Running ‘AHP.R’
  Running ‘LPDMRSort.R’
  Running ‘LPDMRSortIdentifyIncompatibleAssignments.R’
  Running ‘LPDMRSortIdentifyUsedDictatorProfiles.R’
  Running ‘LPDMRSortIdentifyUsedVetoProfiles.R’
  Running ‘LPDMRSortInferenceApprox.R’
  Running ‘LPDMRSortInferenceExact.R’
  Running ‘MARE.R’
  Running ‘MRSort.R’
  Running ‘MRSortIdentifyIncompatibleAssignments.R’
  Running ‘MRSortIdentifyUsedVetoProfiles.R’
  Running ‘MRSortInferenceApprox.R’
  Running ‘MRSortInferenceExact.R’
  Running ‘SRMP.R’
  Running ‘SRMPInference.R’
  Running ‘SRMPInferenceApprox.R’ [53s/61s]
  Running ‘SRMPInferenceApproxFixedLexicographicOrder.R’ [17s/20s]
  Running ‘SRMPInferenceApproxFixedProfilesNumber.R’ [56s/63s]
  Running ‘SRMPInferenceFixedLexicographicOrder.R’
  Running ‘SRMPInferenceFixedProfilesNumber.R’
  Running ‘SRMPInferenceNoInconsist.R’
  Running ‘SRMPInferenceNoInconsistFixedLexicographicOrder.R’
  Running ‘SRMPInferenceNoInconsistFixedProfilesNumber.R’
  Running ‘TOPSIS.R’
  Running ‘UTA.R’
  Running ‘UTADIS.R’
  Running ‘UTASTAR.R’
  Running ‘additiveValueFunctionElicitation.R’
  Running ‘applyPiecewiseLinearValueFunctionsOnPerformanceTable.R’
  Running ‘assignAlternativesToCategoriesByThresholds.R’
  Running ‘normalizePerformanceTable.R’
  Running ‘pairwiseConsistencyMeasures.R’
  Running ‘plotAlternativesValuesPreorder.R’
  Running ‘plotMRSortSortingProblem.R’
  Running ‘plotRadarPerformanceTable.R’
  Running ‘weightedSum.R’
Running the tests in ‘tests/SRMPInferenceApprox.R’ failed.
Complete output:
  > # ranking some students
  > 
  > library(MCDA)
  > 
  > # the performance table
  > 
  > performanceTable <- rbind(c(10,10,9),c(10,9,10),c(9,10,10),c(9,9,10),c(9,10,9),c(10,9,9),
  +                           c(10,10,7),c(10,7,10),c(7,10,10),c(9,9,17),c(9,17,9),c(17,9,9),
  +                           c(7,10,17),c(10,17,7),c(17,7,10),c(7,17,10),c(17,10,7),c(10,7,17),
  +                           c(7,9,17),c(9,17,7),c(17,7,9),c(7,17,9),c(17,9,7),c(9,7,17))
  > 
  > criteriaMinMax <- c("max","max","max")
  > 
  > rownames(performanceTable) <- c("a1","a2","a3","a4","a5","a6","a7","a8","a9","a10","a11","a12","a13","a14","a15","a16","a17","a18","a19","a20","a21","a22","a23","a24")
  > 
  > colnames(performanceTable) <- c("c1","c2","c3")
  > 
  > names(criteriaMinMax) <- colnames(performanceTable)
  > 
  > # expected result for the tests below
  > 
  > expectedValues <- c(10,7,13,3,5,1,10,7,13,4,6,2,14,12,8,15,11,9,4,6,2,6,2,4)
  > 
  > names(expectedValues) <- rownames(performanceTable)
  > 
  > expectedValues <- expectedValues[c("a1","a3","a7","a9","a13","a14","a15","a16","a17","a18")]
  > 
  > expectedValues <- expectedValues - min(expectedValues) + 1
  > 
  > # test - preferences and indifferences
  > 
  > preferencePairs <- matrix(c("a16","a13","a3","a14","a17","a1","a18","a15","a2","a11","a5","a10","a4","a12",
  +                             "a13","a3","a14","a17","a1","a18","a15","a2","a11","a5","a10","a4","a12","a6"),14,2)
  > indifferencePairs <- matrix(c("a3","a1","a2","a11","a11","a20","a10","a10","a19","a12","a12","a21",
  +                               "a9","a7","a8","a20","a22","a22","a19","a24","a24","a21","a23","a23"),12,2)
  > 
  > set.seed(1)
  > 
  > result<-SRMPInferenceApprox(performanceTable, criteriaMinMax, 3, preferencePairs, indifferencePairs, alternativesIDs = c("a1","a3","a7","a9","a13","a14","a15","a16","a17","a18"))
  [1] "Best fitness so far:  77.78%"
  [1] "Best fitness so far:  77.78%"
  [1] "Best fitness so far:  77.78%"
  [1] "Best fitness so far:  77.78%"
  [1] "Best fitness so far:  88.89%"
  [1] "Best fitness so far:  88.89%"
  [1] "Best fitness so far:  88.89%"
  [1] "Best fitness so far:  88.89%"
  [1] "Best fitness so far:  88.89%"
  [1] "Best fitness so far:  88.89%"
  [1] "Best fitness so far:  88.89%"
  [1] "Best fitness so far:  88.89%"
  [1] "Best fitness so far:  88.89%"
  [1] "Final model fitness:  88.89%"
  > 
  > alternativesValues<-SRMP(performanceTable, result$referenceProfiles, result$lexicographicOrder, result$criteriaWeights, criteriaMinMax, alternativesIDs = c("a1","a3","a7","a9","a13","a14","a15","a16","a17","a18"))
  > 
  > stopifnot(all(alternativesValues == expectedValues))
  Error: all(alternativesValues == expectedValues) is not TRUE
  Execution halted
Running the tests in ‘tests/SRMPInferenceApproxFixedProfilesNumber.R’ failed.
Complete output:
  > # ranking some students
  > 
  > library(MCDA)
  > 
  > # the performance table
  > 
  > performanceTable <- rbind(c(10,10,9),c(10,9,10),c(9,10,10),c(9,9,10),c(9,10,9),c(10,9,9),
  +                           c(10,10,7),c(10,7,10),c(7,10,10),c(9,9,17),c(9,17,9),c(17,9,9),
  +                           c(7,10,17),c(10,17,7),c(17,7,10),c(7,17,10),c(17,10,7),c(10,7,17),
  +                           c(7,9,17),c(9,17,7),c(17,7,9),c(7,17,9),c(17,9,7),c(9,7,17))
  > 
  > criteriaMinMax <- c("max","max","max")
  > 
  > rownames(performanceTable) <- c("a1","a2","a3","a4","a5","a6","a7","a8","a9","a10","a11","a12","a13","a14","a15","a16","a17","a18","a19","a20","a21","a22","a23","a24")
  > 
  > colnames(performanceTable) <- c("c1","c2","c3")
  > 
  > names(criteriaMinMax) <- colnames(performanceTable)
  > 
  > # expected result for the tests below
  > 
  > expectedValues <- c(10,7,13,3,5,1,10,7,13,4,6,2,14,12,8,15,11,9,4,6,2,6,2,4)
  > 
  > names(expectedValues) <- rownames(performanceTable)
  > 
  > altIDs <- c("a1","a3","a7","a9","a13","a14","a15","a16","a17","a18")
  > 
  > expectedValues <- expectedValues[altIDs]
  > 
  > expectedValues <- expectedValues - min(expectedValues) + 1
  > 
  > # test - preferences and indifferences
  > 
  > preferencePairs <- matrix(c("a16","a13","a3","a14","a17","a1","a18","a15","a2","a11","a5","a10","a4","a12",
  +                             "a13","a3","a14","a17","a1","a18","a15","a2","a11","a5","a10","a4","a12","a6"),14,2)
  > indifferencePairs <- matrix(c("a3","a1","a2","a11","a11","a20","a10","a10","a19","a12","a12","a21",
  +                               "a9","a7","a8","a20","a22","a22","a19","a24","a24","a21","a23","a23"),12,2)
  > 
  > set.seed(1)
  > 
  > result<-SRMPInferenceApproxFixedProfilesNumber(performanceTable, criteriaMinMax, 3, preferencePairs, indifferencePairs, alternativesIDs = altIDs)
  [1] "Best fitness so far:  77.78%"
  [1] "Best fitness so far:  77.78%"
  [1] "Best fitness so far:  77.78%"
  [1] "Best fitness so far:  77.78%"
  [1] "Best fitness so far:  77.78%"
  [1] "Best fitness so far:  77.78%"
  [1] "Best fitness so far:  77.78%"
  [1] "Best fitness so far:  77.78%"
  [1] "Best fitness so far:  77.78%"
  [1] "Best fitness so far:  77.78%"
  [1] "Best fitness so far:  77.78%"
  [1] "Best fitness so far:  77.78%"
  [1] "Best fitness so far:  77.78%"
  [1] "Final model fitness:  77.78%"
  > 
  > alternativesValues<-SRMP(performanceTable, result$referenceProfiles, result$lexicographicOrder, result$criteriaWeights, criteriaMinMax, alternativesIDs = altIDs)
  > 
  > stopifnot(all(alternativesValues == expectedValues))
  Error: all(alternativesValues == expectedValues) is not TRUE
  Execution halted