version 3.18

# Functions Related to the Implicant Matrix

## Description

This is a set of functions dedicated to the implicant matrix, a space where all causal configurations and their minimized solutions are found.

They can produce all possible implicants and prime implicants, or all possible combinations for a specific number of causal conditions and their number of values (either binary or multi-value).

## Usage

    allExpressions(noflevels, arrange = FALSE, depth, raw = FALSE, ...)
    createMatrix(noflevels, ...)
    getRow(noflevels, row.no, zerobased = FALSE)

## Arguments

`noflevels` |  |  |  | The number of levels (values) for each causal condition.
`arrange` |  |  |  | Logical, if `TRUE` the result matrix is arranged for visual inspection.
`depth` |  |  |  | Integer, an upper number of causal conditions to form expressions with.
`raw` |  |  |  | Logical, if `TRUE` it returns the matrix indicating which conditions have been minimized, using `-1`.
`row.no` |  |  |  | A vector, the desired row numbers.
`zerobased` |  |  |  | Logical, the first row number is zero.
`...` |  |  |  | Other arguments.

## Details

A truth table for binary crisp conditions is a matrix with $2^k$ rows, where $k$ is the number of causal conditions.

For multi-value causal conditions, the same equation can be generalised to:

$v_{1} \cdot v_{2} \phantom{.} \cdot \phantom{.} ... \phantom{.} \cdot \phantom{.} v_{k}$

where $v$ is the number of values (levels) for every causal condition from $1$ to $k$.

Implicant matrices contain all rows from the truth table, plus all of their supersets, (all implicants and prime implicants), including the empty set (Dusa 2007, 2010).

For a binary crisp set procedure, there are $3^k - 1$ possible expressions (groupings), see Ragin (2010). Including the empty set (the situation when all causal conditions have been minimized), the implicant matrix consists of exactly $3^k$ rows, including the truth table configurations.

In fact, $3^k$ is also obtained by the product:

$(2 + 1) \cdot (2 + 1) \phantom{.} \cdot \phantom{.} ... \phantom{.} \cdot \phantom{.} (2 + 1)$

For multi-value causal conditions, the same equation can be generalised to:

$(v_{1} + 1) \cdot (v_{2} + 1) \phantom{.} \cdot \phantom{.} ... \phantom{.} \cdot \phantom{.} (v_{k} + 1)$

where every number of levels in each causal conditions is incremented with 1, to allow coding the minimization of literals in each (prime) implicant (see examples).

The function `allExpressions()` creates a matrix which contains all possible implicants and prime implicants, displayed in the original values form using the code `-1` to point the minimized literals, while the other functions use the code `0`, all other values being incremented with 1.

Specifying a smaller `depth` automatically activates the argument `arrange`.

When the argument `arrange` is activated, the output is arranged in the increasing order of the number of conditions which form conjunctions, up to the maximum number specified by the argument `depth` (which if `NULL`, it is considered equal to the number of columns in the matrix).

The function `createMatrix()` creates a base matrix for truth tables and implicant matrices.

The function `getRow()` takes the number of a row in the truth table or implicant matrix (in its decimal form), and transforms it into its binary (or multi-base) representation, as a configuration of binary or multi-values for each causal condition.

Note that $\textsf{R}$ is not a zero-based language (where all numbers start from 0), and positions in vectors and matrices start with 1. For this reason, although (mathematicall) the binary representation of the decimal number 0 (for example, at three causal conditions) is 0 0 0, in $\textsf{R}$ that would be the “first” line in the implicant matrix, therefore 0 0 0 is translated into the number 1.

## Value

A matrix with $k$ columns and:

$v_{1} \cdot v_{2} \phantom{.} \cdot \phantom{.} ... \phantom{.} \cdot \phantom{.} v_{k}$ rows if a truth table;

$(v_{1} + 1) \cdot (v_{2} + 1) \phantom{.} \cdot \phantom{.} ... \phantom{.} \cdot \phantom{.} (v_{k} + 1)$ rows if an implicant matrix;

$x$ rows, equal to the length of `row.no`.

## References

Dusa, Adrian. 2007. Enhancing Quine-McCluskey. WP 2007-49, COMPASSS.

Dusa, Adrian. 2010. “A Mathematical Approach to the Boolean Minimization Problem.” Quality & Quantity vol.44, no.1, pp.99-113.

Ragin, Charles C. (2000) Fuzzy-Set Social Science. Chicago: University of Chicago Press.

## Examples

    # three binary causal conditions, having two levels each: 0 and 1=
    noflevels <- c(2, 2, 2)

    # for three binary causal conditions
    allExpressions(noflevels)

     1
     2              0
     3              1
     4         0
     5         0    0
     6         0    1
     7         1
     8         1    0
     9         1    1
    10    0
    11    0         0
    12    0         1
    13    0    0
    14    0    0    0
    15    0    0    1
    16    0    1
    17    0    1    0
    18    0    1    1
    19    1
    20    1         0
    21    1         1
    22    1    0
    23    1    0    0
    24    1    0    1
    25    1    1
    26    1    1    0
    27    1    1    1

    # the same matrix, this time arranged better
    # (last rows represent the truth table)
    allExpressions(noflevels, arrange = TRUE)

    1    0
    2    1
    3         0
    4         1
    5              0
    6              1
    7    0    0
    8    0    1
    9    1    0
    10    1    1
    11    0         0
    12    0         1
    13    1         0
    14    1         1
    15         0    0
    16         0    1
    17         1    0
    18         1    1
    19    0    0    0
    20    0    0    1
    21    0    1    0
    22    0    1    1
    23    1    0    0
    24    1    0    1
    25    1    1    0
    26    1    1    1

    # show only the implicants (excluding the truth table)
    allExpressions(noflevels, arrange = TRUE, depth = 2)

     1    0
     2    1
     3         0
     4         1
     5              0
     6              1
     7    0    0
     8    0    1
     9    1    0
    10    1    1
    11    0         0
    12    0         1
    13    1         0
    14    1         1
    15         0    0
    16         0    1
    17         1    0
    18         1    1

    # using the raw form
    allExpressions(noflevels, raw = TRUE)

     1   -1   -1   -1
     2   -1   -1    0
     3   -1   -1    1
     4   -1    0   -1
     5   -1    0    0
     6   -1    0    1
     7   -1    1   -1
     8   -1    1    0
     9   -1    1    1
    10    0   -1   -1
    11    0   -1    0
    12    0   -1    1
    13    0    0   -1
    14    0    0    0
    15    0    0    1
    16    0    1   -1
    17    0    1    0
    18    0    1    1
    19    1   -1   -1
    20    1   -1    0
    21    1   -1    1
    22    1    0   -1
    23    1    0    0
    24    1    0    1
    25    1    1   -1
    26    1    1    0
    27    1    1    1

    # create a base truth table for 3 binary conditions
    createMatrix(noflevels)

         [,1] [,2] [,3]
    [1,]    0    0    0
    [2,]    0    0    1
    [3,]    0    1    0
    [4,]    0    1    1
    [5,]    1    0    0
    [6,]    1    0    1
    [7,]    1    1    0
    [8,]    1    1    1

    # its implicant matrix
    createMatrix(noflevels + 1)

          [,1] [,2] [,3]
     [1,]    0    0    0
     [2,]    0    0    1
     [3,]    0    0    2
     [4,]    0    1    0
     [5,]    0    1    1
     [6,]    0    1    2
     [7,]    0    2    0
     [8,]    0    2    1
     [9,]    0    2    2
    [10,]    1    0    0
    [11,]    1    0    1
    [12,]    1    0    2
    [13,]    1    1    0
    [14,]    1    1    1
    [15,]    1    1    2
    [16,]    1    2    0
    [17,]    1    2    1
    [18,]    1    2    2
    [19,]    2    0    0
    [20,]    2    0    1
    [21,]    2    0    2
    [22,]    2    1    0
    [23,]    2    1    1
    [24,]    2    1    2
    [25,]    2    2    0
    [26,]    2    2    1
    [27,]    2    2    2

    # create a base truth table where the second condition has three levels
    createMatrix(c(2, 3, 2))

          [,1] [,2] [,3]
     [1,]    0    0    0
     [2,]    0    0    1
     [3,]    0    1    0
     [4,]    0    1    1
     [5,]    0    2    0
     [6,]    0    2    1
     [7,]    1    0    0
     [8,]    1    0    1
     [9,]    1    1    0
    [10,]    1    1    1
    [11,]    1    2    0
    [12,]    1    2    1

    # deriving rows
    rows <- c(2, 4, 5, 7, 8, 10, 11, 13, 14, 16, 17)
    mat <- getRow(rows, noflevels + 1) # note the +1
    rownames(mat) <- rows
    colnames(mat) <- c("A", "B", "C")
    mat

       A B C
    2  0 0 1
    4  0 1 0
    5  0 1 1
    7  0 2 0
    8  0 2 1
    10 1 0 0
    11 1 0 1
    13 1 1 0
    14 1 1 1
    16 1 2 0
    17 1 2 1

    # implicant matrix     normal values

    #      A  B  C    |       A  B  C
    #   2  0  0  1    |    2  -  -  0       ~C
    #   4  0  1  0    |    4  -  0  -       ~B
    #   5  0  1  1    |    5  -  0  0       ~B~C
    #   7  0  2  0    |    7  -  1  -       B
    #   8  0  2  1    |    8  -  1  0       B~C
    #  10  1  0  0    |   10  0  -  -       ~A
    #  11  1  0  1    |   11  0  -  0       ~A~C
    #  13  1  1  0    |   13  0  0  -       ~A~B
    #  14  1  1  1    |   14  0  0  0       ~A~B~C
    #  16  1  2  0    |   16  0  1  -       ~AB
    #  17  1  2  1    |   17  0  1  0       ~AB~C

## Author

Adrian Dusa

## See also

`expand.grid`