# NOT RUN {
# x is a character vector.
full.tt("A + B*c")
full.tt("A=1*C=3 + B=2*C=1 + A=3*B=1")
full.tt(c("A + b*C", "a*D"))
full.tt("!A*-(B + c) + F")
# x is a data frame.
full.tt(d.educate)
full.tt(d.jobsecurity, type = "fs")
full.tt(d.pban, type = "mv")
# x is a truthTab.
full.tt(cstt(d.educate))
full.tt(fstt(d.jobsecurity))
full.tt(mvtt(d.pban))
# x is an integer.
full.tt(6)
# x is a list.
full.tt(list(A = 0:1, B = 0:1, C = 0:1)) # cs
full.tt(list(A = 1:2, B = 0:1, C = 1:4)) # mv
# Simulating crisp-set data.
groundTruth.1 <- "(A*b + C*d <-> E)*(E*H + I*k <-> F)"
fullData <- full.tt(groundTruth.1)
idealData <- selectCases(groundTruth.1, fullData)
# Introduce 20% data fragmentation.
fragData <- idealData[-sample(1:nrow(idealData), nrow(idealData)*0.2), ]
# Introduce 10% random noise.
realData <- rbind(tt2df(fullData[sample(1:nrow(fullData), nrow(fragData)*0.1), ]), fragData)
# Simulating multi-value data.
# }
# NOT RUN {
groundTruth.2 <- "(JO=3 + TS=1*PE=3 <-> ES=1)*(ES=1*HI=4 + IQ=2*KT=5 <-> FA=1)"
fullData <- full.tt(list(JO=1:3, TS=1:2, PE=1:3, ES=1:2, HI=1:4, IQ=1:5, KT=1:5, FA=1:2))
idealData <- selectCases(groundTruth.2, fullData)
# Introduce 20% data fragmentation.
fragData <- idealData[-sample(1:nrow(idealData), nrow(idealData)*0.2), ]
# Introduce 10% random noise.
realData <- rbind(tt2df(fullData[sample(1:nrow(fullData), nrow(fragData)*0.1), ]), fragData)
# }
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