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Kaggle > Notebook

My Black Jack Project

21

The movie <21>(2008) has accidentally nspired me to dream the king of Las Vegas.

<List>

1. Set Initial Cards

1.1 Trial 1 - Generate the Entire Initial Cases (2022.08.16)

  • I tried generating the initial cases that each of the player and dealer has 2 cards respectively.
  • But the number of cases are over 6 million …… and the data size is about 200 Mb. It may seem not so large, but I was going to adopt some additional data structure to calculate the probability table for each case, so I think it is needed to find more efficient data structure & algorithm …… For example, a Monte Carlo simulation with recursive search?

BlackJack.r

……

# 1.1 Trial 1 : Generate the entire initial cases

# loading gtools library
if (!requireNamespace("gtools")) {
  install.packages('gtools')
}
library(gtools)                                             # for using permutations() and combinations()

cards <- read.csv("Cards.csv")
nrow(cards)                                                 # 52

init <- permutations(52, 4, v = cards[,1], repeats.allowed = FALSE)
head(init)
nrow(init)                                                  # 6,497,400 cases, 198.3 Mb

[1] 52

     [,1] [,2] [,3] [,4]
[1,] "C10" "C2" "C3" "C4"
[2,] "C10" "C2" "C3" "C5"
[3,] "C10" "C2" "C3" "C6"
[4,] "C10" "C2" "C3" "C7"
[5,] "C10" "C2" "C3" "C8"
[6,] "C10" "C2" "C3" "C9"

[1] 6497400

0. Generate Cards' Data (2022.08.06)

  • Why I chose R for doing it? Because …… I love a:b syntax for generating continuous integers.
  • After made Cards.csv, maybe it will be better to use Python for operating GAN and so on.

BlackJack.r

# 0.1 Make as a vector

suits <- c("S", "H", "D", "C")
num <- c(1:10, 10, 10, 10)

cards <- c()
for (s in suits) {
    for (n in num) {
        cards <- c(cards, paste(s, n, sep=""))
    }
}

cards
cards[11]                                                   # S10
substr(cards[11], 2, 3)                                     #  10
substr(cards[9], 2, 3)                                      #   9

[1] "S1" "S2" "S3" "S4" "S5" "S6" "S7" "S8" "S9" "S10" "S10"
[12] "S10" "S10" "H1" "H2" "H3" "H4" "H5" "H6" "H7" "H8" "H9"
……
[45] "C6" "C7" "C8" "C9" "C10" "C10" "C10" "C10"

[1] "S10"
[1] "10"
[1] "9"

# 0.2 Make as 2-dim dataframe

suits <- c("S", "H", "D", "C")
num <- c("A", 2:10, "J", "Q", "K")
points <- c(1:10, 10, 10, 10)

cards <- c()
for (s in suits) {
    for (n in num) {
        cards <- c(cards, paste(s, n, sep = ""))
    }
}

cards2d <- data.frame(cards, points)
# 0.3 Export to .csv

write.csv(cards2d, "Cards.csv", row.names = FALSE)

Cards.csv

"cards","points"
"SA",1
"S2",2
"S3",3
……