Quantum Random Numbers

import requests
import json
random_no = json.loads(requests.get(\

"https://qrng.anu.edu.au/API/jsonI.php?length=1&type=uint16").text)['data'][0]
print(random_no)

Probability plot - some random question

# There are 50 similar Red balls, 50 similar blue balls, and  exactly 2 similar jars with 
# infinite capacity.
# How can we distribute the balls in 2 jars, so as to maximize the probability that when a 
# ball is picked randomly it is Red ball.

library(plotly)

calc_prob <- function(R , B){

  

  0.5 * (R/(R+B)) + (0.5* (50-R) /(100-R-B))

  

}

r <- c(1:50)

b <-c(1:50)

p <- outer(r,b,FUN = "calc_prob")

p[is.na(p)] <- 0.5

max(p)

calc_prob(49,50)

plotz

Stratified Sampling in R

get_stratified_positions <- function (input_label_list, percentage
                                     
){
 
  set.seed(123)
  temp_df <- data.frame(label = input_label_list)
 
  label_distribution <- group_by(temp_df, label) %>%
    summarise(total_count = n()) %>%
    arrange(desc(total_count))
 
 
  label_distribution$train_count <-  as.integer(label_distribution$total_count * percentage)
  label_distribution$test_count <- label_distribution$total_count - label_distribution$train_count
 
 
  train_position <- c()
  test_position <- c()
 
  for (i in 1:nrow(label_distribution)){
   
   
    position_list   <- which(temp_df$label == label_distribution[i,]$label )
    train_position1 <- sample(position_list  , label_distribution[i,]$train_count )
    test_poisition1 <- setdiff(position_list , train_position1)
   
    train_position <- c(train_position1 , train_position)
    test_poisition <- c(test_poisition1 , test_position)
   
  }
 
  return(train_position)
 
}