## ----eval=FALSE---------------------------------------------------------------
# for (i in 1:n) {
#   distp[1] <- dist(rbind(p[i, ], data[1, ]))
#   minqi <- dist(rbind(q[i, ], data[1, ]))
#   for (j in 2:nrow(data)) {
#     distp[j] <- dist(rbind(p[i, ], data[j, ]))
#     error <- q[i, ] - data[j, ]
#     if (sum(abs(error)) != 0) {
#       distq <- dist(rbind(q[i, ], data[j, ]))
#       if (distq < minqi)
#         minqi <- distq
#     }
#   }

## ----eval=FALSE---------------------------------------------------------------
# DistVecToMat <- function(x,Y){
#   min(apply(Y, 1, function(yi) sqrt(sum((x-yi)^2))))
# }
# # DistVecToMat(c(1,2), matrix(c(4,5,6,7), nrow=2, byrow=TRUE))
# # 4.242641 7.071068 # sqrt(c(18,50))
# 
# # For each ith row of sample, calculate the distance of:
# #   U[i,] to X
# #   W[i,] to X[-k[i] , ], i.e. omitting W[i,] from X
# dux <- rep(NA, n)
# dwx <- rep(NA, n)
# for(i in 1:n) {
#   dux[i] <- DistVecToMat(U[i,], X)
#   dwx[i] <- DistVecToMat(W[i,], X[-k[i],])
# }

## ----eval=FALSE---------------------------------------------------------------
# # pdist(W,X) computes distance between rows of W and rows of X
# tmp <- as.matrix(pdist(W,X))
# dwx <- apply(tmp, 1, min)
# 
# # pdist(U,X) computes distance between rows of U and rows of X
# tmp <- as.matrix(pdist(U,X))
# dux <- apply(tmp, 1, min)

## ----eval=FALSE---------------------------------------------------------------
# library(microbenchmark)
# 
# # Method 1. Loop over vector 1:n
# # for(i in 1:m) dwx[i,k[i]] <- Inf
# microbenchmark(hopkins(X12, 500))
# ## Unit: milliseconds
# ##                expr     min      lq     mean  median       uq      max neval
# ##  hopkins(X12, 500) 38.9493 42.8045 50.73876 45.0668 47.69945 120.9366   100
# 
# # Method 2. Build a matrix of indexes to the cells that need changing
# # dwx[ matrix( c(1:n, k), nrow=n) ] <- Inf
# microbenchmark(hopkins(X12, 500))
# ## Unit: milliseconds
# ##                expr     min       lq     mean   median      uq      max neval
# ##  hopkins(X12, 500) 39.2668 42.41565 50.21628 43.74215 46.9462 126.3522   100

## ----eval=FALSE, echo=FALSE---------------------------------------------------
# set.seed(1)
# X1 <- matrix(rnorm(1000, mean=0, sd=1), ncol=2)
# X2 <- matrix(rnorm(1000, mean=5, sd=1), ncol=2)
# X12 <- rbind(X1, X2)
# plot(X12)
# 
# # Old version
# set.seed(42)
# system.time( hop1 <- clustertend::hopkins(X12, 500) ) # 17 sec
# 1-hop1$H
# # .8994528
# 
# # New version
# set.seed(42)
# system.time( hop2 <- hopkins::hopkins(X12, 500, d=1) ) # .05 sec
# hop2
# # .9054555