R/write_constraints_objFunction_all.R

In CARNIVAL: A CAusal Reasoning tool for Network Identification (from gene expression data) using Integer VALue programming

## This function returns the list of constraints associated with the 'Absolute 
## Difference' variables and which measure the mis-fit between inferred and 
## measured data.
## 
## Enio Gjerga, 2020

write_constraints_objFunction_all <- function(variables=variables, 
                                              dataMatrix=dataMatrix) {

  ## ============================================ ##
  ## === Load write_constraints_objFunction.R === ##
  ## ============================================ ##

  write_constraints_objFunction <- function(variables=variables, 
                                            dataMatrix=dataMatrix, 
                                            conditionIDX=conditionIDX){

    measurements <- as.vector(t(dataMatrix$dataMatrixSign))

    idx2 <- which(measurements==1)
    idx3 <- which(measurements==-1)

    cc1 <- rep("", length(measurements))
    cc2 <- rep("", length(measurements))

    cc1[idx2] <- paste0(variables$variables[idx2], " - absDiff", 
                        idx2, "_", conditionIDX, " <= 1")
    cc2[idx2] <- paste0(variables$variables[idx2], " + absDiff", idx2, "_", 
                        conditionIDX, " >= 1")

    cc1[idx3] <- paste0(variables$variables[idx3], " - absDiff", idx3, "_", 
                        conditionIDX, " <= -1")
    cc2[idx3] <- paste0(variables$variables[idx3], " + absDiff", idx3, "_", 
                        conditionIDX, " >= -1")

    constraints0 <- c(cc1, cc2)

    return(constraints0[-which(constraints0=="")])

  }

  constraints0 <- c()

  for (i in seq_len(nrow(dataMatrix$dataMatrix))) {

    dM <- dataMatrix
    dM$dataMatrix <- as.matrix(t(dataMatrix$dataMatrix[i, ]))
    dM$dataMatrixSign <- as.matrix(t(dataMatrix$dataMatrixSign[i, ]))

    var <- variables[[i]]

    constraints0 <- write_constraints_objFunction(variables = var, 
                                                  dataMatrix = dM, 
                                                  conditionIDX = 1)

  }

  return(constraints0)

}