DiscreteFactorGraph.cpp

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/* ----------------------------------------------------------------------------
 
 * GTSAM Copyright 2010, Georgia Tech Research Corporation,
 * Atlanta, Georgia 30332-0415
 * All Rights Reserved
 * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
 
 * See LICENSE for the license information
 
 * -------------------------------------------------------------------------- */
 
#include <gtsam/discrete/DiscreteBayesTree.h>
#include <gtsam/discrete/DiscreteConditional.h>
#include <gtsam/discrete/DiscreteEliminationTree.h>
#include <gtsam/discrete/DiscreteFactorGraph.h>
#include <gtsam/discrete/DiscreteJunctionTree.h>
#include <gtsam/discrete/DiscreteLookupDAG.h>
#include <gtsam/inference/EliminateableFactorGraph-inst.h>
#include <gtsam/inference/FactorGraph-inst.h>
 
using std::vector;
using std::string;
using std::map;
 
namespace gtsam {
 
  // Instantiate base classes
  template class FactorGraph<DiscreteFactor>;
  template class EliminateableFactorGraph<DiscreteFactorGraph>;
 
  /* ************************************************************************ */
  bool DiscreteFactorGraph::equals(const This& fg, double tol) const {
    return Base::equals(fg, tol);
  }
 
  /* ************************************************************************ */
  KeySet DiscreteFactorGraph::keys() const {
    KeySet keys;
    for (const sharedFactor& factor : *this) {
      if (factor) keys.insert(factor->begin(), factor->end());
    }
    return keys;
  }
 
  /* ************************************************************************ */
  DiscreteKeys DiscreteFactorGraph::discreteKeys() const {
    DiscreteKeys result;
    for (auto&& factor : *this) {
      if (auto p = std::dynamic_pointer_cast<DiscreteFactor>(factor)) {
        DiscreteKeys factor_keys = p->discreteKeys();
        result.insert(result.end(), factor_keys.begin(), factor_keys.end());
      }
    }
 
    return result;
  }
 
  /* ************************************************************************ */
  DecisionTreeFactor DiscreteFactorGraph::product() const {
    DecisionTreeFactor result;
    for (const sharedFactor& factor : *this) {
      if (factor) result = (*factor) * result;
    }
    return result;
  }
 
  /* ************************************************************************ */
  double DiscreteFactorGraph::operator()(const DiscreteValues& values) const {
    double product = 1.0;
    for (const sharedFactor& factor : factors_) {
      if (factor) product *= (*factor)(values);
    }
    return product;
  }
 
  /* ************************************************************************ */
  void DiscreteFactorGraph::print(const string& s,
                                  const KeyFormatter& formatter) const {
    std::cout << s << std::endl;
    std::cout << "size: " << size() << std::endl;
    for (size_t i = 0; i < factors_.size(); i++) {
      std::stringstream ss;
      ss << "factor " << i << ": ";
      if (factors_[i] != nullptr) factors_[i]->print(ss.str(), formatter);
    }
  }
 
//  /* ************************************************************************* */
//  void DiscreteFactorGraph::permuteWithInverse(
//    const Permutation& inversePermutation) {
//      for(const sharedFactor& factor: factors_) {
//        if(factor)
//          factor->permuteWithInverse(inversePermutation);
//      }
//  }
//
//  /* ************************************************************************* */
//  void DiscreteFactorGraph::reduceWithInverse(
//    const internal::Reduction& inverseReduction) {
//      for(const sharedFactor& factor: factors_) {
//        if(factor)
//          factor->reduceWithInverse(inverseReduction);
//      }
//  }
 
  static DecisionTreeFactor ProductAndNormalize(
      const DiscreteFactorGraph& factors) {
    // PRODUCT: multiply all factors
    gttic(product);
    DecisionTreeFactor product = factors.product();
    gttoc(product);
 
    // Max over all the potentials by pretending all keys are frontal:
    auto normalization = product.max(product.size());
 
    // Normalize the product factor to prevent underflow.
    product = product / (*normalization);
 
    return product;
  }
 
  /* ************************************************************************ */
  // Alternate eliminate function for MPE
  std::pair<DiscreteConditional::shared_ptr, DiscreteFactor::shared_ptr>  //
  EliminateForMPE(const DiscreteFactorGraph& factors,
                  const Ordering& frontalKeys) {
    DecisionTreeFactor product = ProductAndNormalize(factors);
 
    // max out frontals, this is the factor on the separator
    gttic(max);
    DecisionTreeFactor::shared_ptr max = product.max(frontalKeys);
    gttoc(max);
 
    // Ordering keys for the conditional so that frontalKeys are really in front
    DiscreteKeys orderedKeys;
    for (auto&& key : frontalKeys)
      orderedKeys.emplace_back(key, product.cardinality(key));
    for (auto&& key : max->keys())
      orderedKeys.emplace_back(key, product.cardinality(key));
 
    // Make lookup with product
    gttic(lookup);
    size_t nrFrontals = frontalKeys.size();
    auto lookup =
        std::make_shared<DiscreteLookupTable>(nrFrontals, orderedKeys, product);
    gttoc(lookup);
 
    return {std::dynamic_pointer_cast<DiscreteConditional>(lookup), max};
  }
 
  /* ************************************************************************ */
  // sumProduct is just an alias for regular eliminateSequential.
  DiscreteBayesNet DiscreteFactorGraph::sumProduct(
      OptionalOrderingType orderingType) const {
    gttic(DiscreteFactorGraph_sumProduct);
    auto bayesNet = eliminateSequential(orderingType);
    return *bayesNet;
  }
 
  DiscreteBayesNet DiscreteFactorGraph::sumProduct(
      const Ordering& ordering) const {
    gttic(DiscreteFactorGraph_sumProduct);
    auto bayesNet = eliminateSequential(ordering);
    return *bayesNet;
  }
 
  /* ************************************************************************ */
  // The max-product solution below is a bit clunky: the elimination machinery
  // does not allow for differently *typed* versions of elimination, so we
  // eliminate into a Bayes Net using the special eliminate function above, and
  // then create the DiscreteLookupDAG after the fact, in linear time.
 
  DiscreteLookupDAG DiscreteFactorGraph::maxProduct(
      OptionalOrderingType orderingType) const {
    gttic(DiscreteFactorGraph_maxProduct);
    auto bayesNet = eliminateSequential(orderingType, EliminateForMPE);
    return DiscreteLookupDAG::FromBayesNet(*bayesNet);
  }
 
  DiscreteLookupDAG DiscreteFactorGraph::maxProduct(
      const Ordering& ordering) const {
    gttic(DiscreteFactorGraph_maxProduct);
    auto bayesNet = eliminateSequential(ordering, EliminateForMPE);
    return DiscreteLookupDAG::FromBayesNet(*bayesNet);
  }
 
  /* ************************************************************************ */
  DiscreteValues DiscreteFactorGraph::optimize(
      OptionalOrderingType orderingType) const {
    gttic(DiscreteFactorGraph_optimize);
    DiscreteLookupDAG dag = maxProduct(orderingType);
    return dag.argmax();
  }
 
  DiscreteValues DiscreteFactorGraph::optimize(
      const Ordering& ordering) const {
    gttic(DiscreteFactorGraph_optimize);
    DiscreteLookupDAG dag = maxProduct(ordering);
    return dag.argmax();
  }
 
  /* ************************************************************************ */
  std::pair<DiscreteConditional::shared_ptr, DiscreteFactor::shared_ptr>  //
  EliminateDiscrete(const DiscreteFactorGraph& factors,
                    const Ordering& frontalKeys) {
    DecisionTreeFactor product = ProductAndNormalize(factors);
 
    // sum out frontals, this is the factor on the separator
    gttic(sum);
    DecisionTreeFactor::shared_ptr sum = product.sum(frontalKeys);
    gttoc(sum);
 
    // Ordering keys for the conditional so that frontalKeys are really in front
    Ordering orderedKeys;
    orderedKeys.insert(orderedKeys.end(), frontalKeys.begin(),
                       frontalKeys.end());
    orderedKeys.insert(orderedKeys.end(), sum->keys().begin(),
                       sum->keys().end());
 
    // now divide product/sum to get conditional
    gttic(divide);
    auto conditional =
        std::make_shared<DiscreteConditional>(product, *sum, orderedKeys);
    gttoc(divide);
 
    return {conditional, sum};
  }
 
  /* ************************************************************************ */
  string DiscreteFactorGraph::markdown(
      const KeyFormatter& keyFormatter,
      const DiscreteFactor::Names& names) const {
    using std::endl;
    std::stringstream ss;
    ss << "`DiscreteFactorGraph` of size " << size() << endl << endl;
    for (size_t i = 0; i < factors_.size(); i++) {
      ss << "factor " << i << ":\n";
      ss << factors_[i]->markdown(keyFormatter, names) << endl;
    }
    return ss.str();
  }
 
  /* ************************************************************************ */
  string DiscreteFactorGraph::html(const KeyFormatter& keyFormatter,
                                   const DiscreteFactor::Names& names) const {
    using std::endl;
    std::stringstream ss;
    ss << "<div><p><tt>DiscreteFactorGraph</tt> of size " << size() << "</p>";
    for (size_t i = 0; i < factors_.size(); i++) {
      ss << "<p>factor " << i << ":</p>";
      ss << factors_[i]->html(keyFormatter, names) << endl;
    }
    return ss.str();
  }
 
  /* ************************************************************************ */
  }  // namespace gtsam