| """ | |
| A minimal implementation of Monte Carlo tree search (MCTS) in Python 3 | |
| Luke Harold Miles, July 2019, Public Domain Dedication | |
| See also https://en.wikipedia.org/wiki/Monte_Carlo_tree_search | |
| https://gist.github.com/qpwo/c538c6f73727e254fdc7fab81024f6e1 | |
| """ | |
| from abc import ABC, abstractmethod | |
| from collections import defaultdict | |
| import math |
| " Put this in your .vimrc and whenever you `git commit` you'll see the diff of your commit next to your commit message. | |
| " For the most accurate diffs, use `git config --global commit.verbose true` | |
| " BufRead seems more appropriate here but for some reason the final `wincmd p` doesn't work if we do that. | |
| autocmd VimEnter COMMIT_EDITMSG call OpenCommitMessageDiff() | |
| function OpenCommitMessageDiff() | |
| " Save the contents of the z register | |
| let old_z = getreg("z") | |
| let old_z_type = getregtype("z") |
| # Benchmark to compare the times for storing numpy arrays in a key-value. | |
| # The main point is to compare numpy serialization vs a bcolz approach. | |
| from __future__ import print_function | |
| import sys | |
| import os | |
| import os.path | |
| import subprocess | |
| import getopt |
| # Context manager to generate batches in the background via a process pool | |
| # Usage: | |
| # | |
| # def batch(seed): | |
| # .... # generate minibatch | |
| # return minibatch | |
| # | |
| # with BatchGenCM(batch) as bg: | |
| # minibatch = next(bg) | |
| # .... # do something with minibatch |
A comparison of Theano with other deep learning frameworks, highlighting a series of low-level design choices in no particular order.
Overview
Symbolic: Theano, CGT; Automatic: Torch, MXNet
Symbolic and automatic differentiation are often confused or used interchangeably, although their implementations are significantly different.
As of writing, this is only possible with my fork of pandas, but hopefully it will make its way into the main pandas stable branch.
The purpose of the fork is to let you specify a custom html formatter for individual columns in a data frame.
In this example we create a formatting function which takes a numpy array and returns a string of the form <img src='--base64-encoded-data'/>. This means that the numpy array is displayed as an image.
Below is the code we use to define our custom format function. Note that the function takes a single element from the data frame and returns and html string::
The final result: require() any module on npm in your browser console with browserify
This article is written to explain how the above gif works in the chrome (and other) browser consoles. A quick disclaimer: this whole thing is a huge hack, it shouldn't be used for anything seriously, and there are probably much better ways of accomplishing the same.
Update: There are much better ways of accomplishing the same, and the script has been updated to use a much simpler method pulling directly from browserify-cdn. See this thread for details: mathisonian/requirify#5
| /* | |
| Swift Programming Language Guide | |
| "A Swift Tour" Solutions | |
| https://developer.apple.com/library/prerelease/ios/documentation/Swift/Conceptual/Swift_Programming_Language/GuidedTour.html#//apple_ref/doc/uid/TP40014097-CH2-XID_1 | |
| These are the solutions to all the "experiments" in the pre-release Swift Programming Guide | |
| (released on the same day Apple announced Swift). A couple of things worth noting: | |
| 1. Swift syntax, e.g. array declarations, has changed since I releasd these. So this code will | |
| probably cause some errors when you paste it into a playground. Should be easy enough to fix |
