Nothing

```
baysout <-
function (D, blocks = 10, nclass=0, k = 3, num.out = 10)
{#blocks must be greater or equal than num.out
# require(FNN)
if (sum(is.na(D))> 0)
stop("This dataset has missing values, impute them before running this function.\n",call.=FALSE)
big = 1e+06
# print(dim(D))
p=dim(D)[2]
if(nclass==0)
D = as.data.frame(D[,-p])
else D=D[as.numeric(factor(D[,p]))==nclass,-p ]
# print(dim(D))
nrows = dim(D)[1]
brows = dim(D)[1]
c = 0
Out = NULL
#number of blocks
repet = ceiling(nrows/blocks)
for (cycle in 1:repet) {
# print(cycle)
block.size = blocks
# print(block.size)
if (block.size * cycle <= brows)
block = (block.size * (cycle - 1) + 1):(block.size *
cycle)
else {
block = (block.size * (cycle - 1) + 1):brows
block.size = length(block)
}
# print(block)
#The block
B = D[block, ]
#initializing the matrix of distane of the neighbors
neighbors = matrix(rep(big, (block.size * k)), block.size,
k)
# print(neighbors)
rownames(neighbors) = rownames(B)
neighbors = as.data.frame(neighbors)
flag = 0
reduce = 0
removeB = rep(0, 0)
removeN = rep(0, 0)
for (j in 1:block.size) {
neighbors[j,]=FNN::knnx.dist(D[-as.integer(rownames(B))[j],],B[j,],k)
# cat("c")
# print(c)
# cat("score")
# print(score(neighbors[j,]))
if (score(neighbors[j, ]) < c) {
removeB = cbind(removeB, j)
removeN = cbind(removeN, j)
reduce = reduce + 1
}
}
if (reduce == dim(B)[1])
flag = 1
else if (reduce != 0) {
block.size = block.size - reduce
B = B[-removeB, ]
neighbors = neighbors[-removeN, ]
}
#print(neighbors)
if (flag == 0) {
#print(neighbors)
Out = top(Out, neighbors, num.out)
# print(Out)
c = min(Out)
}
#print(c)
}#end of loop for blocks
xcoord = as.integer(rownames(Out))
plot(Out, main = "Instances with Greatest score from K nearest neighbors",
ylab = "Sum of Distances")
text(1:num.out, Out, rownames(Out), cex = 0.6, pos = 4)
return(Out)
}
```

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