git clone git@github.com: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 |