leoluyi

# library(RSQLServer)
# library(RJDBC)
library(magrittr)

setGeneric("dbCreateTable", valueClass = "logical",
           signature = c("conn", "name", "value"),
           function(conn, name, value, ...) {
             standardGeneric("dbCreateTable")
           }
)

juanitobanca

library('ggplot2')

# Dataset column names and classes
l_colnames   = c( 'game_no','stadium', 'team', 'x_cord', 'y_cord', 'desc' )
l_colClasses = c( 'numeric', 'character', 'character', 'numeric', 'numeric', 'character' )

# Load the dataset
hip_data <- read.csv( file       = 'hitsPerGame.csv'
                    , header     = F  
                    , col.names  = l_colnames

criccomini

import os
import unittest

from airflow.models import DagBag

class TestDags(unittest.TestCase):
    """
    Generic tests that all DAGs in the repository should be able to pass.
    """

baraldilorenzo

##VGG19 model for Keras

This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

chroth7

React <-> D3 Resources

Thanks for all the stars.

Due to unexpected demand, I move this to a proper github repo, see here: https://github.com/chroth7/reactD3resources

I would very much like to get your PRs there, thank you!

c9s

/*
The original script: https://github.com/dherbst/slack-invite-script/blob/master/code.js
*/


/*
Get channels from: curl 'https://golang.slack.com/api/channels.list?token={token}' | json_pp
*/
function getInviteChannels() {
  return ['C123123123']; 

ronnywang

2015401
{"<3":2612,"  ":1076,"知道":976,"乃哥":952,"李嚴":907,"當家":814,"RR":814,"..":783,"幹 ":754,"小當":742,"!!":715," <":707,"你又":668,"嘟嘟":665,"又知":657,"道了":589,"ㄤㄤ":574,"~~":571,"雷恩":557,"B嘴":552,"料理":538,"怎麼":520,"ED":515,"LE":504,"醬汁":498,"紹安":497,"不要":481," 幹":478,"所以":466,"le":460,"0":460,"不是":451,"1":445,"了 ":430,"bl":429,"出來":427,"向恩":414,"還是":410,"什麼":405,"mp":401,"這樣":397,"hu":396,"ib":395,"um":395,"Th":390,"Bi":389,"eT":384,"%%":379,"魚片":377,"銅牌":377,"生魚":369,"閉嘴":368,"XD":366,"不錄":360,"ww":359,"來的":355,"錄了":353,"\/\/":353,"2":352,"礦物":351,"物質":350,"大麻":344,"小時":344," B":336," 你":335,"四郎":330,"是你":328,"阿飛":320,"ht":320,"tt":318,"tp":316,":\/":316,"0勝":308,"就是":304," 我":301,"1小":299,"可以":298,"3":294,"你的":293,"你不":292,"??":288,"王福":287,"ㄋㄟ":282,"魯夫":282,".c":281,"的料":280,":幹":278,"沒有":277,"RQ":277,"這個":275,"阿貝":267,"ㄟㄋ":263,"的，":261,"幹":256,"co":255,"om":255,"你還":252,"來了":249,"最後":248,"GD":246}
2015402
{"<3":2599,"  ":1496,"RR":1309,"嘟嘟":1086,"幹 ":1054,"當家":1038,"小當":949,"王福":947,"~~":918,"!!":907,"ED":849,

philipz

1. Use the Docker images that includes export volume.
2. Backup volume data
3. Docker-machine create
4. Docker Save/load
5. Run up docker container
6. Import data to volume
7. Check service

kkc

##TUNING##

Configuration

System: set file descriptors to 32K or 64K

vim /etc/security/limit.conf

jazzwang

#!/bin/bash
case "$1" in
new)
	echo "[INFO] Creating new gitlab docker instances ..."
	docker create --name='gitlab' -it -e 'GITLAB_PORT=10080' -e 'GITLAB_SSH_PORT=10022' -p 10022:22 -p 10080:80 -v /var/run/docker.sock:/run/docker.sock -v $(which docker):/bin/docker -v $(pwd):/vagrant sameersbn/gitlab:latest
	;;
start)
	echo "[INFO] Starting gitlab docker instances ..."
	docker start gitlab
	;;