| use std::io::{Read, Write}; | |
| use std::fs::File; | |
| fn main() { | |
| let args = ::std::env::args().collect::<Vec<_>>(); | |
| // skip the program name | |
| if args.len() != 5 { | |
| println!("Usage: {} <filename> <columnname> <replacement> <output_filename>", args[0]); | |
| } | |
Ripgrep is a fast search tool like grep. It's mostly a drop-in replacement for ag, also know as the Silver Searcher.
helm-ag is a fantastic package for Emacs that allows you to display search results in a buffer.
You can also jump to locations of matches. Despite the name, helm-ag works with ripgrep (rg) as well
as with ag.
Using perf:
$ perf record -g binary
$ perf script | stackcollapse-perf.pl | rust-unmangle | flamegraph.pl > flame.svg
NOTE: See @GabrielMajeri's comments below about the
-goption.
| """ 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 |
| """ | |
| 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) |