akihiro4chawon

package akihiro4chawon

class Main {
    @Memoize
    def fib(a) {
        //a <= 1 ? a : fib(a - 2) + fib(a - 1)
        if (a <= 1) return a
        else return fib(a - 2) + fib(a - 1)
    }

mblondel

# Copyright Mathieu Blondel December 2011
# License: BSD 3 clause

import numpy as np
import pylab as pl

from sklearn.base import BaseEstimator
from sklearn.utils import check_random_state
from sklearn.cluster import MiniBatchKMeans
from sklearn.cluster import KMeans as KMeansGood

hanleybrand

def java_string_hashcode(s):
    h = 0
    for c in s:
        h = (31 * h + ord(c)) & 0xFFFFFFFF
    return ((h + 0x80000000) & 0xFFFFFFFF) - 0x80000000

damianavila

"""
Usage: python remove_output.py notebook.ipynb [ > without_output.ipynb ]
Modified from remove_output by Minrk

"""
import sys
import io
import os
from IPython.nbformat.current import read, write

darribas

lmarkus

Extracting Emoji From Slack!

Slack doesn't provide an easy way to extract custom emoji from a team. (Especially teams with thousands of custom emoji) This Gist walks you through a relatively simple approach to get your emoji out.

If you're an admin of your own team, you can get the list of emoji directly using this API: https://api.slack.com/methods/emoji.list. Once you have it, skip to Step 3

HOWEVER! This gist is intended for people who don't have admin access, nor access tokens for using that list.

Follow along...

chenjianjx

'''
A python script which starts celery worker and auto reload it when any code change happens.  
I did this because Celery worker's "--autoreload" option seems not working for a lot of people. 
'''

import time
from watchdog.observers import Observer  ##pip install watchdog
from watchdog.events import PatternMatchingEventHandler  
import psutil  ##pip install psutil
import os

ld86

sswt

def eval_map(yhat, dtrain):
    inds = (-yhat).argsort(kind='mergesort')
    X = np.vstack([dtrain.data['display_id'], dtrain.get_label()]).T
    X = X[inds[X[inds, 0].argsort(kind='mergesort')]]
    y_ind = np.where(X[:, 1] == 1)[0]
    y_pr = np.unique(X[:, 0], return_index=True)[1]
    return 'map@12_ob', np.mean(1 / (y_ind - y_pr + 1)), True

tmylk