PI chart functions: makeChart, solveChart: Create and solve a prime implicants chart

Description

These functions help creating a demo for a prime implicant chart, and also show how to solve it using a minimum number of prime implicants.

Usage

makeChart(primes = "", configs = "", snames = "")
solveChart(chart, row.dom = FALSE, all.sol = FALSE, ...)

Arguments

primes

A string containing prime implicants, separated by commas, or a matrix of implicants.

configs

A string containing causal configurations, separated by commas, or a matrix of causal configurations in the implicants space.

snames

A string containing the sets' names, separated by commas.

chart

A logical matrix (as the PI chart).

row.dom

Logical, apply row dominance to eliminate redundant prime implicants.

all.sol

Derive all possible solutions, irrespective of the number of PIs.

...

Other arguments (mainly for backwards compatibility).

Value

For makeChart: a logical matrix.For solveChart: a matrix containing all possible combinations of PI chart rows necessary to cover all its columns.

Details

A PI chart, in this package, is a logical matrix (with TRUE/FALSE values), containing the prime implicants on the rows and the starting configurations of causal conditions, on the columns, like the one produced by makeChart(). It is useful to determine visually which prime implicant (if any) is essential.

When primes and configs are character, the individual sets are identified using the function translate(), using the DNF - Disjunctive Normal Form, which needs the set names in the absence of any other information. If products are formed using the standard * operator, specifying the set names is not mandatory.

When primes and configs are matrices, they have to be specified at implicants level, where the value 0 is interpreted as a minimized literal.

The chart is subsequently processed algorithmically by solveChart() to further reduce the reduntant prime implicants. It applies a linear programming function from package lpSolve, to find the absolute minimal number M of rows (prime implicants) necessary to cover all columns, then searches through all possible combinations of M rows, to find those which actually cover the columns.

Since all possible combinations grow exponentially with the number of prime implicants resulted from the Quine-McCluskey minimization procedure, this function tries hard to reduce this number by first eliminating the redundant prime implicants.

When set to TRUE, the argument row.dom does something like this by eliminating the dominated rows (those which cover a smaller number of columns than another, dominant prime implicant).

References

Quine, W.V. (1952) The Problem of Simplifying Truth Functions, The American Mathematical Monthly, vol.59, no.8. (Oct., 1952), pp.521-531.

Ragin, Charles C. (1987) The Comparative Method. Moving beyond qualitative and quantitative strategies, Berkeley: University of California Press

Examples

Run this code

if (require("QCA")) {

# non-standard products, it needs the set names
chart <- makeChart("A, B, c", "ABC, Abc, AbC, aBc", snames =  "A,B,C")
prettyTable(chart)
#   ABC Abc AbC aBc
# A  x   x   x   - 
# B  x   -   -   x 
# c  -   x   -   x 

solveChart(chart)
# first and second rows (A + B)
# and first and third rows (A + c)
# A is an essential prime implicant
#      A + B  A + c
#      [,1]   [,2]
# [1,]    1      1
# [2,]    2      3


# Quine's example, page 528
rows <- c("AB, BC, Ac, aC, abd, bcd")
cols <- c("ABCD, ABCd, ABcD, ABcd, AbcD, Abcd, aBCD, aBCd, abCD, abCd, abcd")

prettyTable(chart <- makeChart(rows, cols, "A,B,C,D"))
#     ABCD ABCd ABcD ABcd AbcD Abcd aBCD aBCd abCD abCd abcd
# AB   x    x    x    x    -    -    -    -    -    -    -  
# BC   x    x    -    -    -    -    x    x    -    -    -  
# Ac   -    -    x    x    x    x    -    -    -    -    -  
# aC   -    -    -    -    -    -    x    x    x    x    -  
# abd  -    -    -    -    -    -    -    -    -    x    x  
# bcd  -    -    -    -    -    x    -    -    -    -    x 

solveChart(chart)
#      [,1] [,2] [,3] [,4]
# [1,]    1    1    2    2
# [2,]    3    3    3    3
# [3,]    4    4    4    4
# [4,]    5    6    5    6


# using DNF standard product sign
rows <- c("EF, ~GH, IJ")
cols <- c("~EF*~GH*IJ, EF*GH*~IJ, ~EF*GH*IJ, EF*~GH*~IJ")
prettyTable(chart <- makeChart(rows, cols))
#     ~EF*~GH*IJ EF*GH*~IJ ~EF*GH*IJ EF*~GH*~IJ
# EF      -          x         -         x     
# ~GH     x          -         -         x     
# IJ      x          -         x         -     

solveChart(chart)
# ~GH is redundant
#     EF + IJ
#      [,1]
# [1,]    1
# [2,]    3


# using implicant matrices
primes <- matrix(c(2,2,1,0,2,2,0,2,2,2), nrow = 2)
configs <- matrix(c(2,2,2,1,1,2,2,2,2,1,2,2,2,2,2), nrow = 3)
colnames(primes) <- colnames(configs) <- LETTERS[1:5]

# the prime implicants: AbCE and ACDE
primes
#      A B C D E
# [1,] 2 1 2 0 2
# [2,] 2 0 2 2 2

# the initial causal combinations: AbCdE, AbCDE and ABCDE
configs
#      A B C D E
# [1,] 2 1 2 1 2
# [2,] 2 1 2 2 2
# [3,] 2 2 2 2 2

chartLC <- makeChart(primes, configs)
prettyTable(chartLC)
#      AbCdE AbCDE ABCDE
# AbCE   x     x     -  
# ACDE   -     x     x   
}

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