    ...: 
    ...: def complexity_model(x, n, A, C): 
    ...:  return A*x**n + C 
    ...: 
    ...: problem_size = np.logspace(2, 4, 10) 
    ...: 
    ...: res = [] 
    ...: for N in problem_size: 
    ...:  x = np.linspace(0, 1, N) 
    ...:  y = x.copy() 
    ...:  
    ...:  t0 = time() 
    ...:  np.meshgrid(x,y) 
    ...:  dt = time() - t0 
    ...:  res.append(dt) 
    ...: 
    ...: nn = np.logspace(np.log10(problem_size.min()), np.log10(problem_size.max()), 100) 
    ...: 
    ...: time_to_solution = np.asarray(res) 
    ...: fig, ax = plt.subplots(1,1) 
    ...: ax.loglog(problem_size, time_to_solution, 'o-b') 
    ...: 
    ...: mask = problem_size > 100 # ignore initial points 
    ...: 
    ...: popt, _ = curve_fit(complexity_model, problem_size[mask], 
    ...:        time_to_solution[mask], 
    ...:        p0=(1.0, 1.0, 0.0)) 
    ...: print(popt) 
    ...: ax.loglog(nn, complexity_model(nn, *popt), '--k') 
    ...: 
    ...: 
    ...: ax.set_xlabel('Problem size: N') 
    ...: ax.set_ylabel('Time to solution 
    [ 1.94816942e+00 1.40955397e-08 -7.33862899e-04] 

其給出如下曲線，

對於足夠大的陣列尺寸，因此numpy.meshgrid(x,y)的時間複雜度爲O(n**α)，其中α = 1.95 ≈ 2。