- Author: Richard Wei
- Date: October 2018
This document is written for both the machine learning community and the Swift programming language design community, with a strong focus on language design.
| #!/usr/bin/env bash | |
| aws-mfa-print-info() | |
| { | |
| echo "We've set your credentials in this shell" | |
| echo "Generated at: '${EPHEMERAL_TOKEN_GENERATED_AT}'" | |
| echo "These credentials are valid for *12 hours*" | |
| unset EPHEMERAL_TOKEN_GENERATED_AT | |
| } |
This document is written for both the machine learning community and the Swift programming language design community, with a strong focus on language design.
| ### JHW 2018 | |
| import numpy as np | |
| import umap | |
| # This code from the excellent module at: | |
| # https://stackoverflow.com/questions/4643647/fast-prime-factorization-module | |
| import random |
| /* | |
| * Easing Functions - inspired from http://gizma.com/easing/ | |
| * only considering the t value for the range [0, 1] => [0, 1] | |
| */ | |
| EasingFunctions = { | |
| // no easing, no acceleration | |
| linear: function (t) { return t }, | |
| // accelerating from zero velocity | |
| easeInQuad: function (t) { return t*t }, | |
| // decelerating to zero velocity |