- 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 |