Read similarity or dissimilarity input data

Description

Read the similarity (or dissimilarity) information between the objects that one wants to order and build a 3-columns data frame, where each row (i, j, A_{ij}) represents the (dis)similarity A_{ij} between objects i and j. In case of symmetric data (i.e., A_{ij} = A_{ji}), only the entries for pairs (i,j) with i<j are listed.

Usage

read(data, zero_epsilon = 1e-200, symmetric = TRUE, identical_val = FALSE)

Arguments

Details

The input data can be a weighted adjacency matrix (represented by the objects: matrix, dist or data frame), or a list of all the weighted edges of a weighted graph (represented by a 3-col data frame) where each row (i, j, A_{ij}) represents the (dis)similarity A_{ij} between objects i and j with i<j). If not specified, the data is assumed to be symmetric (i.e., same entry at positions (i,j) and (j,i)). Since by default the data is assumed to be symmetric, if it is represented by a 3-columns data frame, then it is assumed that symmetric pairs are not listed, and thus by default identical_val = FALSE. The reason for this choice is that for large symmetric data, it is more efficient to list the symmetric entries only once. However, note that if symmetric = FALSE then identical_val = TRUE automatically.

Value

Returns a 3-columns data frame representation of the original data listing all the pairwise (dis)similarities (i, j, A_{ij}) between objects and selecting only the entries A_{ij} with i<j when the data is a symmetric matrix A.

Author(s)

Matteo Seminaroti (SFS) and Utz-Uwe Haus (R wrapping)

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

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)