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% Run the composite mapping algorithm to make a positive sequence % Copyright 1999 by Todd K. Moon % Set up the initial vector N = 512; q = 10; t = (0:q)'; h = 0.54 + 0.46*cos(pi*t/q);

% Illustration of example em algorithm computations % % Copyright 1999 by Todd K. Moon % Initialize the data x1 = 25; x2 = 38; x3=37; y1 = x1+x2; y2 = x3; maxsteps = 10; p = 0; % in

function d = convnorm(branch,input,state,nextstate) % % Compute the Hamming distance between the branchweights and the input % This function may be feval'ed for use with the Viterbi algorithm % (s

function P = greedyperm2(x,z) % % Using a greedy algorithm, determine a permutation P such that Px=z % as closely as possible. % This algorithm is more complex than greedyperm % % function P =

function P = greedyperm(x,z) % % Using a greedy algorithm, determine a permutation P such that Px=z % as closely as possible. % % function P = greedyperm(x,z) % Copyright 1999 by Todd K. Moon

function g = gcdint1(b,c) % % Compute (only) the GCD (a,b) using the Euclidean algorithm % % function g = gcdint1(b,c) % % b,c = integers % % g = GCD(b,c) % Copyright 1999 by Todd K. Moon

function d = vitnop(branch,input) % % Compute the norm of the difference between inputs % This function may be feval'ed for use with the Viterbi algorithm % In this case, the norm is simply taken

% Test the conjugate gradient algorithm % Copyright 1999 by Todd K. Moon hold off ; rosenbrock; hold on xoff = -.3; x = [-1;-1]; [xn,X] = conjgrad2(x,'rosengrad','rosenhess') [n,k] = size

% Test the forward dynamic programming algorithm % Copyright 1999 by Todd K. Moon G{1} = [2,3,4,5]; G{2} = [6,7]; G{3} = [7,8]; G{4} = [6,8]; G{5} = 8; G{6} = [9,10,11]; G{7} = [10,11,12];

% test the ART algorithm % Copyright 1999 by Todd K. Moon A = [1 2 3; -4 -2 -5; 2 7 10; 4 3 -2]; xtrue = [1;2;3]; b = A*xtrue; lastx = [1;1;1]; e = []; for i=1:10 x =