python3 高精度小數

python3 高精度小數

做數學運算需要用高精度的浮點數，可以使用decimal模組

>>> from decimal import *
>>> Decimal(1/3)Decimal('0.333333333333333314829616256247390992939472198486328125')

>>> Decimal( 5e-115)

Decimal('5.000000000000000253224511584642904700031198975255267803449800938244847

07054082960234923746081859400858021097772021778402792777070155705765691787462823

35047908242924101834562519900509625714227417248975325003782562360654965752209815

93257539786334869563372595928867119233244230552121178945412793837022036314010620

1171875E-115')

ref: http://www.cnblogs.com/herbert/p/3402245.html

java iterators

Iterators

Q. What is an Iterable ?
A. Has a method that returns an Iterator.

public interface Iterable<Item>
{
    Iterator<Item> iterator();
}

Q. What is an Iterator ?

A. Has methods hasNext() and next().

public interface Iterator<Item>

{

    boolean hasNext();

    Item next();

    void remove();

}

Q. Why make data structures Iterable ?

A. Java supports elegant client code.

for (String s : stack){

    StdOut.println(s);

}

ref: http://algs4.cs.princeton.edu/home/

python3 multiprocessing: Server process

Python3

Server process

A manager object returned by Manager() controls a server process which holds Python objects and allows other processes to manipulate them using proxies.

A manager returned by Manager() will support types list, dict, Namespace, Lock, RLock, Semaphore, BoundedSemaphore,Condition, Event, Barrier, Queue, Value and Array. For example,

from multiprocessing import Process, Manager

def f(d, l):
    d[1] = '1'
    d['2'] = 2
    d[0.25] = None
    l.reverse()

if __name__ == '__main__':
    with Manager() as manager:
        d = manager.dict()
        l = manager.list(range(10))

        p = Process(target=f, args=(d, l))
        p.start()
        p.join()

        print(d)
        print(l)

will print

{0.25: None, 1: '1', '2': 2}
[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]

Server process managers are more flexible than using shared memory objects because they can be made to support arbitrary object types. Also, a single manager can be shared by processes on different computers over a network. They are, however, slower than using shared memory.

ref: https://docs.python.org/3.4/library/multiprocessing.html

python3 用profiling測執行時間

Python 有內建 profiling 的工具，叫做 profile，可以透過命令列直接下參數使用：

1	python -m cProfile -s time MODULE.py ARGUMENTS

這樣會執行 MODULE 再將測量結果輸出到螢幕，時間依單一函式執行時間來排序。引用官網的例子，cProfile 的輸出格式如下：

1 2 3 4 5 6 7 8

2706 function calls (2004 primitive calls) in 4.504 CPU seconds   Ordered by: standard name   ncalls tottime percall cumtime percall filename:lineno(function) 2 0.006 0.003 0.953 0.477 pobject.py:75(save_objects) 43/3 0.533 0.012 0.749 0.250 pobject.py:99(evaluate) ...

轉錄自: http://fcamel-fc.blogspot.tw/2010/01/python.html