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)

olooney

CREATE OR REPLACE FUNCTION pnorm(z double precision) RETURNS double precision AS $$
  SELECT CASE 
  	WHEN $1 >= 0 THEN 1 - POWER(((((((0.000005383*$1+0.0000488906)*$1+0.0000380036)*$1+0.0032776263)*$1+0.0211410061)*$1+0.049867347)*$1+1),-16)/2
  	ELSE 1 - pnorm(-$1)
  END;
$$ LANGUAGE SQL IMMUTABLE STRICT;

olooney

# MP setup *must* happen at the very start of the process!
# Otherwise you'll get errors or it will be ignored.
import multiprocessing
multiprocessing.set_start_method('forkserver')
import os
os.environ["OMP_NUM_THREADS"] = "10"

import numpy as np
import pandas as pd
import pickle

olooney

import platform

print("architecture = {!r}".format(platform.architecture()))
print("machine = {!r}".format(platform.machine()))
print("node = {!r}".format(platform.node()))
print("platform = {!r}".format(platform.platform()))
print("processor = {!r}".format(platform.processor()))
print("python_build = {!r}".format(platform.python_build()))
print("python_compiler = {!r}".format(platform.python_compiler()))
print("python_branch = {!r}".format(platform.python_branch()))

olooney

//g++ -std=c++17 -O3 -I eigen main.cpp -o main && time ./main
//g++ -std=c++17 -fopenmp -O3 -I eigen main.cpp -o main && time ./main
//g++ -std=c++17 -pg -fopenmp -O3 -I eigen main.cpp -o main && time ./main && gprof main gmon.out > profile.txt
#include <iostream>
#include <Eigen/Dense>
#include <cmath>

using namespace Eigen;

float logistic(float x) {

olooney

import kafka
p = kafka.KafkaProducer(bootstrap_servers='kafka:9092')
c = kafka.KafkaConsumer(bootstrap_servers='kafka:9092', auto_offset_reset='earliest')
p.send('oran', b'cool')
c.subscribe('oran')
p.send('oran', b'neat')
v1 = next(c)
v2 = next(c)
print(v1)
print(v2)

olooney

# apt-get install tesseract-ocr
# pip3 install Pillow, pytesseract
from PIL import Image, ImageDraw, ImageFont
import os, sys
import pytesseract as t

def round_trip(text, font_size=32):
        font_filename = '/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf'
        font = ImageFont.truetype(font_filename, size=font_size)
        text_size = font.getsize(text)

olooney

compare_histograms <- function(sn) { 
  sample_means <- function(r,n) { sqrt(n)*rowMeans(matrix(r(n*10000), ncol=n)) }
  center_scale <- function(v) (v - mean(v))/sd(v)
  
  exp_sample <- center_scale(sample_means(rexp,  sn))
  unif_sample <- center_scale(sample_means(runif, sn)) 

  max_break = ceiling(max(c(unif_sample, exp_sample)))
  min_break = floor(min(c(unif_sample, exp_sample)))
  breaks = seq(min_break,max_break,length.out=(max_break-min_break)*8+1)

olooney

Markdown==2.6.10
numpy==1.6.1
ordereddict==1.2
pandas==0.14.1
patsy==0.4.1
python-dateutil==2.6.1
pytz==2017.3
requests==2.18.4
six==1.11.0