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remove unused methods

release/4.3a0
Varun Agrawal 2024-12-31 14:08:25 -05:00
parent 9cacb9876e
commit c6e9bfc824
2 changed files with 2 additions and 79 deletions
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34
gtsam/discrete/DiscreteTableConditional.cpp
View File

@ -141,18 +141,6 @@ bool DiscreteTableConditional::equals(const DiscreteFactor& other,
}
}
/* ************************************************************************** */
TableFactor::shared_ptr DiscreteTableConditional::likelihood(
const DiscreteValues& frontalValues) const {
throw std::runtime_error("Likelihood not implemented");
}
/* ****************************************************************************/
TableFactor::shared_ptr DiscreteTableConditional::likelihood(
size_t frontal) const {
throw std::runtime_error("Likelihood not implemented");
}
/* ****************************************************************************/
DiscreteConditional::shared_ptr DiscreteTableConditional::max(
const Ordering& keys) const {
@ -180,26 +168,4 @@ DiscreteConditional::shared_ptr DiscreteTableConditional::prune(
this->nrFrontals(), this->discreteKeys(), pruned.sparseTable());
}
/* ************************************************************************** */
size_t DiscreteTableConditional::argmax(
const DiscreteValues& parentsValues) const {
// Initialize
size_t maxValue = 0;
double maxP = 0;
DiscreteValues values = parentsValues;
assert(nrFrontals() == 1);
Key j = firstFrontalKey();
for (size_t value = 0; value < cardinality(j); value++) {
values[j] = value;
double pValueS = (*this)(values);
// Update MPE solution if better
if (pValueS > maxP) {
maxP = pValueS;
maxValue = value;
}
}
return maxValue;
}
} // namespace gtsam

47
gtsam/discrete/DiscreteTableConditional.h
View File

@ -158,28 +158,8 @@ class GTSAM_EXPORT DiscreteTableConditional : public DiscreteConditional {
/// @name Standard Interface
/// @{
/// Log-probability is just -error(x).
double logProbability(const DiscreteValues& x) const { return -error(x); }
/// print index signature only
void printSignature(
const std::string& s = "Discrete Conditional: ",
const KeyFormatter& formatter = DefaultKeyFormatter) const {
static_cast<const BaseConditional*>(this)->print(s, formatter);
}
/** Convert to a likelihood factor by providing value before bar. */
TableFactor::shared_ptr likelihood(const DiscreteValues& frontalValues) const;
/** Single variable version of likelihood. */
TableFactor::shared_ptr likelihood(size_t frontal) const;
/**
* @brief Return assignment for single frontal variable that maximizes value.
* @param parentsValues Known assignments for the parents.
* @return maximizing assignment for the frontal variable.
*/
size_t argmax(const DiscreteValues& parentsValues = DiscreteValues()) const;
/// Return the underlying TableFactor
TableFactor table() const { return table_; }
/**
* @brief Create new conditional by maximizing over all
@ -195,29 +175,6 @@ class GTSAM_EXPORT DiscreteTableConditional : public DiscreteConditional {
/// @name Advanced Interface
/// @{
/// Return all assignments for frontal variables.
std::vector<DiscreteValues> frontalAssignments() const;
/// Return all assignments for frontal *and* parent variables.
std::vector<DiscreteValues> allAssignments() const;
/// @}
/// @name HybridValues methods.
/// @{
using BaseConditional::operator(); ///< HybridValues version
/**
* Calculate log-probability log(evaluate(x)) for HybridValues `x`.
* This is actually just -error(x).
*/
double logProbability(const HybridValues& x) const override {
return -error(x);
}
/// Return the underlying TableFactor
TableFactor table() const { return table_; }
/// Evaluate the conditional given the values.
virtual double evaluate(const Assignment<Key>& values) const override {
return table_.evaluate(values);