olooney

TARGET_HOST=prometheus

# for each user with a home directory
for USER in `ls /home`; do

    # if the user has a known_hosts files
    echo "checking /home/$USER/.ssh/known_hosts..."
    if [ -f /home/$USER/.ssh/known_hosts ]; then

        # remove the outdated key

olooney

server {
    server_name api.oranlooney.com;
    listen 80;

    # additional security
    server_tokens off;
    add_header X-Frame-Options "DENY";
    add_header X-Content-Type-Options nosniff;

    # performance

olooney

from scipy.optimize import linear_sum_assignment
import numpy as np

def maximize_trace(X):
    """
    Maximize the trace of a square matrix using only row and
    column permutations. Furthermore, sort the trace
    in desending order so that largest value ends
    up the upper left and the smallest in the lower right.
    

olooney

import os
import sys
from time import sleep
from threading import Thread
from queue import Queue
import codecs

def hard_work(x):
    for n in range(100001):
        x = codecs.encode(x, 'rot_13')

olooney

from typing import NamedTuple, Any, Optional

class Node(NamedTuple):
    """A single Node in a binary tree."""
    value: Any
    left: Node
    right: Node

def count(node: Optional[Node]) -> int:
    """Count the total number of nodes in a tree rooted at this node."""

olooney

X = np.hstack([np.ones(shape=(23, 1)), np.random.normal(size=(23, 2))])
Theta = np.array([0.5, +0.1, -0.2]).reshape( (3, 1) )
Y = X @ Theta + np.random.normal(0, 0.1, size=(23, 1))

Theta_hat = np.linalg.inv(X.T @ X) @ X.T @ Y
Theta_hat

olooney

# https://www.youtube.com/watch?v=d3mHfqd0VZY
def harmonic(x, y, n=6, epsilon=1e-3):
    r = sqrt(x/y)
    for a in range(1, n):
        for b in range(1, n):
            if abs(r - a/b) < epsilon:
                return True
    return False

for x in np.arange(1.6, 6.4, 0.2):

olooney

This is a SCRIPT-8 cassette. Click here to boot it.

olooney

# Knuth's algorithm to partition ns into m sets.
# https://codereview.stackexchange.com/questions/1526/finding-all-k-subset-partitions
# http://cs.utsa.edu/~wagner/knuth/
# http://cs.utsa.edu/~wagner/knuth/fasc3b.pdf

# ns must be a list of integers
# m must be an integer less than len(ns)
# this is a generator expression
def algorithm_u(ns, m):
    def visit(n, a):

olooney

library(MASS)
library(ggplot2)

results <- NULL

# Generate simulated data
for ( seed in 1:30 ) {
set.seed(seed+42)
mu = c(0,-1+seed/10)
Sigma = matrix(c(1.5,-.5,-.5,.7),2,2)