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1 function engine = belprop_mrf2_inf_engine(mrf2, varargin)
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2 % BELPROP_MRF2_INF_ENGINE Belief propagation for MRFs with discrete pairwise potentials
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3 % engine = belprop_mrf2_inf_engine(mrf2, ...)
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4 %
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5 % This is like belprop_inf_engine, except it is designed for mrf2, so is much faster.
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6 %
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7 % [ ... ] = belprop_mrf2_inf_engine(..., 'param1',val1, 'param2',val2, ...)
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8 % allows you to specify optional parameters as name/value pairs.
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9 % Parameters modifying behavior of enter_evidence are below [default value in brackets]
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10 %
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11 % max_iter - max. num. iterations [ 5*nnodes]
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12 % momentum - weight assigned to old message in convex combination
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13 % (useful for damping oscillations) [0]
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14 % tol - tolerance used to assess convergence [1e-3]
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15 % verbose - 1 means print error at every iteration [0]
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16 %
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17 % Parameters can be changed later using set_params
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18
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19
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20 % The advantages of pairwise potentials are
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21 % (1) we can compute messages using vector-matrix multiplication
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22 % (2) we can easily specify the parameters: one potential per edge
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23 % In contrast, potentials on larger cliques are more complicated to deal with.
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24
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25
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26 nnodes = length(mrf2.adj_mat);
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27
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28 [engine.max_iter, engine.momentum, engine.tol, engine.verbose] = ...
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29 process_options(varargin, 'max_iter', [], 'momentum', 0, 'tol', 1e-3, ...
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30 'verbose', 0);
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31
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32 if isempty(engine.max_iter) % no user supplied value, so compute default
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33 engine.max_iter = 5*nnodes;
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34 %if acyclic(mrf2.adj_mat, 0) --- can be very slow!
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35 % engine.max_iter = nnodes;
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36 %else
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37 % engine.max_iter = 5*nnodes;
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38 %end
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39 end
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40
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41 engine.bel = cell(1, nnodes); % store results of enter_evidence here
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42 engine.mrf2 = mrf2;
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43
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44 engine = class(engine, 'belprop_mrf2_inf_engine');
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45
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46
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