abikoushi

rcfnorm <- function(n, shape=0, inv_scale=1){
  rnorm(n, shape/inv_scale, 1/sqrt(inv_scale))
}

dcfnorm <- function(n, shape=0, inv_scale=1, ...){
  dnorm(n, shape/inv_scale, 1/sqrt(inv_scale), ...)
}

pcfnorm <- function(n, shape=0, inv_scale=1, ...){
  pnorm(n, shape/inv_scale, 1/sqrt(inv_scale), ...)

abikoushi

size_mtx <- function(file_path){
  con <- file(file_path, open = "r") #Open for reading in text mode
  size <- scan(con, what=integer(), comment.char = "%", nlines = 1, skip = 1, quiet = TRUE)
  close(con)
  names(size) <- c("row","column","nonzero")
  return(size)
}

take_last <- function(x){
  x[length(x)]

abikoushi

\documentclass[tikz,border=10pt]{standalone}
\usetikzlibrary{matrix,positioning,quotes}
\begin{document}

\tikzset{ 
    table/.style={
        matrix of nodes,
        row sep=-\pgflinewidth,
        column sep=-\pgflinewidth,
        nodes={

abikoushi

%ref:
%https://tex.stackexchange.com/questions/516073/how-to-draw-3d-matrix-using-tikz
%https://tikz.net/tikz-table/
%https://tikz.net/conv2d/
%
\documentclass[tikz,border=10pt]{standalone}
\usepackage[dvipsnames]{xcolor}
\usetikzlibrary{matrix,positioning}
\begin{document}

abikoushi

%ref:
%https://tex.stackexchange.com/questions/516073/how-to-draw-3d-matrix-using-tikz
%https://tikz.net/tikz-table/
%https://tikz.net/conv2d/
%
\documentclass[tikz,border=10pt]{standalone}
\usetikzlibrary{matrix,positioning}
\begin{document}

\tikzset{ 

abikoushi

%ref:
%https://tex.stackexchange.com/questions/516073/how-to-draw-3d-matrix-using-tikz
%https://tikz.net/tikz-table/
%
\documentclass[tikz,border=10pt]{standalone}
\usetikzlibrary{matrix,positioning}
\begin{document}

\tikzset{ 
    table/.style={

abikoushi

#ref:
#Jan Laukemann et al. (2025) Accelerating Sparse Tensor Decomposition Using Adaptive Linearized Representation
#https://arxiv.org/abs/2403.06348

ALTO_indexing <- function(object, data = environment(object), ...) {
  mf <- model.frame(object, data, ...)
  t <- if (missing(data)) terms(object) else terms(object, data = data)
  labs <- attr(t, "term.labels")

  mf <- lapply(labs, function(x) {

abikoushi

library(moltenNMF)
library(Matrix)

df = expand.grid(letters[1:2], LETTERS[1:2])
X = sparse_onehot(~., data = df)

A = matrix(
  c(0.5, 0.5, 0.5, 0.5, 0, 1, 0, 0, 0, 0, 0, 1),
  nrow = 4,
  ncol = 3

abikoushi

#ref:
#Jan Laukemann et al. (2025) Accelerating Sparse Tensor Decomposition Using Adaptive Linearized Representation
#https://arxiv.org/abs/2403.06348

library(dplyr)
ALTO_indexing <- function(object, data = environment(object), ...) {
  mf <- model.frame(object, data, ...)
  t <- if (missing(data)) terms(object) else terms(object, data = data)
  labs <- attr(t, "term.labels")

abikoushi

library(ggplot2)
library(dplyr)
library(tidyr)
library(rvest)
library(readr)

url = "https://ja.wikipedia.org/wiki/2015年韓国におけるMERSの流行"
taball <- read_html(url) |>
  html_table()