output = [n ** 2.5forninnumbers] returnoutput 结果如下: # Summary Of Test Results Baseline: 32.158 ns per loop Improved: 16.040 ns per loop % Improvement: 50.1 % Speedup: 2.00x 可以看到使用列表推导式可以得到2倍速的提高 2、在外部
output = [n ** 2.5forninnumbers] returnoutput 结果如下: # Summary Of Test Results Baseline: 32.158 ns per loop Improved: 16.040 ns per loop % Improvement: 50.1 % Speedup: 2.00x 可以看到使用列表推导式可以得到2倍速的提高 2、在外部计算长度 如果需要依靠列表的长度进行迭代,请在for循环之外进行...
Baseline: 112.135 ns per loop Improved: 68.304 ns per loop % Improvement: 39.1 % Speedup: 1.64x 3、使用Set 在使用for循环进行比较的情况下使用set。 # Use for loops for nested lookups def test_03_v0(list_1, list_2): # Baseline version (Inefficient way) # (nested lookups using for loop...
循环（Loop）是在满足特定条件的情况下重复执行一组语句或操作的过程。Python中有两种主要的循环结构：for...
output = [n ** 2.5 for n in numbers] return output 结果如下: # Summary Of Test Results Baseline: 32.158 ns per loop Improved: 16.040 ns per loop % Improvement: 50.1 % Speedup: 2.00x 可以看到使用列表推导式可以得到2倍速的提高 2、在外部计算长度 ...
output=[n**2.5forninnumbers] returnoutput 结果如下: # Summary Of Test Results Baseline: 32.158 ns per loop Improved: 16.040 ns per loop % Improvement: 50.1 % Speedup: 2.00x 可以看到使用列表推导式可以得到2倍速的提高 2、在外部计算长度 ...
For example, # iterate from i = 0 to 3 for _ in range(0, 4: print('Hi') Run Code Output 0 1 2 3 Here, the loop runs four times. In each iteration, we have displayed Hi. Since we are not using the items of the sequence(0, 1, 2 and 4) in the loop body, it is ...
In[240]:from randomimportnormalvariate In[241]:N=1000000In[242]:%timeit samples=[normalvariate(0,1)for_inrange(N)]1.77s+-126ms perloop(mean+-std.dev.of7runs,1loop each)In[243]:%timeit np.random.normal(size=N)61.7ms+-1.32ms perloop(mean+-std.dev.of7runs,10loops each) ...
duration_cast<std::chrono::nanoseconds>(end - begin);avg_time += elapsed.count() *1e-9;printf("Pi is approximately %g and took %.5f seconds to calculate.\n", pi, elapsed.count() *1e-9);}printf("\nEach loop took on average %.5f seconds ...
pySLAM is a visual SLAM pipeline in Python for monocular, stereo and RGBD cameras. It supports many modern local and global features, different loop-closing methods, a volumetric reconstruction pipeline, and depth prediction models. - luigifreda/pyslam