| SFS_sfs {SFS} | R Documentation |
Similarity-First Search multisweep algorithm
Description
Return a ranking of the objects such that similar objects are ordered close to each other. If the matrix is Robinsonian, then the ranking returned is a Robinson ordering.
Usage
sfs(matrix, sfs_epsilon = 0, dissimilarity = FALSE, Robinsonian = FALSE, num_sweeps = 4)
Arguments
matrix |
a 3-columns |
sfs_epsilon |
a numerical value which determines that two entries whose difference is below this threshold are considered to be equal. |
dissimilarity |
a boolean value equal to |
Robinsonian |
a boolean value equal to |
num_sweeps |
an integer value that determines how many iterations of SFS shall be performed. |
Details
Given a a 3-columns data frame (i, j, A_{ij}) listing all the similarities (or dissimilarities) among the objects, this function builds a spMat object in Armadillo and computes a finite number of repeated SFS iterations (each called a sweep). The user may decide the threshold for which two entries are considered equal, meaning that if |A_{ij} - A_{ik}| \leq sfs_epsilon, then objects j and k have the same similarity (or dissimilarity) with respect to object i. By default, this threshold is set to 0.
If not specified, the matrix represents the similarity information between objects. If dissimilarity = TRUE, then the matrix represents the dissimilarity information and the SFS algorithm is modified by sorting the neighborhood of a visited vertex for increasing values (instead of for decreasing values).
The parameter k=num_sweeps sets the number of sweeps performed by SFS(). This number directly affects the complexity of the function since, as each sweep runs in (k(n+m\log n)) time, SFS() runs in (k(n+m \log n)) time. By default, num_sweeps=4, as it is known that three sweeps suffice for recognizing Robinonian binary matrices and for general matrices experiments show that four sweeps are enough for finding a good ranking for most data. If Robinsonian = TRUE, then the number of sweeps is automatically set to the number of objects n to rank minus one. In this case, sfs() also checks if the returned permutation is a Robinson ordering (since it is known that if the order returned after n-1 sweeps is not a Robinson ordering then the data is not Robinsonian). Efficient measures are implemented in order to avoid unnecessary time consuming loops between consecutive SFS iterations. Note that checking if a given permutation is a Robinson ordering is implemented at the moment only when dealing with similarities among the objects.
Finally, the object returned by SFS() is a vector of integers, where the entry at position i represents the i-th object in the ranking. If the matrix is 0-based, then the returned vector is 0-based. If matrix is 1-based, then the returned vector is 1-based.
Value
Return a (row) vector of integers representing the ranking of the objects, which is 0-based or 1-based accordingly with the input matrix.
Author(s)
Matteo Seminaroti (SFS) and Utz-Uwe Haus (R wrapping)
References
M. Laurent and M. Seminaroti. Similarity-First Search: a new algorithm with application to Robinsonian matrix recognition. SIAM Journal on Discrete Mathematics (to appear). arXiv:1601.03521. 2016.
M. Seminaroti. Combinatorial Algorithms for the Seriation Problem. PhD thesis. School of Economics and Management, Tilburg University, pages 1–209. 2016.
Examples
## install package in R
#install.packages("SFS_0.1.tar.gz")
#install.packages("seriation")
## load package in R
library(SFS)
## invoke SFS on a R Matrix
mtxt <- c("11 2 9 0 5 0 5 5 2 0 5 0 5 6 0 0 2 0 5",
"2 11 2 0 9 0 8 5 10 0 5 0 5 2 0 0 10 0 8",
"9 2 11 0 5 0 5 5 2 0 5 0 5 10 0 0 2 0 5",
"0 0 0 11 0 3 0 0 0 3 0 3 0 0 10 3 0 9 0",
"5 9 5 0 11 0 8 7 9 0 7 0 7 5 0 0 9 0 10",
"0 0 0 3 0 11 0 0 0 10 0 6 0 0 5 8 0 5 0",
"5 8 5 0 8 0 11 7 8 0 7 0 7 5 0 0 8 0 9",
"5 5 5 0 7 0 7 11 6 0 10 0 8 7 0 0 6 0 7",
"2 10 2 0 9 0 8 6 11 0 6 0 5 2 0 0 10 0 8",
"0 0 0 3 0 10 0 0 0 11 0 6 0 0 4 9 0 5 0",
"5 5 5 0 7 0 7 10 6 0 11 0 9 7 0 0 6 0 7",
"0 0 0 3 0 6 0 0 0 6 0 11 0 0 9 6 0 10 0",
"5 5 5 0 7 0 7 8 5 0 9 0 11 7 0 0 5 0 7",
"6 2 10 0 5 0 5 7 2 0 7 0 7 11 0 0 2 0 5",
"0 0 0 10 0 5 0 0 0 4 0 9 0 0 11 4 0 10 0",
"0 0 0 3 0 8 0 0 0 9 0 6 0 0 4 11 0 4 0",
"2 10 2 0 9 0 8 6 10 0 6 0 5 2 0 0 11 0 8",
"0 0 0 9 0 5 0 0 0 5 0 10 0 0 10 4 0 11 0",
"5 8 5 0 10 0 9 7 8 0 7 0 7 5 0 0 8 0 11")
M <- as.matrix(read.table(textConnection(mtxt)))
A <- SFS::read(M)
SFS::sfs(A, Robinsonian = TRUE)
## invoke SFS on a data-frame with 3-column data-frames with 0-based vertices, with
## (row, col, value) triples containing symmetric values
data <- c("0 0 1.0",
"1 0 1.5",
"2 0 1.9",
"0 1 2.0",
"1 1 2.5",
"2 1 2.9",
"0 2 3.0",
"1 2 3.5",
"2 2 3.9")
M <- read.table(textConnection(data))
A <- SFS::read(M, identical_val = TRUE)
SFS::sfs(A)
## invoke SFS on a \code{dist} from seriation package:
library(seriation)
data("iris");
x <- as.matrix(iris[-5]);
x <- x[sample(1:nrow(x)),];
M <- dist(x)
D <- SFS::read(M)
SFS::sfs(D, sfs_epsilon = 0.01, dissimilarity = TRUE)
## invoke SFS reading from file a Robinsonian matrix
M <- read.table(system.file("extdata", "list_130.txt", package = "SFS"))
A <- SFS::read(M, identical_val = TRUE)
SFS::sfs(A, Robinsonian = TRUE)
## invoke SFS reading from file containing 3-columns (row, col, value) entries
## of an asymmetric non-Robinsonian similarity matrix with 1-based vertices
M <- read.table(system.file("extdata", "list_130.txt", package = "SFS"))
A <- SFS::read(M, identical_val = TRUE, symmetric = FALSE)
SFS::sfs(A, num_sweeps = 7)