# You can write R code here and then click "Run" to run it on our platform

# setwd("D:\\Kaggel\\MINT")

require("readr")||install.packages("readr")||library(readr)

# The competition datafiles are in the directory ../input
train <- read.csv("../input/train.csv")
test <- read_csv("../input/test.csv")


require('FNN')||install.packages('FNN');library(FNN)
require('caret')||install.packages('caret');library(caret)


set.seed(1234)

# Splitting the train data into 50:50 
sample_index = createDataPartition(train$label, p = .50, list = FALSE, times = 1)

# Dimension of the train & test
dim(train)
dim(test)

# KNN method at k = 5
pc <- proc.time()




fnn.kd <- knn(train[,-1], test, train$label , k=5 , algorithm = c("kd_tree"))
proc.time() - pc


submission <- data.frame(ImageId=1:nrow(test), Label=fnn.kd)
head(submission)

write_csv(submission, "KNN_benchmark.csv") 


split_train <- split(train, train$label, drop = FALSE)


head(split_train)

t0 = split_train$`0`

matrix(t0[1,2:785],28,28)