Meant to draw this with turtle using recursion: But I'm really bad at this. This is what I'm getting: Here's the code: https://gyazo.com/24cebddbb111506fd6959bb91dadb481 import turtle def draw_shape(t, level,size): if level == 1: #draws an I t.down() t.forward(size/2) t.left(90) t.forward(size/2) t.back(size) t.forward(size/2)
想用乌龟用递归绘制这个： 但我在这方面真的很糟糕。 这是我得到的： 以下是代码：https://gyazo.com/24cebddbb111506fd6959bb91dadb481 import turtle def draw_shape(t, level,size): if level == 1: #draws an I t.down() t.forward(size/2) t.left(90) t.forward(size/2) t.back(size) t.forward(size/2) t.left(90) t.forward(size) t.l
I have to define a function that takes two numbers: n and k (n >= k) and returns the binomial coefficent of these two numbers. #defining a function that computes the factorial of an integer def fac(b): if b==1: return 1 else: return b * fac(b-1) #takes two integers and does the binomial coefficient operand def combinations(n,k): result = (fac(n)) / (fac(k) * fac
我必须定义一个函数，它需要两个数字：n和k（n> = k）并返回这两个数字的二项式系数。 #defining a function that computes the factorial of an integer def fac(b): if b==1: return 1 else: return b * fac(b-1) #takes two integers and does the binomial coefficient operand def combinations(n,k): result = (fac(n)) / (fac(k) * fac(n-k)) return result n=10 k=2 print(comb
People including me know there is something in Python called __future__ and it appears in quite a few modules I read. And the dull people like me don't know why it's there, and how/when to use it , even after reading the Python's __future__ doc. So any explains with examples to demonstrate it? I have got a few answers quickly, which look all correct, in terms of the basic usage.
包括我在内的人知道Python中有一些名为__future__东西，它出现在我阅读的很多模块中。 像我这样沉闷的人不知道为什么它在那里，以及如何/何时使用它，即使在阅读了Python的__future__ doc之后。 那么有没有用示例解释来证明它呢？ 我很快就得到了一些答案，就基本用法而言，这看起来都是正确的。 但是，也为了进一步了解__future__如何工作： 当我试图理解它的时候，我刚刚意识到一件令人困惑的事情，那就是，当前的Pyt
I have this tail recursive function here: def fib(n, sum): if n < 1: return sum else: return fib(n-1, sum+n) c = 998 print(fib(c, 0)) It works up to n=997, then it just breaks and spits a "maximum recursion depth exceeded in comparison" RuntimeError . Is this just a stack overflow? Is there a way to get around it? It is a guard against a stack overflow,
我在这里有这个尾递归函数： def fib(n, sum): if n < 1: return sum else: return fib(n-1, sum+n) c = 998 print(fib(c, 0)) 它工作到n = 997，然后它只是打破并吐出一个“比较超出最大递归深度” RuntimeError 。 这只是一个堆栈溢出？ 有没有办法避开它？ 它是防止堆栈溢出的警卫，是的。 Python（或更确切地说，CPython实现）不优化尾递归，而无约束递归会导致堆栈溢出。 您可以使用sys.
I have the following piece of code which fails with the following error: RuntimeError: maximum recursion depth exceeded I attempted to rewrite this to allow for tail recursion optimization (TCO). I believe that this code should have been successful if a TCO had taken place. def trisum(n, csum): if n == 0: return csum else: return trisum(n - 1, csum + n) print(trisum
我有以下代码失败，出现以下错误： RuntimeError：超过最大递归深度 我试图重写这个以允许尾递归优化（TCO）。 我相信，如果TCO发生，这个代码应该是成功的。 def trisum(n, csum): if n == 0: return csum else: return trisum(n - 1, csum + n) print(trisum(1000, 0)) 我应该得出结论：Python不会做任何类型的TCO，或者我只需要以不同的方式定义它？ 不，并且它从不会因为Guido喜欢能够有
I just started Python and I've got no idea what memoization is and how to use it. Also, may I have a simplified example? Memoization effectively refers to remembering ("memoization" → "memorandum" → to be remembered) results of method calls based on the method inputs and then returning the remembered result rather than computing the result again. You can think of it as
我刚开始使用Python，我不知道memoization是什么以及如何使用它。 另外，我可以有一个简化的例子吗？ 记忆有效地指的是基于方法输入记忆方法调用的结果（“记忆”→“备忘录”→被记住），然后返回记忆的结果而不是再次计算结果。 您可以将其视为方法结果的缓存。 有关更多详细信息，请参阅第387页， 以了解算法导论 （3e）中的定义，Cormen等人。 在Python中使用memoization来计算阶乘的简单例子就像这样： factorial_memo = {
This question already has an answer here: What is tail recursion? 23 answers Well, let's look at a super simple function. def super_simple_function(): return 0 What happens when I do x = super_simple_function() ? >>> x = super_simple_function() >>> x 0 That's because the function's return value is zero. So there's a function object, which gives (retur
这个问题在这里已经有了答案： 什么是尾递归？ 23个答案 那么，让我们看看一个超级简单的功能。 def super_simple_function(): return 0 当我做x = super_simple_function()时会发生什么？ >>> x = super_simple_function() >>> x 0 这是因为函数的返回值为零。 所以有一个函数对象，它在被调用时给出（返回）一个值。 让我们一行一行看看递归函数。 想象一下，我们通过2和3作为参数，如下所示
In C#, Value Types (eg: int, float, etc) are stored on the stack. Method parameters may also be stored on the stack as well. Most everything else, however, is stored on the heap. This includes Lists, objects, etc. I was wondering, does CPython do the same thing internally? What does it store on the stack, and what does it put on the heap? All Python objects in the CPython implementation g
在C＃中，值类型（例如：int，float等）存储在堆栈中。 方法参数也可以存储在堆栈中。 然而，大多数其他东西都存储在堆中。 这包括列表，对象等 我想知道，CPython在内部做同样的事情吗？ 它在堆栈中存储了什么内容，它堆放在什么地方？ CPython实现中的所有Python对象都堆在一起。 您可以详细Python的内存管理是如何工作这里的文档阅读： Python中的内存管理涉及一个包含所有Python对象和数据结构的私有堆。 Python
What's a good way to estimate the memory footprint of an object? Conversely, what's a good way to measure the footprint? For example, say I have a dictionary whose values are lists of integer,float tuples: d['key'] = [ (1131, 3.11e18), (9813, 2.48e19), (4991, 9.11e18) ] I have 4G of physical memory and would like to figure out approximately how many rows (key:values) I can store in
估计对象内存占用量的好方法是什么？ 相反，测量足迹的好方法是什么？ 例如，假设我有一个字典，它的值是整型，浮点型元组列表： d['key'] = [ (1131, 3.11e18), (9813, 2.48e19), (4991, 9.11e18) ] 我拥有4G的物理内存，并且想要知道在溢出之前我可以在内存中存储多少行（键：值）。 这是在linux / ubuntu 8.04和OS X 10.5.6上。 此外，找出程序的实际内存占用空间的最佳方法是什么？ 当耗尽物理记忆和溢出时，我如
I want to measure memory usage by processes on Linux, notably Ubuntu 15.04, but I'm not sure how to do this correctly. I want the measurements to correlate with the free command, so that the total amount of memory found to be in use corresponds to what free reports (sans buffers/cache). So far, I've written this Python script, but it isn't in agreement with free , as it reports a l
我想通过Linux上的进程来测量内存使用情况，特别是Ubuntu 15.04，但我不确定如何正确执行此操作。 我希望测量结果与free命令相关联，以便发现使用的内存总量与free报告（sans buffers / cache）相对应。 到目前为止，我已经编写了这个Python脚本，但它与free ，因为它报告的总内存使用量较低： #!/usr/bin/env python from collections import OrderedDict import os.path import re def parse_mem_file(filename): dat