#SOSCC algorithm
#George M. Garrity and Timothy G. Lilburn
#Copyright Michigan State University 2004, all rights reserved

"step9" = function(temp, tax.table)
{
	print("inside step9")
	temp.order <- matrix(0, nrow(temp), ncol(temp))
	while (TRUE) {
		temp <- abs(jitter(temp, factor = 0.001))
		int <- F
		for (j in 1:nrow(temp)) {
			temp.order[j,] <- rank(as.matrix(temp[j,  ]))
			tf <- unique(as.integer(as.vector(temp.order[j,])) == as.vector(temp.order[j,]))
			print(tf)
			if (length(tf)==1) {
				int <- T
			}
			else {
				int <- F
			}	
		}
		if (int) break
	}
	
	dimnames(temp.order)[[2]] <- dimnames(temp)[[2]]
	dimnames(temp.order)[[1]] <- dimnames(temp)[[1]]
	temp.reorder <- NULL
	for(j in 1:ncol(temp.order)) {
		temp.reorder <- c(temp.reorder, dimnames(temp.order)[[1]][temp.order[, j] == 1])
	}

	#identify any data that would have been dropped by a tie
	dropped.names <- setdiff(dimnames(temp)[[1]], temp.reorder)
	temp <- temp[c(temp.reorder, dropped.names),  ]
	###NOTE - This step can be problematic and may require manual intervention
	best.match <- NULL
	for(i in 1:nrow(temp.order)) {
		best.match <- c(best.match, dimnames(temp.order)[[2]][temp.order[i,  ] == 1])
	}
	
	nearest.neighbors <- cbind(dimnames(temp.order)[[1]], tax.table[best.match, c("species", "family")])
	nearest.neighbors <- cbind(nearest.neighbors, substring(as.character(nearest.neighbors[, 2]), 1, regexpr("[^a-zA-Z]", as.character(nearest.neighbors[, 2])) - 1))
	dimnames(nearest.neighbors)[[2]][2:3] <- c("MPI.species", "MPI.family")
	nearest.neighbors <- cbind(tax.table[as.character(nearest.neighbors[, 1]),  c("species", "family", "taxon.seq")], nearest.neighbors[, 2:3])
	return(nearest.neighbors)
}