michaelfeng

#!/usr/bin/env bash
# Reset routing table on OSX

# display current routing table
echo "********** BEFORE ****************************************"
netstat -r
echo "**********************************************************"

for i in {0..4}; do
  sudo route -n flush # several times

michaelfeng

// T 指代任何你希望存储的类型
Queue<T> queue = new LinkedList<>();
Set<T> set = new HashSet<>();

set.add(start);
queue.offer(start);
while (!queue.isEmpty()) {
    T head = queue.poll();
    for (T neighbor : head.neighbors) {
        if (!set.contains(neighbor)) {

michaelfeng

class Pipe(object):
    def __init__(self, func):
        self.func = func
 
    def __ror__(self, other):
        def generator():
            for obj in other:
                if obj is not None:
                    yield self.func(obj)
        return generator()

michaelfeng

"""
tiny script to convert a pandas data frame into a JSON object
"""
 
import json as json

def df_to_json(df, filename=''):
    x = df.reset_index().T.to_dict().values()
    if filename:
        with open(filename, 'w+') as f: f.write(json.dumps(x))

michaelfeng

import numpy as np
import cv2


class BackGroundSubtractor:
	# When constructing background subtractor, we
	# take in two arguments:
	# 1) alpha: The background learning factor, its value should
	# be between 0 and 1. The higher the value, the more quickly
	# your program learns the changes in the background. Therefore, 

michaelfeng

import numpy as np
import cv2

def getSobel (channel):

    sobelx = cv2.Sobel(channel, cv2.CV_16S, 1, 0, borderType=cv2.BORDER_REPLICATE)
    sobely = cv2.Sobel(channel, cv2.CV_16S, 0, 1, borderType=cv2.BORDER_REPLICATE)
    sobel = np.hypot(sobelx, sobely)

    return sobel;

michaelfeng

def karatsuba(x,y):
	"""Function to multiply 2 numbers in a more efficient manner than the grade school algorithm"""
	if len(str(x)) == 1 or len(str(y)) == 1:
		return x*y
	else:
		n = max(len(str(x)),len(str(y)))
		nby2 = n / 2
		
		a = x / 10**(nby2)
		b = x % 10**(nby2)

michaelfeng

import os

import falcon
import jinja2


def load_template(name):
    path = os.path.join('templates', name)
    with open(os.path.abspath(path), 'r') as fp:
        return jinja2.Template(fp.read())

michaelfeng

#!/usr/bin/env python2
'''Serving dynamic images with Pandas and matplotlib (using flask).'''

import matplotlib
matplotlib.use('Agg')

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from cStringIO import StringIO

michaelfeng

# to generate your dhparam.pem file, run in the terminal
openssl dhparam -out /etc/nginx/ssl/dhparam.pem 2048