hswick

Visualizing the Iris with Principal Component Analysis

This post will teach you how to visualize higher dimensional datasets in lower dimensions using Principal Component Analysis. And guess what?! Its all in Clojure! I was inspired to write this because I was curious how Principal Component Analysis worked, and there aren't a lot of data analysis resources out there for Clojure.

The best one I could find was from Data Sorcery https://data-sorcery.org/category/pca/.

Now that blog post was very informative on how to do Principal Component Analysis (will be referring to this as PCA as well) in Clojure. However, when I decided to use it on a larger dataset I got an out of memory exception because the pca function incanter provides requires a matrix as input. The input matrix requires a lot of memory if the dataset is rather large. So I decided to write my own implementation which could calculate the covariance matrix with an input as a lazyseq. That way my input could be as big as I wanted. And learning

hswick

Atom Clojure Setup

This describes how I setup Atom for an ideal Clojure development workflow. This fixes indentation on newlines, handles parentheses, etc. The keybinding settings for enter (in keymap.cson) are important to get proper newlines with indentation at the right level. There are other helpers in init.coffee and keymap.cson that are useful for cutting, copying, pasting, deleting, and indenting Lisp expressions.

Install Atom

Download Atom

The Atom documentation is excellent. It's highly worth reading the flight manual.

hswick

Install ethereum by following these instructions:

For example on Mac:

brew update
brew upgrade
brew tap ethereum/ethereum
brew install solidity
brew linkapps solidity

hswick

Ethereum

https://www.ethereum.org/

Truebit

https://truebit.io/

https://medium.com/@simondlr/an-intro-to-truebit-a-scalable-decentralized-computational-court-1475531400c3

Dogecoin

Official subreddit