From 301842723772985e29c3ebd21fd17be030ca098c Mon Sep 17 00:00:00 2001
From: Waldir Leoncio <waldir.leoncio@gmail.com>
Date: Fri, 31 Jul 2020 13:59:11 +0200
Subject: [PATCH] Fixed linkage()

---
 R/linkage.R | 50 +++++++++++++++++++++++++++++++++++---------------
 1 file changed, 35 insertions(+), 15 deletions(-)

diff --git a/R/linkage.R b/R/linkage.R
index 80d6b30..ccaf9b5 100644
--- a/R/linkage.R
+++ b/R/linkage.R
@@ -4,10 +4,14 @@
 #' linkage algorithm.  The input Y is a distance matrix such as is generated by
 #' PDIST. Y may also be a more general dissimilarity matrix conforming to the
 #' output format of PDIST.
-#' @param Y data
+#'
+#' Z = linkage(X) returns a matrix Z that encodes a tree containing hierarchical clusters of the rows of the input data matrix X.
+#' @param Y matrix
 #' @param method either 'si', 'av', 'co' 'ce' or 'wa'
+#' @note This is also a base Matlab function. The reason why the source code is also present here is unclear.
 #' @export
 linkage <- function(Y, method = 'co') {
+	#TODO: compare R output with MATLAB output
 	k <- size(Y)[1]
 	n <- size(Y)[2]
 	m <- (1 + sqrt(1 + 8 * n)) / 2
@@ -24,18 +28,33 @@ linkage <- function(Y, method = 'co') {
 	N[1:m] <- 1
 	n <- m; # since m is changing, we need to save m in n.
 	R <- 1:n
-	for (s in 1:(n-1)) {
-		X <- Y
-		v <- min(X)[1]
-		k <- min(X)[2]
+	for (s in 1:(n - 1)) {
+		X <- as.matrix(as.vector(Y), ncol=1)
+
+		v <- min_MATLAB(X)$mins
+		k <- min_MATLAB(X)$idx
+
 		i <- floor(m + 1 / 2 - sqrt(m ^ 2 - m + 1 / 4 - 2 * (k - 1)))
 		j <- k - (i - 1) * (m - i / 2) + i
-		Z[s, ] <- c(R[i], R[j], v) # update one more row to the output matrix A
-		# Temp variables
-		I1 <- 1:(i - 1)
-		I2 <- (i + 1):(j - 1)
-		I3 <- (j + 1):m
 
+		Z[s, ] <- c(R[i], R[j], v) # update one more row to the output matrix A
+
+		# Temp variables
+		if (i > 1) {
+			I1 <- 1:(i - 1)
+		} else {
+			I1 <- NULL
+		}
+		if (i + 1 <= j - 1) {
+			I2 <- (i + 1):(j - 1)
+		} else {
+			I2 <- NULL
+		}
+		if (j + 1 <= m) {
+			I3 <- (j + 1):m
+		} else {
+			I3 <- NULL
+		}
 		U <- c(I1, I2, I3)
 		I <- c(
 			I1 * (m - (I1 + 1) / 2) - m + i,
@@ -47,11 +66,13 @@ linkage <- function(Y, method = 'co') {
 			I2 * (m - (I2 + 1) / 2) - m + j,
 			j * (m - (j + 1) / 2) - m + I3
 		)
-
+		# Workaround in R for negative values in I and J
+		# I <- I[I > 0 & I <= length(Y)]
+		# J <- J[J > 0 & J <= length(Y)]
 		switch(method,
-			'si' = Y[I] <- min(Y[I], Y[J]), # single linkage
+			'si' = Y[I] <- apply(cbind(Y[I], Y[J]), 1, min), # single linkage
 			'av' = Y[I] <- Y[I] + Y[J], # average linkage
-			'co' = Y[I] <- max(Y[I], Y[J]), #complete linkage
+			'co' = Y[I] <- apply(cbind(Y[I], Y[J]), 1, max), #complete linkage
 			'ce' = {
 				K <- N[R[i]] + N[R[j]] # centroid linkage
 				Y[I] <- (N[R[i]] * Y[I] + N[R[j]] * Y[J] -
@@ -61,8 +82,7 @@ linkage <- function(Y, method = 'co') {
 				Y[J] - N[R[U]] * v) / (N[R[i]] + N[R[j]] + N[R[U]])
 		)
 		J <- c(J, i * (m - (i + 1) / 2) - m + j)
-		Y[J] <- vector() # no need for the cluster information about j
-
+		Y <- Y[-J] # no need for the cluster information about j
 		# update m, N, R
 		m <- m - 1
 		N[n + s] <- N[R[i]] + N[R[j]]