| import seaborn as sns | |
| from scipy.optimize import curve_fit | |
| # Function for linear fit | |
| def func(x, a, b): | |
| return a + b * x | |
| # Seaborn conveniently provides the data for | |
| # Anscombe's quartet. | |
| df = sns.load_dataset("anscombe") |
Rich Hickey • 3 years ago
Sorry, I have to disagree with the entire premise here.
A wide variety of experiences might lead to well-roundedness, but not to greatness, nor even goodness. By constantly switching from one thing to another you are always reaching above your comfort zone, yes, but doing so by resetting your skill and knowledge level to zero.
Mastery comes from a combination of at least several of the following:
| I recently found myself in need of a function to sample randomly from an arbitrarily defined probability density function. An excellent post by Quantitations shows how to accomplish this using some of Rs fairly sophisticated functional approximation tools such as integrate and uniroot. The only problem with this excellent post was that the machine cost was enormous with samples of 1000 draws taking 10 seconds on my machine and repeated samples of 100,000+ draws (which I was after) clearly being unworkable. | |
| Thus I decided to take my own crack at it. First let us review the basics of drawing random variables from non-uniform distributions. The standard method I think most algorithms use works as follows: | |
| Assumptions | |
| 1. You can draw pseudo-random uniform variable u | |
| 2. You can integrate the pdf to construct a cdf | |
| $$p = F(x) = \int_{-\infty}^\infty f(x) dx$$ | |
| 3. You can invert the cdf in order to solve for p | |
| $$G(F(x))=F^{-1}(F(x))=F^{-1}(p)=x$$ |
| #!/usr/bin/env bash | |
| # Runs a command wrapped in btrfs snapper pre-post snapshots. | |
| # Usage: $ snp <commands> | |
| # e.g.: $ snp pacman -Syyu | |
| # Requirements: snapper (https://wiki.archlinux.org/title/snapper) | |
| # The latest version of this script is hosted at https://gist.github.com/erikw/5229436 | |
| log_path="/var/local/log/snp" | |
| date=$(date "+%Y-%m-%d-%H%M%S") | |
| log_file="${log_path}/snp_${date}.log" |
