ha0ye

# For more info, see http://mathworld.wolfram.com/PartitionFunctionQ.html
# This uses the 2nd recurrence relation defined therein.

max_n <- 1000

# helper function
s <- integer(length = max_n)
for(j in 1:sqrt((2*max_n+1)/3))
{
    n <- j * (3 * j + 1) / 2

ha0ye

Keybase proof

I hereby claim:

• I am ha0ye on github.
• I am hao_and_y (https://keybase.io/hao_and_y) on keybase.
• I have a public key ASALs4BLGzLmT9iU4H5GOc11WPPLg9KQvuwvbLG8MntrQwo

To claim this, I am signing this object:

ha0ye

travis_fold:start:worker_info
Worker information
hostname: 12607e0a-d790-494c-abc0-33bec19d51ab@1.production-1-worker-org-a-4-gce
version: v3.6.0 https://github.com/travis-ci/worker/tree/170b2a0bb43234479fd1911ba9e4dbcc36dadfad
instance: travis-job-a2fd8785-6b65-4c56-b87e-cbd4d1016e0f travis-ci-garnet-trusty-1512502259-986baf0 (via amqp)
startup: 26.186769693s
travis_fold:end:worker_info
travis_fold:start:system_info
Build system information
Build language: ruby

ha0ye

library(lavaan)
library(visNetwork)

model <- '
  # measurement model
    ind60 =~ x1 + x2 + x3
    dem60 =~ y1 + y2 + y3 + y4
    dem65 =~ y5 + y6 + y7 + y8
  # regressions
    dem60 ~ ind60

ha0ye

library(drake)
library(furrr)

future::plan(future::multiprocess) 

tictoc::tic()
params = data.frame(t = c(1, 2, 3, 1, 2, 3))
out = purrr::pmap(params, Sys.sleep)
tictoc::toc()

ha0ye

library(likert)
data(pisaitems)

##### Item 24: Reading Attitudes
items24 <- pisaitems[,substr(names(pisaitems), 1,5) == 'ST24Q']
head(items24); ncol(items24)

names(items24) <- c(
    ST24Q01="I read only if I have to.",
    ST24Q02="Reading is one of my favorite hobbies.",

ha0ye

library(dplyr)
library(microbenchmark)

set.seed(42)
n <- 10000
df <- data.frame(a = rep(seq(n), 2), 
                 x = rnorm(2 * n))

group_summarize_mutate <- function(df)
{

ha0ye

full_text <- readLines(file, warn = FALSE)
start_par_line <- grep("^\\$par", full_text)
start_objective_line <- grep("^\\$objective", full_text)
param_lines <- seq(from = start_par_line, 
                   to = start_objective_line - 3, 
                   by = 2)

params <- purrr::map_dfc(param_lines, function(line_idx)
{
    readr::read_delim(file, delim = " ", 

ha0ye

library(rEDM)

#### Example 1: a single forward prediction

# Assume we have already done the analysis, finding that the best embedding 
#   dimension is 2. Our objective is to make multiple 1-step ahead forecasts 
#   using simplex and E = 2.
dat <- data.frame(yr = as.numeric(time(sunspot.year)), 
                  sunspot_count = as.numeric(sunspot.year))
E <- 3

ha0ye

calc3 <- function(sets)
{
  sets <- check_sets(sets)
  set_lengths <- vapply(sets, length, 0)
  set_order <- order(set_lengths)
  sets <- sets[set_order]
  set_lengths <- set_lengths[set_order]
  
  n_sets <- length(sets)
  set_names <- names(sets)