| int currentValue = 0; | |
| int values[] = {0,0}; | |
| void setup() { | |
| Serial.begin(9600); | |
| } | |
| void loop() { | |
| if(Serial.available()){ |
| import random | |
| class Markov(object): | |
| def __init__(self, open_file, chain_size=3): | |
| self.chain_size = chain_size | |
| self.cache = {} | |
| self.open_file = open_file | |
| self.words = self.file_to_words() | |
| self.word_size = len(self.words) |
People
 :bowtie: |
😄 :smile: |
😆 :laughing: |
|---|---|---|
😊 :blush: |
😃 :smiley: |
:relaxed: |
😏 :smirk: |
😍 :heart_eyes: |
😘 :kissing_heart: |
😚 :kissing_closed_eyes: |
😳 :flushed: |
😌 :relieved: |
😆 :satisfied: |
😁 :grin: |
😉 :wink: |
😜 :stuck_out_tongue_winking_eye: |
😝 :stuck_out_tongue_closed_eyes: |
😀 :grinning: |
😗 :kissing: |
😙 :kissing_smiling_eyes: |
😛 :stuck_out_tongue: |
This whole thing hinges on having the right version of Python installed. We're going to do it via Homebrew. You have to install Python BEFORE you install Vim. So if Vim is already installed (the Homebrew version, that is) then remove it first.
Check to see if vim or python are installed.
brew list
It's not immediately obvious how to pull down the code for a PR and test it locally. But it's pretty easy. (This assumes you have a remote for the main repo named upstream.)
Getting the PR code
Make note of the PR number. For example, Rod's latest is PR #37: Psiphon-Labs/psiphon-tunnel-core#37
Fetch the PR's pseudo-branch (or bookmark or rev pointer whatever the word is), and give it a local branch name. Here we'll name it pr37:
$ git fetch upstream pull/37/head:pr37
| # Implementation of a simple MLP network with one hidden layer. Tested on the iris data set. | |
| # Requires: numpy, sklearn, theano | |
| # NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1' | |
| # where x' = [x | 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's. | |
| # Similarly, for h * W_2 + b_2 | |
| import theano | |
| from theano import tensor as T | |
| import numpy as np | |
| from sklearn import datasets |
| source 'https://rubygems.org' | |
| gem 'osc-ruby', '1.1.1' | |
| gem 'serialport' |
| use_debug false | |
| use_bpm 130 | |
| # Our mixer! | |
| master = (ramp *range(0, 1, 0.01)) | |
| kick_volume = 1 | |
| bass_volume = 1 | |
| revbass_volume = 1 | |
| snare_volume = 0.5 | |
| hats_volume = 0.5 |
| """ 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 |
| ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" | |
| alias brew=/usr/local/bin/brew | |
| brew install python3 | |
| alias python=python3 | |
| brew install mercurial | |
| brew install git | |
| brew install sdl sdl_image sdl_mixer sdl_ttf | |
| brew tap homebrew/headonly | |
| brew install --HEAD smpeg | |
| brew install portmidi |
