suzaku

[etcd] Jul 31 03:41:53.475 INFO      | 20634ec121f9479ca7a6cd50a5efcc0b: warning: heartbeat time out peer="86a6adecb8c
[etcd] Jul 31 03:42:24.976 INFO      | 20634ec121f9479ca7a6cd50a5efcc0b: warning: heartbeat time out peer="86a6adecb8c
[etcd] Jul 31 03:44:00.477 INFO      | 20634ec121f9479ca7a6cd50a5efcc0b: warning: heartbeat time out peer="86a6adecb8c
[etcd] Jul 31 03:44:32.225 INFO      | 20634ec121f9479ca7a6cd50a5efcc0b: warning: heartbeat time out peer="86a6adecb8c
[etcd] Jul 31 03:45:36.475 INFO      | 20634ec121f9479ca7a6cd50a5efcc0b: warning: heartbeat time out peer="86a6adecb8c
[etcd] Jul 31 03:46:10.975 INFO      | 20634ec121f9479ca7a6cd50a5efcc0b: warning: heartbeat time out peer="86a6adecb8c
[etcd] Jul 31 03:46:43.976 INFO      | 20634ec121f9479ca7a6cd50a5efcc0b: warning: heartbeat time out peer="86a6adecb8c
[etcd] Jul 31 03:47:12.563 INFO      | 20634ec121f9479ca7a6cd50a5efcc0b: warning: heartbeat time out peer="86a6adecb8c
[etcd] Jul 31 03:47:16.475 INFO      | 20634ec121f9479ca7a6cd50a5efcc0b:

suzaku

(function () {
    var hot_movies = document.querySelectorAll('.ui-slide-item');
    [].forEach.call(
        hot_movies,
        function (e) {
            var rate = e.getAttribute('data-rate');
            rate = parseInt(rate, 10);
            if (!isNaN(rate) && rate < 7) {
                e.remove();
            }

suzaku

method1-6 are taken from this article

method7 is added because I believe using generator expression would be faster

to run this experiment, you'll need to install line_profiler, memory_profiler and psutil first, all of which are just one pip install away.

after installing these profiling tools, we can run stest.py like this:

kernprof.py -l -v stest.py method1

suzaku

import os
import sys

def my_system(command):
    pid = os.fork()

    if pid == -1:
        return -1
    elif pid == 0:
        os.execv('/bin/sh', ['/bin/sh', '-c', command])

suzaku

In [19]: fd = os.open('t1', os.O_CREAT | os.O_DSYNC | os.O_WRONLY)                                                                                                                       
 
In [20]: %timeit os.write(fd, 'a')
1000 loops, best of 3: 232 us per loop
 
In [21]: fd2 = os.open('t2', os.O_CREAT | os.O_SYNC | os.O_WRONLY)                                                                                                                       
 
In [22]: %timeit os.write(fd2, 'a')                                                                                                                                                      
1000 loops, best of 3: 737 us per loop
 

suzaku

Pony Object-Relational Mapper

Pony is an object-relational mapper. The most interesting feature of Pony is its ability to write queries to the database using generator expressions. Pony works with entities which are mapped to a SQL database. Using generator syntax for writing queries allows the user to formulate very eloquent queries. It increases the level of abstraction and allows a programmer to concentrate on the business logic of the application. For this purpose Pony analyzes the abstract syntax tree of a generator and translates it to its SQL equivalent.

Following is an example of a query in Pony:

    select(p for p in Product if p.name.startswith('A') and p.cost <= 1000)

suzaku

"""Compute mean and variance to normalize data"""
import dpark
import plac
import numpy as np

def prepare(s):
    return np.array([float(i.strip()) for i in s.split(",")])

def main(filename):
    lines = dpark.textFile(filename)

suzaku

"""Compute mean and variance to normalize data"""
import dpark
import plac
import math

def prepare(s):
    return [float(i.strip()) for i in s.split(",")]

def add_squared_list(vals):
    squared_vals = [v * v for v in vals]

suzaku

import plac
import random

@plac.annotations(name="your name")
def hello(name):
    "Greet you half of the time, and otherwise * you"
    if random.randint(0, 10) > 5:
        print "Hello %s" % name
    else:
        print "Screw you. I'm ... going ... home~"

suzaku

import random
import math
import csv 

def hypothesis(params, features):
    z = sum(p * f for p, f in zip(params, features))
    return 1 / (1 + math.e ** -z) 


def logistic_regression(learning_rate, samples):