git clone [email protected]:YOUR-USERNAME/YOUR-FORKED-REPO.git
cd into/cloned/fork-repo
git remote add upstream git://github.com/ORIGINAL-DEV-USERNAME/REPO-YOU-FORKED-FROM.git
git fetch upstream
git clone [email protected]:YOUR-USERNAME/YOUR-FORKED-REPO.git
cd into/cloned/fork-repo
git remote add upstream git://github.com/ORIGINAL-DEV-USERNAME/REPO-YOU-FORKED-FROM.git
git fetch upstream
private boolean isServiceRunning() { | |
ActivityManager manager = (ActivityManager) getSystemService(ACTIVITY_SERVICE); | |
for (RunningServiceInfo service : manager.getRunningServices(Integer.MAX_VALUE)){ | |
if("com.example.MyNeatoIntentService".equals(service.service.getClassName())) { | |
return true; | |
} | |
} | |
return false; | |
} |
Install convmv if you don't have it
sudo apt-get install convmv
Convert all files in a directory from NFD to NFC:
convmv -r -f utf8 -t utf8 --nfc --notest .
// Require our core node modules. | |
var util = require( "util" ); | |
// Export the constructor function. | |
exports.AppError = AppError; | |
// Export the factory function for the custom error object. The factory function lets | |
// the calling context create new AppError instances without calling the [new] keyword. | |
exports.createAppError = createAppError; |
""" | |
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
BSD License | |
""" | |
import numpy as np | |
# data I/O | |
data = open('input.txt', 'r').read() # should be simple plain text file | |
chars = list(set(data)) | |
data_size, vocab_size = len(data), len(chars) |
class A { | |
fun shout() = println("go team A!") | |
} | |
class B { | |
fun shout() = println("go team B!") | |
} | |
interface Shoutable { | |
fun shout() |
import sys,os | |
import curses | |
def draw_menu(stdscr): | |
k = 0 | |
cursor_x = 0 | |
cursor_y = 0 | |
# Clear and refresh the screen for a blank canvas | |
stdscr.clear() |
{0: 'tench, Tinca tinca', | |
1: 'goldfish, Carassius auratus', | |
2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias', | |
3: 'tiger shark, Galeocerdo cuvieri', | |
4: 'hammerhead, hammerhead shark', | |
5: 'electric ray, crampfish, numbfish, torpedo', | |
6: 'stingray', | |
7: 'cock', | |
8: 'hen', | |
9: 'ostrich, Struthio camelus', |
""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
import numpy as np | |
import cPickle as pickle | |
import gym | |
# hyperparameters | |
H = 200 # number of hidden layer neurons | |
batch_size = 10 # every how many episodes to do a param update? | |
learning_rate = 1e-4 | |
gamma = 0.99 # discount factor for reward |