![]() def random_walk_fastest ( n = 1000 ): # No 's' in numpy choice (Python offers choice & choices) steps = np. We just have to translate itertools call into numpy ones. But the advantage of this new version is that it makes numpy vectorization We gained 85% of computation-time compared to the previous version, not soīad. Things faster: > from tools import timeit > timeit ( "random_walk_faster(n=10000)", globals ()) 10 loops, best of 3: 2.21 msec per loop Sequential steps and add them to the current position, we first generated all theįunction to compute all the positions. ![]() In fact, we've just vectorized our function. ![]() choices (, k = n ) return + list ( accumulate ( steps )) walk = random_walk_faster ( 1000 ) Loop: def random_walk_faster ( n = 1000 ): from itertools import accumulate # Only available from Python 3.6 steps = random. Observe that a random walk is an accumulation of steps, we can rewrite theįunction by first generating all the steps and accumulate them without any Offers a set of functions creating iterators for efficient looping. > from tools import timeit > timeit ( "random_walk(n=10000)", globals ()) 10 loops, best of 3: 15.6 msec per loopīut we can do better using the itertools Python module that We saved probably come from the inner Python object-oriented machinery. Is pretty much the same as in the object-oriented approach and the few cycles This new method saves some CPU cycles but not that much because this function append ( position ) return walk walk = random_walk ( 1000 ) def random_walk ( n ): position = 0 walk = for i in range ( n ): position += 2 * random. randint ( 0, 1 ) - 1 walker = RandomWalker () walk = īenchmarking gives us: > from tools import timeit > walker = RandomWalker () > timeit ( "", globals ()) 10 loops, best of 3: 15.7 msec per loopįor such a simple problem, we can probably save the class definition andĬoncentrate only on the walk method that computes successive positions afterĮach random step. position = 0 for i in range ( n ): yield self. position = 0 def walk ( self, n ): self. Object oriented approach class RandomWalker : def _init_ ( self ): self. Method that would return the current position after each (random) step. One possible object orientedĪpproach would be to define a RandomWalker class and write a walk Let's take a very simple example, random walk. Main difficulty you'll face because you'll need to change your way of thinkingĪnd your new friends (among others) are named "vectors", "arrays", "views" or If you are familiar with Python, this is the Such a case, you might want to use the magic command %timeit instead of the Regular python shell or from inside an IPython session or Jupyter notebook. You can execute any code below from the code folder using the Of the popular article Ten Simple Rules for Better Figures and a popular Numpy and scientific visualization at the University of Bordeaux and in variousĬonferences and schools worldwide (SciPy, EuroScipy, etc). Resources and tutorials (Matplotlib, numpy, OpenGL) and he's teaching Python, Years for modeling in neuroscience, machine learning and for advanced He has been using Python for more than 15 years and numpy for more than 10 ( LaBRI), the University of Bordeaux and the national center for scientific Neuroscience in association with the Institute of Neurodegenerativeĭiseases, the Bordeaux laboratory for research in computer science Project which lies at the frontier between integrative and computational Supervision of the Research & Education Ministry, and the Ministry of Economyįinance and Industry. This isĪ public scientific and technological establishment (EPST) under the double Rougier is a full-time research scientist at Inria which is theįrench national institute for research in computer science and control.
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