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mintable.m
function y = mintable(n) generates a table of minterm
vectors by repeated use of the m-function
minterm .
function y = mintable(n)
% MINTABLE y = mintable(n) Table of minterms vectors% Version of 3/2/93
% Generates a table of minterm vectors% Uses the m-function minterm
y = zeros(n,2^n);for i = 1:n
y(i,:) = minterm(n,i);end minvec3.m sets basic minterm vectors for the class . (Similarly for minvec4.m, minvec5.m , etc.)
% MINVEC3 file minvec3.m Basic minterm vectors
% Version of 1/31/95A = minterm(3,1);
B = minterm(3,2);C = minterm(3,3);
Ac = ~A;Bc = ~B;
Cc = ~C;disp('Variables are A, B, C, Ac, Bc, Cc')
disp('They may be renamed, if desired.')
minmap
function y = minmap(pm) reshapes a row or column vector
pm of minterm probabilities into minterm map format.
function y = minmap(pm)
% MINMAP y = minmap(pm) Reshapes vector of minterm probabilities% Version of 12/9/93
% Reshapes a row or column vector pm of minterm% probabilities into minterm map format
m = length(pm);n = round(log(m)/log(2));
a = fix(n/2);if m ~= 2^n
disp('The number of minterms is incorrect')else
y = reshape(pm,2^a,2^(n-a));end
binary.m
function y = binary(d,n) converts a matrix
d of
floating point nonnegative integers to a matrix of binary equivalents, one on each row.Adapted from m-functions written by Hans Olsson and by Simon Cooke. Each matrix row may
be converted to an unspaced string of zeros and ones by the device ys = setstr(y + '0').
function y = binary(d,n)
% BINARY y = binary(d,n) Integers to binary equivalents% Version of 7/14/95
% Converts a matrix d of floating point, nonnegative% integers to a matrix of binary equivalents. Each row
% is the binary equivalent (n places) of one number.% Adapted from the programs dec2bin.m, which shared
% first prize in an April 95 Mathworks contest.% Winning authors: Hans Olsson from Lund, Sweden,
% and Simon Cooke from Glasgow, UK.% Each matrix row may be converted to an unspaced string
% of zeros and ones by the device: ys = setstr(y + '0').if nargin<2, n = 1; end % Allows omission of argument n
[f,e]= log2(d);
n = max(max(max(e)),n);y = rem(floor(d(:)*pow2(1-n:0)),2); mincalc.m The m-procedure mincalc determines minterm probabilities from suitable data. For a discussion of the data formatting and certain problems, see 2.6.
% MINCALC file mincalc.m Determines minterm probabilities
% Version of 1/22/94 Updated for version 5.1 on 6/6/97% Assumes a data file which includes
% 1. Call for minvecq to set q basic minterm vectors, each (1 x 2^q)% 2. Data vectors DV = matrix of md data Boolean combinations of basic sets--
% Matlab produces md minterm vectors-- one on each row.% The first combination is always A|Ac (the whole space)
% 3. DP = row matrix of md data probabilities.% The first probability is always 1.
% 4. Target vectors TV = matrix of mt target Boolean combinations.% Matlab produces a row minterm vector for each target combination.
% If there are no target combinations, set TV = [];
[md,nd]= size(DV);
ND = 0:nd-1;ID = eye(nd); % Row i is minterm vector i-1
[mt,nt]= size(TV);
MT = 1:mt;rd = rank(DV);
if rd<md
disp('Data vectors are NOT linearly independent')else
disp('Data vectors are linearly independent')end
% Identification of which minterm probabilities can be determined from the data% (i.e., which minterm vectors are not linearly independent of data vectors)
AM = zeros(1,nd);for i = 1:nd
AM(i) = rd == rank([DV;ID(i,:)]); % Checks for linear dependence of each
endam = find(AM); % minterm vector
CAM = ID(am,:)/DV; % Determination of coefficients for the available mintermspma = DP*CAM'; % Calculation of probabilities of available minterms
PMA = [ND(am);pma]';
if sum(pma<-0.001)>0 % Check for data consistency
disp('Data probabilities are INCONSISTENT')else
% Identification of which target probabilities are computable from the dataCT = zeros(1,mt);
for j = 1:mtCT(j) = rd == rank([DV;TV(j,:)]);end
ct = find(CT);CCT = TV(ct,:)/DV; % Determination of coefficients for computable targets
ctp = DP*CCT'; % Determination of probabilitiesdisp(' Computable target probabilities')
disp([MT(ct); ctp]')
end % end for "if sum(pma<-0.001)>0"
disp(['The number of minterms is ',num2str(nd),])
disp(['The number of available minterms is ',num2str(length(pma)),])
disp('Available minterm probabilities are in vector pma')disp('To view available minterm probabilities, call for PMA') 
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