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primaryobjects/wumpus-1.csv

Last active October 12, 2020 21:12
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Playing Wumpus World with supervised learning using AI machine learning in R and caret. http://primaryobjects.github.io/wumpus
Raw
wumpus-1.csv
room_n room_e room_s room_w breeze stench glitter move
unvisited unvisited wall wall false false false north
visited unvisited wall wall false false false east
unvisited unvisited visited wall false false false north
unvisited visited visited wall false false false north
visited unvisited visited wall false false false east
unvisited unvisited visited wall true false false south
unvisited unvisited visited wall false true false south
unvisited unvisited visited wall true true false south
unvisited unvisited visited wall false false true north
visited unvisited visited wall false false true east
wall unvisited unvisited unvisited false false false east
wall visited visited visited false false false east
wall wall visited visited false false false south
wall visited visited wall false false false east
wall unvisited visited wall false false false east
wall visited unvisited wall false false false south
unvisited unvisited unvisited unvisited false false false north
visited unvisited unvisited unvisited false false false east
visited unvisited unvisited unvisited true false false north
visited unvisited unvisited unvisited false true false north
visited unvisited unvisited unvisited true true false north
unvisited visited unvisited unvisited true false false east
unvisited visited unvisited unvisited false true false east
unvisited visited unvisited unvisited true true false east
unvisited unvisited visited unvisited true false false south
unvisited unvisited visited unvisited false true false south
unvisited unvisited visited unvisited true true false south
unvisited unvisited unvisited visited true false false west
unvisited unvisited unvisited visited false true false west
unvisited unvisited unvisited visited true true false west
visited visited unvisited unvisited true false false south
visited visited unvisited unvisited false true false south
visited visited unvisited unvisited true true false south
unvisited visited visited unvisited true false false north
unvisited visited visited unvisited false true false north
unvisited visited visited unvisited true true false north
unvisited unvisited visited visited true false false north
unvisited unvisited visited visited false true false north
unvisited unvisited visited visited true true false north
visited unvisited unvisited visited true false false north
visited unvisited unvisited visited false true false north
visited unvisited unvisited visited true true false north
unvisited visited unvisited visited true false false east
unvisited visited unvisited visited false true false east
unvisited visited unvisited visited true true false east
visited visited visited unvisited false false false west
visited unvisited visited visited false false false east
visited visited unvisited visited false false false south
visited unvisited unvisited unvisited false false true east
visited visited unvisited unvisited false false true south
visited visited visited unvisited false false true west
visited visited unvisited visited false false true south
unvisited visited visited visited false false true north
unvisited unvisited visited unvisited false true true south
visited visited wall visited false false false north
unvisited unvisited visited wall false false true east
unvisited unvisited visited unvisited true true false south
unvisited wall wall visited false false false north
unvisited wall visited unvisited true false false south
visited wall visited unvisited false false true west
unvisited visited unvisited unvisited false false true north
wall unvisited visited unvisited false false true west
unvisited unvisited unvisited visited false true false west
unvisited unvisited unvisited visited false false false east
unvisited wall visited unvisited false false false west
wall wall visited unvisited false false true west
wall wall visited unvisited false false false west
visited wall wall unvisited false false false south
Raw
wumpus.R
library(caret)
# function to set up random seeds
setSeeds <- function(method = "cv", numbers = 1, repeats = 1, tunes = NULL, seed = 1237) {
#B is the number of resamples and integer vector of M (numbers + tune length if any)
B <- if (method == "cv") numbers
else if(method == "repeatedcv") numbers * repeats
else NULL
if(is.null(length)) {
seeds <- NULL
} else {
set.seed(seed = seed)
seeds <- vector(mode = "list", length = B)
seeds <- lapply(seeds, function(x) sample.int(n = 1000000, size = numbers + ifelse(is.null(tunes), 0, tunes)))
seeds[[length(seeds) + 1]] <- sample.int(n = 1000000, size = 1)
}
# return seeds
seeds
}
seed <- 1002
number <- 4
method <- 'cv'
cvSeeds <- setSeeds(method = method, numbers = number, tunes = 3, seed = seed)
# Control list for model training.
myControl <- trainControl(method = method, number = number, seeds = cvSeeds)
# Split the data into a training/test set by 70% training/30% test.
data <- read.csv('wumpus-1.csv')
inTrain <- createDataPartition(y = data$move, p=0.7, list=FALSE)
training <- data[inTrain,]
test <- data[-inTrain,]
# SVM: Correct: 6/19 = 32%
fit <- train(move ~ ., data = training, method = 'svmRadial', trControl = myControl)
results <- predict(fit, newdata = test)
table(results, test$move)
data.frame(test, results, test$move == results)
print(paste0('Correct: ', length(which(test$move == results)), '/', nrow(test), ' = ', round(length(which(test$move == results)) / nrow(test) * 100), '%'))
# Logistic regression: Correct: 8/19 = 42%
fit <- train(move ~ ., data = training, method = 'multinom', trControl = myControl)
results <- predict(fit, newdata = test)
table(results, test$move)
data.frame(test, results, test$move == results)
print(paste0('Correct: ', length(which(test$move == results)), '/', nrow(test), ' = ', round(length(which(test$move == results)) / nrow(test) * 100), '%'))
# K-nearest-neighbors: Correct: 10/19 = 53%
fit <- train(move ~ ., data = training, method = 'knn', trControl = myControl)
results <- predict(fit, newdata = test)
table(results, test$move)
data.frame(test, results, test$move == results)
print(paste0('Correct: ', length(which(test$move == results)), '/', nrow(test), ' = ', round(length(which(test$move == results)) / nrow(test) * 100), '%'))
# Random Forest: Correct: 16/19 = 84%
fit <- train(move ~ ., data = training, method = 'rf', trControl = myControl)
results <- predict(fit, newdata = test)
table(results, test$move)
data.frame(test, results, test$move == results)
print(paste0('Correct: ', length(which(test$move == results)), '/', nrow(test), ' = ', round(length(which(test$move == results)) / nrow(test) * 100), '%'))
# Regularized Random Forest: Correct: 16/19 = 84%
fit <- train(move ~ ., data = training, method = 'RRF', trControl = myControl)
results <- predict(fit, newdata = test)
table(results, test$move)
data.frame(test, results, test$move == results)
print(paste0('Correct: ', length(which(test$move == results)), '/', nrow(test), ' = ', round(length(which(test$move == results)) / nrow(test) * 100), '%'))
# Finally, train a model on the entire data-set so we can play the game: Correct: 67/68 = 99%
fit <- train(move ~ ., data = data, method = 'rf')
results <- predict(fit, newdata = data)
table(results, data$move)
data.frame(data, results, data$move == results)
print(paste0('Correct: ', length(which(data$move == results)), '/', nrow(data), ' = ', round(length(which(data$move == results)) / nrow(data) * 100), '%'))
# Manual test.
custom <- data.frame(room_n='visited', room_e='visited', room_s='visited', room_w='unvisited', breeze='false', stench='false', glitter='true')
predict(fit, newdata=custom)
# ^^ west (unvisited room)
# Actual play of the game.
custom <- data.frame(room_n='unvisited', room_e='unvisited', room_s='wall', room_w='wall', breeze='false', stench='false', glitter='false')
predict(fit, newdata=custom)
custom <- data.frame(room_n='unvisited', room_e='unvisited', room_s='visited', room_w='wall', breeze='true', stench='false', glitter='false')
predict(fit, newdata=custom)
custom <- data.frame(room_n='visited', room_e='unvisited', room_s='wall', room_w='wall', breeze='false', stench='false', glitter='false')
predict(fit, newdata=custom)
custom <- data.frame(room_n='unvisited', room_e='unvisited', room_s='wall', room_w='visited', breeze='true', stench='false', glitter='false')
predict(fit, newdata=custom)
# Example run of actual game from starting position.
# > custom <- data.frame(room_n='unvisited', room_e='unvisited', room_s='wall', room_w='wall', breeze='false', stench='false', glitter='false')
# > predict(fit, newdata=custom)
# [1] north
# Levels: east north south west
# ^^^ This is correct behavior, moving north when no obstacles detected.
# > custom <- data.frame(room_n='unvisited', room_e='unvisited', room_s='visited', room_w='wall', breeze='true', stench='false', glitter='false')
# > predict(fit, newdata=custom)
# [1] south
# Levels: east north south west
# ^^^ This is correct behavior, moving back to the south room since a breeze was detected.
# > custom <- data.frame(room_n='visited', room_e='unvisited', room_s='wall', room_w='wall', breeze='false', stench='false', glitter='false')
# > predict(fit, newdata=custom)
# [1] east
# Levels: east north south west
# ^^^ This is correct behavior, moving east to a newly unvisited room since we've already been north.
# > custom <- data.frame(room_n='unvisited', room_e='unvisited', room_s='wall', room_w='visited', breeze='true', stench='false', glitter='false')
# > predict(fit, newdata=custom)
# [1] west
# Levels: east north south west
# ^^^ This is expected behavior, moving back to the west since a breeze was detected.
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