LIB=lib
# Define build targets
-TARGETS=tests utils lib examples testregression
+TARGETS=tests utils lib examples testregression testem
ifdef WITH_DOC
TARGETS:=$(TARGETS) doc
endif
cd tests && testregression.bat && cd ..
endif
+testem: tests/testem/testem$(EE)
+ @echo Starting EM tests
+ cd tests/testem && ./runtests && cd ../..
# DOCUMENTATION
################
/// The maximization steps to take
std::vector<MaximizationStep> _msteps;
-
size_t _iters;
std::vector<Real> _lastLogZ;
+
+ size_t _max_iters;
+ Real _log_z_tol;
public:
+ /// Key for setting maximum iterations @see setTermConditions
+ static const std::string MAX_ITERS_KEY;//("max_iters");
+ /// Default maximum iterations
+ static const size_t MAX_ITERS_DEFAULT;
+ /// Key for setting likelihood termination condition @see setTermConditions
+ static const std::string LOG_Z_TOL_KEY;
+ /// Default log_z_tol
+ static const Real LOG_Z_TOL_DEFAULT;
+
/// Construct an EMAlg from all these objects
EMAlg(const Evidence& evidence, InfAlg& estep,
- std::vector<MaximizationStep>& msteps)
+ std::vector<MaximizationStep>& msteps,
+ PropertySet* termconditions=NULL)
: _evidence(evidence),
_estep(estep),
_msteps(msteps),
_iters(0),
- _lastLogZ()
- {}
+ _lastLogZ(),
+ _max_iters(MAX_ITERS_DEFAULT),
+ _log_z_tol(LOG_Z_TOL_DEFAULT) {
+ setTermConditions(termconditions);
+ }
/// Construct an EMAlg from an input stream
EMAlg(const Evidence& evidence, InfAlg& estep, std::istream& mstep_file);
+ /// Change the coditions for termination
+ /** There are two possible parameters in the PropertySety
+ * - max_iters maximum number of iterations (default 30)
+ * - log_z_tol proportion of increase in logZ (default 0.01)
+ *
+ * \see hasSatisifiedTermConditions()
+ */
+ void setTermConditions(const PropertySet* p);
+
+ /// Determine if the termination conditions have been met.
+ /** There are two sufficient termination conditions:
+ * -# the maximum number of iterations has been performed
+ * -# the ratio of logZ increase over previous logZ is less than the
+ * tolerance. I.e.
+ \f$ \frac{\log(Z_{current}) - \log(Z_{previous})}
+ {| \log(Z_{previous}) | } < tol \f$.
+ */
+ bool hasSatisfiedTermConditions() const;
+
+ size_t getCurrentIters() const { return _iters; }
+
/// Perform an iteration over all maximization steps
Real iterate();
+
/// Performs an iteration over a single MaximizationStep
- Real iterate(const MaximizationStep& mstep);
+ Real iterate(MaximizationStep& mstep);
+
+ /// Iterate until termination conditions satisfied
+ void run();
};
}
}
+const std::string EMAlg::MAX_ITERS_KEY("max_iters");
+const std::string EMAlg::LOG_Z_TOL_KEY("log_z_tol");
+const size_t EMAlg::MAX_ITERS_DEFAULT = 30;
+const Real EMAlg::LOG_Z_TOL_DEFAULT = 0.01;
+
EMAlg::EMAlg(const Evidence& evidence, InfAlg& estep, std::istream& msteps_file)
- : _evidence(evidence),
+ : _evidence(evidence),
_estep(estep),
_msteps(),
_iters(0),
- _lastLogZ()
+ _lastLogZ(),
+ _max_iters(MAX_ITERS_DEFAULT),
+ _log_z_tol(LOG_Z_TOL_DEFAULT)
{
- size_t num_msteps;
+ msteps_file.exceptions( std::istream::eofbit | std::istream::failbit
+ | std::istream::badbit );
+ size_t num_msteps = -1;
msteps_file >> num_msteps;
_msteps.reserve(num_msteps);
for (size_t i = 0; i < num_msteps; ++i) {
}
}
-Real EMAlg::iterate(const MaximizationStep& mstep) {
+void EMAlg::setTermConditions(const PropertySet* p) {
+ if (NULL == p) {
+ return;
+ }
+ if (p->hasKey(MAX_ITERS_KEY)) {
+ _max_iters = p->getStringAs<size_t>(MAX_ITERS_KEY);
+ }
+ if (p->hasKey(LOG_Z_TOL_KEY)) {
+ _log_z_tol = p->getStringAs<Real>(LOG_Z_TOL_KEY);
+ }
+}
+
+bool EMAlg::hasSatisfiedTermConditions() const {
+ if (_iters >= _max_iters) {
+ return 1;
+ } else if (_lastLogZ.size() < 3) {
+ // need at least 2 to calculate ratio
+ // Also, throw away first iteration, as the parameters may not
+ // have been normalized according to the estimation method
+ return 0;
+ } else {
+ Real current = _lastLogZ[_lastLogZ.size() - 1];
+ Real previous = _lastLogZ[_lastLogZ.size() - 2];
+ if (previous == 0) return 0;
+ Real diff = current - previous;
+ if (diff < 0) {
+ std::cerr << "Error: in EM log-likehood decreased from " << previous
+ << " to " << current << std::endl;
+ return 1;
+ }
+ return diff / abs(previous) <= _log_z_tol;
+ }
+}
+
+Real EMAlg::iterate(MaximizationStep& mstep) {
Evidence::const_iterator e = _evidence.begin();
Real logZ = 0;
return likelihood;
}
+void EMAlg::run() {
+ while(!hasSatisfiedTermConditions()) {
+ iterate();
+ }
+}
+
}
"Can't parse Evidence line",
"Invalid observation in Evidence file",
"Invalid variable order in SharedParameters",
- "Input line in variable order invalid",
+ "Variable order line in EM file is invalid",
"Unrecognized parameter estimation method"
};
--- /dev/null
+1
+
+1
+ConditionalProbEstimation [target_dim=2,total_dim=8,pseudo_count=1]
+1
+0 1 0 2
--- /dev/null
+1
+
+3
+0 1 2
+2 2 2
+8
+0 0.5
+1 0.5
+2 0.5
+3 0.5
+4 0.5
+5 0.5
+6 0.5
+7 0.5
\ No newline at end of file
--- /dev/null
+1
+
+1
+ConditionalProbEstimation [target_dim=2,total_dim=8,pseudo_count=1]
+1
+2 1 2 4
--- /dev/null
+1
+
+1
+ConditionalProbEstimation [target_dim=2,total_dim=8,pseudo_count=1]
+3
+2 1 2 4
+1 0 1 6
+0 6 2 7
--- /dev/null
+1
+
+1
+ConditionalProbEstimation [target_dim=2,total_dim=8,pseudo_count=1]
+2
+2 1 2 4
+0 6 2 7
--- /dev/null
+#!/bin/bash
+TMPFILE1=`mktemp /var/tmp/testem.XXXXXX`
+trap 'rm -f $TMPFILE1' 0 1 15
+
+./testem 2var.fg 2var_data.tab 2var.em > $TMPFILE1
+./testem 3var.fg 2var_data.tab 3var.em >> $TMPFILE1
+./testem ../hoi1.fg hoi1_data.tab hoi1_share_f0_f2.em >> $TMPFILE1
+./testem ../hoi1.fg hoi1_data.tab hoi1_share_f0_f1_f2.em >> $TMPFILE1
+diff -s $TMPFILE1 testem.out || exit 1
+
+rm -f $TMPFILE1
ifstream emstream(argv[3]);
EMAlg em(e, *inf, emstream);
- for (size_t i = 0; i < 10; ++i) {
+ while(!em.hasSatisfiedTermConditions()) {
Real l = em.iterate();
- cout << "Iteration " << i << " likelihood: " << l <<endl;
+ cout << "Iteration " << em.getCurrentIters() << " likelihood: " << l <<endl;
}
+ cout << endl
+ << "Inferred Factor Graph:" << endl
+ << "######################" << endl
+ << inf->fg();
+
return 0;
}
--- /dev/null
+Number of samples: 20
+Sample sample.0 has 2 observations.
+Sample sample.1 has 2 observations.
+Sample sample.2 has 2 observations.
+Sample sample.3 has 2 observations.
+Sample sample.4 has 2 observations.
+Sample sample.5 has 2 observations.
+Sample sample.6 has 2 observations.
+Sample sample.7 has 2 observations.
+Sample sample.8 has 2 observations.
+Sample sample.9 has 2 observations.
+Sample sample_0 has 2 observations.
+Sample sample_1 has 2 observations.
+Sample sample_2 has 2 observations.
+Sample sample_3 has 2 observations.
+Sample sample_4 has 2 observations.
+Sample sample_5 has 2 observations.
+Sample sample_6 has 2 observations.
+Sample sample_7 has 2 observations.
+Sample sample_8 has 2 observations.
+Sample sample_9 has 2 observations.
+Iteration 1 likelihood: -13.8629
+Iteration 2 likelihood: -9.56675
+Iteration 3 likelihood: -9.56675
+
+Inferred Factor Graph:
+######################
+1
+
+2
+0 1
+2 2
+4
+0 0.16666666666667
+1 0.66666666666667
+2 0.83333333333333
+3 0.33333333333333
+Number of samples: 20
+Sample sample.0 has 2 observations.
+Sample sample.1 has 2 observations.
+Sample sample.2 has 2 observations.
+Sample sample.3 has 2 observations.
+Sample sample.4 has 2 observations.
+Sample sample.5 has 2 observations.
+Sample sample.6 has 2 observations.
+Sample sample.7 has 2 observations.
+Sample sample.8 has 2 observations.
+Sample sample.9 has 2 observations.
+Sample sample_0 has 2 observations.
+Sample sample_1 has 2 observations.
+Sample sample_2 has 2 observations.
+Sample sample_3 has 2 observations.
+Sample sample_4 has 2 observations.
+Sample sample_5 has 2 observations.
+Sample sample_6 has 2 observations.
+Sample sample_7 has 2 observations.
+Sample sample_8 has 2 observations.
+Sample sample_9 has 2 observations.
+Iteration 1 likelihood: 0
+Iteration 2 likelihood: 3.97035
+Iteration 3 likelihood: 3.97035
+
+Inferred Factor Graph:
+######################
+1
+
+3
+0 1 2
+2 2 2
+8
+0 0.21428571428571
+1 0.64285714285714
+2 0.78571428571429
+3 0.35714285714286
+4 0.21428571428571
+5 0.64285714285714
+6 0.78571428571429
+7 0.35714285714286
+Number of samples: 5
+Sample sample_0 has 5 observations.
+Sample sample_1 has 4 observations.
+Sample sample_2 has 6 observations.
+Sample sample_3 has 6 observations.
+Sample sample_4 has 5 observations.
+Iteration 1 likelihood: 11.1646
+Iteration 2 likelihood: 1.53723
+Iteration 3 likelihood: 1.64691
+Iteration 4 likelihood: 1.67497
+Iteration 5 likelihood: 1.68191
+
+Inferred Factor Graph:
+######################
+3
+
+3
+2 6 7
+2 2 2
+8
+0 0.39834336080566
+1 0.35146414656547
+2 0.60165663919434
+3 0.64853585343454
+4 0.80484468795651
+5 0.67374245802992
+6 0.19515531204349
+7 0.32625754197008
+
+3
+0 1 6
+2 2 2
+8
+0 1.0352133626924
+1 1.547478952122
+2 2.3176521897449
+3 1.2804190071868
+4 4.9220798130027
+5 2.5272557501946
+6 0.83127929631575
+7 0.26280563080263
+
+3
+1 2 4
+2 2 2
+8
+0 0.39834336080566
+1 0.60165663919434
+2 0.35146414656547
+3 0.64853585343454
+4 0.80484468795651
+5 0.19515531204349
+6 0.67374245802992
+7 0.32625754197008
+Number of samples: 5
+Sample sample_0 has 5 observations.
+Sample sample_1 has 4 observations.
+Sample sample_2 has 6 observations.
+Sample sample_3 has 6 observations.
+Sample sample_4 has 5 observations.
+Iteration 1 likelihood: 11.1646
+Iteration 2 likelihood: -7.29331
+Iteration 3 likelihood: -7.261
+
+Inferred Factor Graph:
+######################
+3
+
+3
+2 6 7
+2 2 2
+8
+0 0.49531219972645
+1 0.49910794290825
+2 0.50468780027355
+3 0.50089205709175
+4 0.64041654995841
+5 0.49512229392399
+6 0.35958345004159
+7 0.50487770607601
+
+3
+0 1 6
+2 2 2
+8
+0 0.49531219972645
+1 0.50468780027355
+2 0.49910794290825
+3 0.50089205709175
+4 0.64041654995841
+5 0.35958345004159
+6 0.49512229392399
+7 0.50487770607601
+
+3
+1 2 4
+2 2 2
+8
+0 0.49531219972645
+1 0.50468780027355
+2 0.49910794290825
+3 0.50089205709175
+4 0.64041654995841
+5 0.35958345004159
+6 0.49512229392399
+7 0.50487770607601
projects / libdai.git / commitdiff