Merge branch 'no-edges2'
[libdai.git] / src / mf.cpp
1 /* Copyright (C) 2006-2008 Joris Mooij [j dot mooij at science dot ru dot nl]
2 Radboud University Nijmegen, The Netherlands
3
4 This file is part of libDAI.
5
6 libDAI is free software; you can redistribute it and/or modify
7 it under the terms of the GNU General Public License as published by
8 the Free Software Foundation; either version 2 of the License, or
9 (at your option) any later version.
10
11 libDAI is distributed in the hope that it will be useful,
12 but WITHOUT ANY WARRANTY; without even the implied warranty of
13 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
14 GNU General Public License for more details.
15
16 You should have received a copy of the GNU General Public License
17 along with libDAI; if not, write to the Free Software
18 Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
19 */
20
21
22 #include <iostream>
23 #include <sstream>
24 #include <map>
25 #include <set>
26 #include <dai/mf.h>
27 #include <dai/diffs.h>
28 #include <dai/util.h>
29
30
31 namespace dai {
32
33
34 using namespace std;
35
36
37 const char *MF::Name = "MF";
38
39
40 bool MF::checkProperties() {
41 if( !HasProperty("tol") )
42 return false;
43 if (!HasProperty("maxiter") )
44 return false;
45 if (!HasProperty("verbose") )
46 return false;
47
48 ConvertPropertyTo<double>("tol");
49 ConvertPropertyTo<size_t>("maxiter");
50 ConvertPropertyTo<size_t>("verbose");
51
52 return true;
53 }
54
55
56 void MF::Regenerate() {
57 // DAIAlgFG::Regenerate();
58
59 // clear beliefs
60 _beliefs.clear();
61 _beliefs.reserve( nrVars() );
62
63 // create beliefs
64 for( size_t i = 0; i < nrVars(); ++i )
65 _beliefs.push_back(Factor(var(i)));
66 }
67
68
69 string MF::identify() const {
70 stringstream result (stringstream::out);
71 result << Name << GetProperties();
72 return result.str();
73 }
74
75
76 void MF::init() {
77 assert( checkProperties() );
78
79 for( vector<Factor>::iterator qi = _beliefs.begin(); qi != _beliefs.end(); qi++ )
80 qi->fill(1.0);
81 }
82
83
84 double MF::run() {
85 clock_t tic = toc();
86
87 if( Verbose() >= 1 )
88 cout << "Starting " << identify() << "...";
89
90 size_t pass_size = _beliefs.size();
91 Diffs diffs(pass_size * 3, 1.0);
92
93 size_t t=0;
94 for( t=0; t < (MaxIter()*pass_size) && diffs.max() > Tol(); t++ ) {
95 // choose random Var i
96 size_t i = (size_t) (nrVars() * rnd_uniform());
97
98 Factor jan;
99 Factor piet;
100 foreach( const Neighbor &I, nbV(i) ) {
101 Factor henk;
102 foreach( const Neighbor &j, nbF(I) ) // for all j in I \ i
103 if( j != i )
104 henk *= _beliefs[j];
105 piet = factor(I).log0();
106 piet *= henk;
107 piet = piet.part_sum(var(i));
108 piet = piet.exp();
109 jan *= piet;
110 }
111
112 jan.normalize( _normtype );
113
114 if( jan.hasNaNs() ) {
115 cout << "MF::run(): ERROR: jan has NaNs!" << endl;
116 return NAN;
117 }
118
119 diffs.push( dist( jan, _beliefs[i], Prob::DISTLINF ) );
120
121 _beliefs[i] = jan;
122 }
123
124 updateMaxDiff( diffs.max() );
125
126 if( Verbose() >= 1 ) {
127 if( diffs.max() > Tol() ) {
128 if( Verbose() == 1 )
129 cout << endl;
130 cout << "MF::run: WARNING: not converged within " << MaxIter() << " passes (" << toc() - tic << " clocks)...final maxdiff:" << diffs.max() << endl;
131 } else {
132 if( Verbose() >= 2 )
133 cout << "MF::run: ";
134 cout << "converged in " << t / pass_size << " passes (" << toc() - tic << " clocks)." << endl;
135 }
136 }
137
138 return diffs.max();
139 }
140
141
142 Factor MF::beliefV (size_t i) const {
143 Factor piet;
144 piet = _beliefs[i];
145 piet.normalize( Prob::NORMPROB );
146 return(piet);
147 }
148
149
150 Factor MF::belief (const VarSet &ns) const {
151 if( ns.size() == 1 )
152 return belief( *(ns.begin()) );
153 else {
154 assert( ns.size() == 1 );
155 return Factor();
156 }
157 }
158
159
160 Factor MF::belief (const Var &n) const {
161 return( beliefV( findVar( n ) ) );
162 }
163
164
165 vector<Factor> MF::beliefs() const {
166 vector<Factor> result;
167 for( size_t i = 0; i < nrVars(); i++ )
168 result.push_back( beliefV(i) );
169 return result;
170 }
171
172
173 Complex MF::logZ() const {
174 Complex sum = 0.0;
175
176 for(size_t i=0; i < nrVars(); i++ )
177 sum -= beliefV(i).entropy();
178 for(size_t I=0; I < nrFactors(); I++ ) {
179 Factor henk;
180 foreach( const Neighbor &j, nbF(I) ) // for all j in I
181 henk *= _beliefs[j];
182 henk.normalize( Prob::NORMPROB );
183 Factor piet;
184 piet = factor(I).log0();
185 piet *= henk;
186 sum -= Complex( piet.totalSum() );
187 }
188
189 return -sum;
190 }
191
192
193 void MF::init( const VarSet &ns ) {
194 for( size_t i = 0; i < nrVars(); i++ ) {
195 if( ns && var(i) )
196 _beliefs[i].fill( 1.0 );
197 }
198 }
199
200
201 } // end of namespace dai