Added TableFactor, a discrete factor optimized for sparsity.
ykim742
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dca7a980dc
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/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file TableFactor.cpp
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* @brief discrete factor
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* @date May 4, 2023
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* @author Yoonwoo Kim
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*/
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#include <gtsam/discrete/DecisionTreeFactor.h>
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#include <gtsam/base/FastSet.h>
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#include <gtsam/hybrid/HybridValues.h>
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#include <gtsam/discrete/TableFactor.h>
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#include <gtsam/discrete/SparseDiscreteConditional.h>
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#include <boost/format.hpp>
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#include <utility>
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using namespace std;
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namespace gtsam {
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/* ************************************************************************ */
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TableFactor::TableFactor() {}
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/* ************************************************************************ */
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TableFactor::TableFactor(const DiscreteKeys& dkeys,
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const TableFactor& potentials)
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: DiscreteFactor(dkeys.indices()),
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cardinalities_(potentials .cardinalities_) {
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sparse_table_ = potentials.sparse_table_;
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denominators_ = potentials.denominators_;
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sorted_dkeys_ = discreteKeys();
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sort(sorted_dkeys_.begin(), sorted_dkeys_.end());
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}
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/* ************************************************************************ */
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TableFactor::TableFactor(const DiscreteKeys& dkeys,
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const Eigen::SparseVector<double>& table)
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: DiscreteFactor(dkeys.indices()), sparse_table_(table.size()) {
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sparse_table_ = table;
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double denom = table.size();
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for (const DiscreteKey& dkey : dkeys) {
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cardinalities_.insert(dkey);
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denom /= dkey.second;
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denominators_.insert(std::pair<Key, double>(dkey.first, denom));
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}
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sorted_dkeys_ = discreteKeys();
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sort(sorted_dkeys_.begin(), sorted_dkeys_.end());
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}
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/* ************************************************************************ */
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TableFactor::TableFactor(const SparseDiscreteConditional& c)
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: DiscreteFactor(c.keys()),
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sparse_table_(c.sparse_table_),
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denominators_(c.denominators_) {
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cardinalities_ = c.cardinalities_;
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sorted_dkeys_ = discreteKeys();
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sort(sorted_dkeys_.begin(), sorted_dkeys_.end());
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}
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/* ************************************************************************ */
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Eigen::SparseVector<double> TableFactor::Convert(
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const std::vector<double>& table) {
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Eigen::SparseVector<double> sparse_table(table.size());
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// Count number of nonzero elements in table and reserving the space.
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const uint64_t nnz = std::count_if(table.begin(), table.end(),
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[](uint64_t i) { return i != 0; });
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sparse_table.reserve(nnz);
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for (uint64_t i = 0; i < table.size(); i++) {
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if (table[i] != 0) sparse_table.insert(i) = table[i];
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}
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sparse_table.pruned();
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sparse_table.data().squeeze();
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return sparse_table;
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}
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/* ************************************************************************ */
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Eigen::SparseVector<double> TableFactor::Convert(const std::string& table) {
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// Convert string to doubles.
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std::vector<double> ys;
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std::istringstream iss(table);
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std::copy(std::istream_iterator<double>(iss), std::istream_iterator<double>(),
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std::back_inserter(ys));
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return Convert(ys);
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}
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/* ************************************************************************ */
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bool TableFactor::equals(const DiscreteFactor& other,
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double tol) const {
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if (!dynamic_cast<const TableFactor*>(&other)) {
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return false;
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} else {
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const auto& f(static_cast<const TableFactor&>(other));
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return sparse_table_.isApprox(f.sparse_table_, tol);
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}
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}
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/* ************************************************************************ */
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double TableFactor::operator()(const DiscreteValues& values) const {
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// a b c d => D * (C * (B * (a) + b) + c) + d
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uint64_t idx = 0, card = 1;
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for (auto it = sorted_dkeys_.rbegin(); it != sorted_dkeys_.rend(); ++it) {
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if (values.find(it->first) != values.end()) {
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idx += card * values.at(it->first);
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}
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card *= it->second;
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}
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return sparse_table_.coeff(idx);
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}
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/* ************************************************************************ */
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double TableFactor::findValue(const DiscreteValues& values) const {
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// a b c d => D * (C * (B * (a) + b) + c) + d
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uint64_t idx = 0, card = 1;
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for (auto it = keys_.rbegin(); it != keys_.rend(); ++it) {
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if (values.find(*it) != values.end()) {
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idx += card * values.at(*it);
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}
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card *= cardinality(*it);
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}
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return sparse_table_.coeff(idx);
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}
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/* ************************************************************************ */
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double TableFactor::error(const DiscreteValues& values) const {
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return -log(evaluate(values));
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}
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/* ************************************************************************ */
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double TableFactor::error(const HybridValues& values) const {
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return error(values.discrete());
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}
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/* ************************************************************************ */
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DecisionTreeFactor TableFactor::operator*(const DecisionTreeFactor& f) const {
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return toDecisionTreeFactor() * f;
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}
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/* ************************************************************************ */
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DecisionTreeFactor TableFactor::toDecisionTreeFactor() const {
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DiscreteKeys dkeys = discreteKeys();
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std::vector<double> table;
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for (auto i = 0; i < sparse_table_.size(); i++) {
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table.push_back(sparse_table_.coeff(i));
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}
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DecisionTreeFactor f(dkeys, table);
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return f;
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}
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/* ************************************************************************ */
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TableFactor TableFactor::choose(const DiscreteValues parent_assign,
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DiscreteKeys parent_keys) const {
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if (parent_keys.empty()) return *this;
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// Unique representation of parent values.
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uint64_t unique = 0;
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uint64_t card = 1;
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for (auto it = keys_.rbegin(); it != keys_.rend(); ++it) {
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if (parent_assign.find(*it) != parent_assign.end()) {
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unique += parent_assign.at(*it) * card;
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card *= cardinality(*it);
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}
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}
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// Find child DiscreteKeys
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DiscreteKeys child_dkeys;
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std::sort(parent_keys.begin(), parent_keys.end());
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std::set_difference(sorted_dkeys_.begin(), sorted_dkeys_.end(), parent_keys.begin(),
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parent_keys.end(), std::back_inserter(child_dkeys));
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// Create child sparse table to populate.
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uint64_t child_card = 1;
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for (const DiscreteKey& child_dkey : child_dkeys)
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child_card *= child_dkey.second;
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Eigen::SparseVector<double> child_sparse_table_(child_card);
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child_sparse_table_.reserve(child_card);
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// Populate child sparse table.
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for (SparseIt it(sparse_table_); it; ++it) {
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// Create unique representation of parent keys
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uint64_t parent_unique = uniqueRep(parent_keys, it.index());
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// Populate the table
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if (parent_unique == unique) {
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uint64_t idx = uniqueRep(child_dkeys, it.index());
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child_sparse_table_.insert(idx) = it.value();
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}
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}
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child_sparse_table_.pruned();
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child_sparse_table_.data().squeeze();
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return TableFactor(child_dkeys, child_sparse_table_);
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}
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/* ************************************************************************ */
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double TableFactor::safe_div(const double& a, const double& b) {
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// The use for safe_div is when we divide the product factor by the sum
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// factor. If the product or sum is zero, we accord zero probability to the
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// event.
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return (a == 0 || b == 0) ? 0 : (a / b);
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}
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/* ************************************************************************ */
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void TableFactor::print(const string& s, const KeyFormatter& formatter) const {
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cout << s;
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cout << " f[";
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for (auto&& key : keys())
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cout << boost::format(" (%1%,%2%),") % formatter(key) % cardinality(key);
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cout << " ]" << endl;
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for (SparseIt it(sparse_table_); it; ++it) {
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DiscreteValues assignment = findAssignments(it.index());
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for (auto&& kv : assignment) {
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cout << "(" << formatter(kv.first) << ", " << kv.second << ")";
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}
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cout << " | " << it.value() << " | " << it.index() << endl;
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}
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cout << "number of nnzs: " <<sparse_table_.nonZeros() << endl;
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}
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/* ************************************************************************ */
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TableFactor TableFactor::apply(const TableFactor& f, Binary op) const {
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if (keys_.empty() && sparse_table_.nonZeros() == 0)
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return f;
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else if (f.keys_.empty() && f.sparse_table_.nonZeros() == 0)
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return *this;
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// 1. Identify keys for contract and free modes.
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DiscreteKeys contract_dkeys = contractDkeys(f);
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DiscreteKeys f_free_dkeys = f.freeDkeys(*this);
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DiscreteKeys union_dkeys = unionDkeys(f);
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// 2. Create hash table for input factor f
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unordered_map<uint64_t, AssignValList> map_f =
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f.createMap(contract_dkeys, f_free_dkeys);
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// 3. Initialize multiplied factor.
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uint64_t card = 1;
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for (auto u_dkey : union_dkeys) card *= u_dkey.second;
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Eigen::SparseVector<double> mult_sparse_table(card);
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mult_sparse_table.reserve(card);
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// 3. Multiply.
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for (SparseIt it(sparse_table_); it; ++it) {
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uint64_t contract_unique = uniqueRep(contract_dkeys, it.index());
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if (map_f.find(contract_unique) == map_f.end()) continue;
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for (auto assignVal : map_f[contract_unique]) {
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uint64_t union_idx = unionRep(union_dkeys, assignVal.first, it.index());
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mult_sparse_table.insert(union_idx) = op(it.value(), assignVal.second);
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}
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}
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// 4. Free unused memory.
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mult_sparse_table.pruned();
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mult_sparse_table.data().squeeze();
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// 5. Create union keys and return.
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return TableFactor(union_dkeys, mult_sparse_table);
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}
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/* ************************************************************************ */
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DiscreteKeys TableFactor::contractDkeys(const TableFactor& f) const {
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// Find contract modes.
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DiscreteKeys contract;
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set_intersection(sorted_dkeys_.begin(), sorted_dkeys_.end(),
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f.sorted_dkeys_.begin(), f.sorted_dkeys_.end(),
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back_inserter(contract));
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return contract;
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}
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/* ************************************************************************ */
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DiscreteKeys TableFactor::freeDkeys(const TableFactor& f) const {
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// Find free modes.
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DiscreteKeys free;
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set_difference(sorted_dkeys_.begin(), sorted_dkeys_.end(),
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f.sorted_dkeys_.begin(), f.sorted_dkeys_.end(),
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back_inserter(free));
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return free;
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}
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/* ************************************************************************ */
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DiscreteKeys TableFactor::unionDkeys(const TableFactor& f) const {
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// Find union modes.
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DiscreteKeys union_dkeys;
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set_union(sorted_dkeys_.begin(), sorted_dkeys_.end(),
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f.sorted_dkeys_.begin(), f.sorted_dkeys_.end(),
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back_inserter(union_dkeys));
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return union_dkeys;
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}
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/* ************************************************************************ */
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uint64_t TableFactor::unionRep(const DiscreteKeys& union_keys,
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const DiscreteValues& f_free, const uint64_t idx) const {
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uint64_t union_idx = 0, card = 1;
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for (auto it = union_keys.rbegin(); it != union_keys.rend(); it++) {
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if (f_free.find(it->first) == f_free.end()) {
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union_idx += keyValueForIndex(it->first, idx) * card;
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} else {
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union_idx += f_free.at(it->first) * card;
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}
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card *= it->second;
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}
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return union_idx;
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}
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/* ************************************************************************ */
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unordered_map<uint64_t, TableFactor::AssignValList> TableFactor::createMap(
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const DiscreteKeys& contract, const DiscreteKeys& free) const {
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// 1. Initialize map.
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unordered_map<uint64_t, AssignValList> map_f;
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// 2. Iterate over nonzero elements.
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for (SparseIt it(sparse_table_); it; ++it) {
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// 3. Create unique representation of contract modes.
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uint64_t unique_rep = uniqueRep(contract, it.index());
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// 4. Create assignment for free modes.
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DiscreteValues free_assignments;
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for (auto& key : free) free_assignments[key.first]
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= keyValueForIndex(key.first, it.index());
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// 5. Populate map.
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if (map_f.find(unique_rep) == map_f.end()) {
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map_f[unique_rep] = {make_pair(free_assignments, it.value())};
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} else {
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map_f[unique_rep].push_back(make_pair(free_assignments, it.value()));
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}
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}
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return map_f;
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}
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/* ************************************************************************ */
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uint64_t TableFactor::uniqueRep(const DiscreteKeys& dkeys, const uint64_t idx) const {
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if (dkeys.empty()) return 0;
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uint64_t unique_rep = 0, card = 1;
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for (auto it = dkeys.rbegin(); it != dkeys.rend(); it++) {
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unique_rep += keyValueForIndex(it->first, idx) * card;
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card *= it->second;
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}
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return unique_rep;
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}
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/* ************************************************************************ */
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uint64_t TableFactor::uniqueRep(const DiscreteValues& assignments) const {
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if (assignments.empty()) return 0;
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uint64_t unique_rep = 0, card = 1;
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for (auto it = assignments.rbegin(); it != assignments.rend(); it++) {
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unique_rep += it->second * card;
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card *= cardinalities_.at(it->first);
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}
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return unique_rep;
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}
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/* ************************************************************************ */
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DiscreteValues TableFactor::findAssignments(const uint64_t idx) const {
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DiscreteValues assignment;
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for (Key key : keys_) {
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assignment[key] = keyValueForIndex(key, idx);
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}
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return assignment;
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}
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/* ************************************************************************ */
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TableFactor::shared_ptr TableFactor::combine(
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size_t nrFrontals, Binary op) const {
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if (nrFrontals > size()) {
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throw invalid_argument(
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"TableFactor::combine: invalid number of frontal "
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"keys " +
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to_string(nrFrontals) + ", nr.keys=" + std::to_string(size()));
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}
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// Find remaining keys.
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DiscreteKeys remain_dkeys;
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uint64_t card = 1;
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for (auto i = nrFrontals; i < keys_.size(); i++) {
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remain_dkeys.push_back(discreteKey(i));
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card *= cardinality(keys_[i]);
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}
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// Create combined table.
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Eigen::SparseVector<double> combined_table(card);
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combined_table.reserve(sparse_table_.nonZeros());
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// Populate combined table.
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for (SparseIt it(sparse_table_); it; ++it) {
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uint64_t idx = uniqueRep(remain_dkeys, it.index());
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double new_val = op(combined_table.coeff(idx), it.value());
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combined_table.coeffRef(idx) = new_val;
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}
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// Free unused memory.
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combined_table.pruned();
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combined_table.data().squeeze();
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return std::make_shared<TableFactor>(remain_dkeys, combined_table);
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}
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/* ************************************************************************ */
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TableFactor::shared_ptr TableFactor::combine(
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const Ordering& frontalKeys, Binary op) const {
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if (frontalKeys.size() > size()) {
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throw invalid_argument(
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"TableFactor::combine: invalid number of frontal "
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"keys " +
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std::to_string(frontalKeys.size()) + ", nr.keys=" +
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std::to_string(size()));
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}
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// Find remaining keys.
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DiscreteKeys remain_dkeys;
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uint64_t card = 1;
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for (Key key : keys_) {
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if (std::find(frontalKeys.begin(), frontalKeys.end(), key) ==
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frontalKeys.end()) {
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remain_dkeys.emplace_back(key, cardinality(key));
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card *= cardinality(key);
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}
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}
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// Create combined table.
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Eigen::SparseVector<double> combined_table(card);
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combined_table.reserve(sparse_table_.nonZeros());
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// Populate combined table.
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for (SparseIt it(sparse_table_); it; ++it) {
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uint64_t idx = uniqueRep(remain_dkeys, it.index());
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double new_val = op(combined_table.coeff(idx), it.value());
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combined_table.coeffRef(idx) = new_val;
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}
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// Free unused memory.
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combined_table.pruned();
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combined_table.data().squeeze();
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return std::make_shared<TableFactor>(remain_dkeys, combined_table);
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}
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/* ************************************************************************ */
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size_t TableFactor::keyValueForIndex(Key target_key, uint64_t index) const {
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// http://phrogz.net/lazy-cartesian-product
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return (index / denominators_.at(target_key)) % cardinality(target_key);
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}
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/* ************************************************************************ */
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std::vector<std::pair<DiscreteValues, double>> TableFactor::enumerate()
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const {
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// Get all possible assignments
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std::vector<std::pair<Key, size_t>> pairs = discreteKeys();
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// Reverse to make cartesian product output a more natural ordering.
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std::vector<std::pair<Key, size_t>> rpairs(pairs.rbegin(), pairs.rend());
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const auto assignments = DiscreteValues::CartesianProduct(rpairs);
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// Construct unordered_map with values
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std::vector<std::pair<DiscreteValues, double>> result;
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for (const auto& assignment : assignments) {
|
||||
result.emplace_back(assignment, operator()(assignment));
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
/* ************************************************************************ */
|
||||
DiscreteKeys TableFactor::discreteKeys() const {
|
||||
DiscreteKeys result;
|
||||
for (auto&& key : keys()) {
|
||||
DiscreteKey dkey(key, cardinality(key));
|
||||
if (std::find(result.begin(), result.end(), dkey) == result.end()) {
|
||||
result.push_back(dkey);
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
// Print out header.
|
||||
/* ************************************************************************ */
|
||||
string TableFactor::markdown(const KeyFormatter& keyFormatter,
|
||||
const Names& names) const {
|
||||
stringstream ss;
|
||||
|
||||
// Print out header.
|
||||
ss << "|";
|
||||
for (auto& key : keys()) {
|
||||
ss << keyFormatter(key) << "|";
|
||||
}
|
||||
ss << "value|\n";
|
||||
|
||||
// Print out separator with alignment hints.
|
||||
ss << "|";
|
||||
for (size_t j = 0; j < size(); j++) ss << ":-:|";
|
||||
ss << ":-:|\n";
|
||||
|
||||
// Print out all rows.
|
||||
for (SparseIt it(sparse_table_); it; ++it) {
|
||||
DiscreteValues assignment = findAssignments(it.index());
|
||||
ss << "|";
|
||||
for (auto& key : keys()) {
|
||||
size_t index = assignment.at(key);
|
||||
ss << DiscreteValues::Translate(names, key, index) << "|";
|
||||
}
|
||||
ss << it.value() << "|\n";
|
||||
}
|
||||
return ss.str();
|
||||
}
|
||||
|
||||
/* ************************************************************************ */
|
||||
string TableFactor::html(const KeyFormatter& keyFormatter,
|
||||
const Names& names) const {
|
||||
stringstream ss;
|
||||
|
||||
// Print out preamble.
|
||||
ss << "<div>\n<table class='TableFactor'>\n <thead>\n";
|
||||
|
||||
// Print out header row.
|
||||
ss << " <tr>";
|
||||
for (auto& key : keys()) {
|
||||
ss << "<th>" << keyFormatter(key) << "</th>";
|
||||
}
|
||||
ss << "<th>value</th></tr>\n";
|
||||
|
||||
// Finish header and start body.
|
||||
ss << " </thead>\n <tbody>\n";
|
||||
|
||||
// Print out all rows.
|
||||
for (SparseIt it(sparse_table_); it; ++it) {
|
||||
DiscreteValues assignment = findAssignments(it.index());
|
||||
ss << " <tr>";
|
||||
for (auto& key : keys()) {
|
||||
size_t index = assignment.at(key);
|
||||
ss << "<th>" << DiscreteValues::Translate(names, key, index) << "</th>";
|
||||
}
|
||||
ss << "<td>" << it.value() << "</td>"; // value
|
||||
ss << "</tr>\n";
|
||||
}
|
||||
ss << " </tbody>\n</table>\n</div>";
|
||||
return ss.str();
|
||||
}
|
||||
|
||||
/* ************************************************************************ */
|
||||
TableFactor TableFactor::prune(size_t maxNrAssignments) const {
|
||||
const size_t N = maxNrAssignments;
|
||||
|
||||
// Get the probabilities in the TableFactor so we can threshold.
|
||||
vector<pair<Eigen::Index, double>> probabilities;
|
||||
|
||||
// Store non-zero probabilities along with their indices in a vector.
|
||||
for (SparseIt it(sparse_table_); it; ++it) {
|
||||
probabilities.emplace_back(it.index(), it.value());
|
||||
}
|
||||
|
||||
// The number of probabilities can be lower than max_leaves.
|
||||
if (probabilities.size() <= N) return *this;
|
||||
|
||||
// Sort the vector in descending order based on the element values.
|
||||
sort(probabilities.begin(), probabilities.end(), [] (
|
||||
const std::pair<Eigen::Index, double>& a,
|
||||
const std::pair<Eigen::Index, double>& b) {
|
||||
return a.second > b.second;
|
||||
});
|
||||
|
||||
// Keep the largest N probabilities in the vector.
|
||||
if (probabilities.size() > N) probabilities.resize(N);
|
||||
|
||||
// Create pruned sparse vector.
|
||||
Eigen::SparseVector<double> pruned_vec(sparse_table_.size());
|
||||
pruned_vec.reserve(probabilities.size());
|
||||
|
||||
// Populate pruned sparse vector.
|
||||
for (const auto& prob : probabilities) {
|
||||
pruned_vec.insert(prob.first) = prob.second;
|
||||
}
|
||||
|
||||
// Create pruned decision tree factor and return.
|
||||
return TableFactor(this->discreteKeys(), pruned_vec);
|
||||
}
|
||||
|
||||
/* ************************************************************************ */
|
||||
} // namespace gtsam
|
||||
|
|
@ -0,0 +1,333 @@
|
|||
/* ----------------------------------------------------------------------------
|
||||
|
||||
* 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
|
||||
|
||||
* -------------------------------------------------------------------------- */
|
||||
|
||||
/**
|
||||
* @file TableFactor.h
|
||||
* @date May 4, 2023
|
||||
* @author Yoonwoo Kim
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <gtsam/discrete/DiscreteFactor.h>
|
||||
#include <gtsam/discrete/DiscreteKey.h>
|
||||
#include <gtsam/inference/Ordering.h>
|
||||
|
||||
#include <Eigen/Sparse>
|
||||
#include <algorithm>
|
||||
#include <memory>
|
||||
#include <map>
|
||||
#include <stdexcept>
|
||||
#include <string>
|
||||
#include <utility>
|
||||
#include <vector>
|
||||
|
||||
namespace gtsam {
|
||||
|
||||
class SparseDiscreteConditional;
|
||||
class HybridValues;
|
||||
|
||||
/**
|
||||
* A discrete probabilistic factor optimized for sparsity.
|
||||
*
|
||||
* @ingroup discrete
|
||||
*/
|
||||
class GTSAM_EXPORT TableFactor : public DiscreteFactor {
|
||||
protected:
|
||||
std::map<Key, size_t> cardinalities_;
|
||||
Eigen::SparseVector<double> sparse_table_;
|
||||
|
||||
private:
|
||||
std::map<Key, size_t> denominators_;
|
||||
DiscreteKeys sorted_dkeys_;
|
||||
|
||||
/**
|
||||
* @brief Finds nth entry in the cartesian product of arrays in O(1)
|
||||
* Example)
|
||||
* v0 | v1 | val
|
||||
* 0 | 0 | 10
|
||||
* 0 | 1 | 21
|
||||
* 1 | 0 | 32
|
||||
* 1 | 1 | 43
|
||||
* keyValueForIndex(v1, 2) = 0
|
||||
* @param target_key nth entry's key to find out its assigned value
|
||||
* @param index nth entry in the sparse vector
|
||||
* @return TableFactor
|
||||
*/
|
||||
size_t keyValueForIndex(Key target_key, uint64_t index) const;
|
||||
|
||||
DiscreteKey discreteKey(size_t i) const {
|
||||
return DiscreteKey(keys_[i], cardinalities_.at(keys_[i]));
|
||||
}
|
||||
|
||||
/// Convert probability table given as doubles to SparseVector.
|
||||
static Eigen::SparseVector<double> Convert(const std::vector<double>& table);
|
||||
|
||||
/// Convert probability table given as string to SparseVector.
|
||||
static Eigen::SparseVector<double> Convert(const std::string& table);
|
||||
|
||||
public:
|
||||
// typedefs needed to play nice with gtsam
|
||||
typedef TableFactor This;
|
||||
typedef DiscreteFactor Base; ///< Typedef to base class
|
||||
typedef std::shared_ptr<TableFactor> shared_ptr;
|
||||
typedef Eigen::SparseVector<double>::InnerIterator SparseIt;
|
||||
typedef std::vector<std::pair<DiscreteValues, double>> AssignValList;
|
||||
using Binary = std::function<double(const double, const double)>;
|
||||
|
||||
public:
|
||||
/** The Real ring with addition and multiplication */
|
||||
struct Ring {
|
||||
static inline double zero() { return 0.0; }
|
||||
static inline double one() { return 1.0; }
|
||||
static inline double add(const double& a, const double& b) { return a + b; }
|
||||
static inline double max(const double& a, const double& b) {
|
||||
return std::max(a, b);
|
||||
}
|
||||
static inline double mul(const double& a, const double& b) { return a * b; }
|
||||
static inline double div(const double& a, const double& b) {
|
||||
return (a == 0 || b == 0) ? 0 : (a / b);
|
||||
}
|
||||
static inline double id(const double& x) { return x; }
|
||||
};
|
||||
|
||||
/// @name Standard Constructors
|
||||
/// @{
|
||||
|
||||
/** Default constructor for I/O */
|
||||
TableFactor();
|
||||
|
||||
/** Constructor from DiscreteKeys and TableFactor */
|
||||
TableFactor(const DiscreteKeys& keys, const TableFactor& potentials);
|
||||
|
||||
/** Constructor from sparse_table */
|
||||
TableFactor(const DiscreteKeys& keys,
|
||||
const Eigen::SparseVector<double>& table);
|
||||
|
||||
/** Constructor from doubles */
|
||||
TableFactor(const DiscreteKeys& keys, const std::vector<double>& table)
|
||||
: TableFactor(keys, Convert(table)) {}
|
||||
|
||||
/** Constructor from string */
|
||||
TableFactor(const DiscreteKeys& keys, const std::string& table)
|
||||
: TableFactor(keys, Convert(table)) {}
|
||||
|
||||
/// Single-key specialization
|
||||
template <class SOURCE>
|
||||
TableFactor(const DiscreteKey& key, SOURCE table)
|
||||
: TableFactor(DiscreteKeys{key}, table) {}
|
||||
|
||||
/// Single-key specialization, with vector of doubles.
|
||||
TableFactor(const DiscreteKey& key, const std::vector<double>& row)
|
||||
: TableFactor(DiscreteKeys{key}, row) {}
|
||||
|
||||
/** Construct from a DiscreteTableConditional type */
|
||||
explicit TableFactor(const SparseDiscreteConditional& c);
|
||||
|
||||
/// @}
|
||||
/// @name Testable
|
||||
/// @{
|
||||
|
||||
/// equality
|
||||
bool equals(const DiscreteFactor& other, double tol = 1e-9) const override;
|
||||
|
||||
// print
|
||||
void print(
|
||||
const std::string& s = "TableFactor:\n",
|
||||
const KeyFormatter& formatter = DefaultKeyFormatter) const override;
|
||||
|
||||
// /// @}
|
||||
// /// @name Standard Interface
|
||||
// /// @{
|
||||
|
||||
/// Calculate probability for given values `x`,
|
||||
/// is just look up in TableFactor.
|
||||
double evaluate(const DiscreteValues& values) const {
|
||||
return operator()(values);
|
||||
}
|
||||
|
||||
/// Evaluate probability distribution, sugar.
|
||||
double operator()(const DiscreteValues& values) const override;
|
||||
|
||||
/// Calculate error for DiscreteValues `x`, is -log(probability).
|
||||
double error(const DiscreteValues& values) const;
|
||||
|
||||
/// multiply two TableFactors
|
||||
TableFactor operator*(const TableFactor& f) const {
|
||||
return apply(f, Ring::mul);
|
||||
};
|
||||
|
||||
/// multiple with DecisionTreeFactor
|
||||
DecisionTreeFactor operator*(const DecisionTreeFactor& f) const override;
|
||||
|
||||
static double safe_div(const double& a, const double& b);
|
||||
|
||||
size_t cardinality(Key j) const { return cardinalities_.at(j); }
|
||||
|
||||
/// divide by factor f (safely)
|
||||
TableFactor operator/(const TableFactor& f) const {
|
||||
return apply(f, safe_div);
|
||||
}
|
||||
|
||||
/// Convert into a decisiontree
|
||||
DecisionTreeFactor toDecisionTreeFactor() const override;
|
||||
|
||||
/// Generate TableFactor from TableFactor
|
||||
// TableFactor toTableFactor() const override { return *this; }
|
||||
|
||||
/// Create a TableFactor that is a subset of this TableFactor
|
||||
TableFactor choose(const DiscreteValues assignments,
|
||||
DiscreteKeys parent_keys) const;
|
||||
|
||||
/// Create new factor by summing all values with the same separator values
|
||||
shared_ptr sum(size_t nrFrontals) const {
|
||||
return combine(nrFrontals, Ring::add);
|
||||
}
|
||||
|
||||
/// Create new factor by summing all values with the same separator values
|
||||
shared_ptr sum(const Ordering& keys) const {
|
||||
return combine(keys, Ring::add);
|
||||
}
|
||||
|
||||
/// Create new factor by maximizing over all values with the same separator.
|
||||
shared_ptr max(size_t nrFrontals) const {
|
||||
return combine(nrFrontals, Ring::max);
|
||||
}
|
||||
|
||||
/// Create new factor by maximizing over all values with the same separator.
|
||||
shared_ptr max(const Ordering& keys) const {
|
||||
return combine(keys, Ring::max);
|
||||
}
|
||||
|
||||
/// @}
|
||||
/// @name Advanced Interface
|
||||
/// @{
|
||||
|
||||
/**
|
||||
* Apply binary operator (*this) "op" f
|
||||
* @param f the second argument for op
|
||||
* @param op a binary operator that operates on TableFactor
|
||||
*/
|
||||
TableFactor apply(const TableFactor& f, Binary op) const;
|
||||
|
||||
/// Return keys in contract mode.
|
||||
DiscreteKeys contractDkeys(const TableFactor& f) const;
|
||||
|
||||
/// Return keys in free mode.
|
||||
DiscreteKeys freeDkeys(const TableFactor& f) const;
|
||||
|
||||
/// Return union of DiscreteKeys in two factors.
|
||||
DiscreteKeys unionDkeys(const TableFactor& f) const;
|
||||
|
||||
/// Create unique representation of union modes.
|
||||
uint64_t unionRep(const DiscreteKeys& keys,
|
||||
const DiscreteValues& assign, const uint64_t idx) const;
|
||||
|
||||
/// Create a hash map of input factor with assignment of contract modes as
|
||||
/// keys and vector of hashed assignment of free modes and value as values.
|
||||
std::unordered_map<uint64_t, AssignValList> createMap(
|
||||
const DiscreteKeys& contract, const DiscreteKeys& free) const;
|
||||
|
||||
/// Create unique representation
|
||||
uint64_t uniqueRep(const DiscreteKeys& keys, const uint64_t idx) const;
|
||||
|
||||
/// Create unique representation with DiscreteValues
|
||||
uint64_t uniqueRep(const DiscreteValues& assignments) const;
|
||||
|
||||
/// Find DiscreteValues for corresponding index.
|
||||
DiscreteValues findAssignments(const uint64_t idx) const;
|
||||
|
||||
/// Find value for corresponding DiscreteValues.
|
||||
double findValue(const DiscreteValues& values) const;
|
||||
|
||||
/**
|
||||
* Combine frontal variables using binary operator "op"
|
||||
* @param nrFrontals nr. of frontal to combine variables in this factor
|
||||
* @param op a binary operator that operates on TableFactor
|
||||
* @return shared pointer to newly created TableFactor
|
||||
*/
|
||||
shared_ptr combine(size_t nrFrontals, Binary op) const;
|
||||
|
||||
/**
|
||||
* Combine frontal variables in an Ordering using binary operator "op"
|
||||
* @param nrFrontals nr. of frontal to combine variables in this factor
|
||||
* @param op a binary operator that operates on TableFactor
|
||||
* @return shared pointer to newly created TableFactor
|
||||
*/
|
||||
shared_ptr combine(const Ordering& keys, Binary op) const;
|
||||
|
||||
/// Enumerate all values into a map from values to double.
|
||||
std::vector<std::pair<DiscreteValues, double>> enumerate() const;
|
||||
|
||||
/// Return all the discrete keys associated with this factor.
|
||||
DiscreteKeys discreteKeys() const;
|
||||
|
||||
/**
|
||||
* @brief Prune the decision tree of discrete variables.
|
||||
*
|
||||
* Pruning will set the values to be "pruned" to 0 indicating a 0
|
||||
* probability. An assignment is pruned if it is not in the top
|
||||
* `maxNrAssignments` values.
|
||||
*
|
||||
* A violation can occur if there are more
|
||||
* duplicate values than `maxNrAssignments`. A violation here is the need to
|
||||
* un-prune the decision tree (e.g. all assignment values are 1.0). We could
|
||||
* have another case where some subset of duplicates exist (e.g. for a tree
|
||||
* with 8 assignments we have 1, 1, 1, 1, 0.8, 0.7, 0.6, 0.5), but this is
|
||||
* not a violation since the for `maxNrAssignments=5` the top values are (1,
|
||||
* 0.8).
|
||||
*
|
||||
* @param maxNrAssignments The maximum number of assignments to keep.
|
||||
* @return TableFactor
|
||||
*/
|
||||
TableFactor prune(size_t maxNrAssignments) const;
|
||||
|
||||
/// @}
|
||||
/// @name Wrapper support
|
||||
/// @{
|
||||
|
||||
/**
|
||||
* @brief Render as markdown table
|
||||
*
|
||||
* @param keyFormatter GTSAM-style Key formatter.
|
||||
* @param names optional, category names corresponding to choices.
|
||||
* @return std::string a markdown string.
|
||||
*/
|
||||
std::string markdown(const KeyFormatter& keyFormatter = DefaultKeyFormatter,
|
||||
const Names& names = {}) const override;
|
||||
|
||||
/**
|
||||
* @brief Render as html table
|
||||
*
|
||||
* @param keyFormatter GTSAM-style Key formatter.
|
||||
* @param names optional, category names corresponding to choices.
|
||||
* @return std::string a html string.
|
||||
*/
|
||||
std::string html(const KeyFormatter& keyFormatter = DefaultKeyFormatter,
|
||||
const Names& names = {}) const override;
|
||||
|
||||
/// @}
|
||||
/// @name HybridValues methods.
|
||||
/// @{
|
||||
|
||||
/**
|
||||
* Calculate error for HybridValues `x`, is -log(probability)
|
||||
* Simply dispatches to DiscreteValues version.
|
||||
*/
|
||||
double error(const HybridValues& values) const override;
|
||||
|
||||
/// @}
|
||||
};
|
||||
|
||||
// traits
|
||||
template <>
|
||||
struct traits<TableFactor> : public Testable<TableFactor> {};
|
||||
} // namespace gtsam
|
||||
|
|
@ -0,0 +1,359 @@
|
|||
/* ----------------------------------------------------------------------------
|
||||
|
||||
* 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
|
||||
|
||||
* -------------------------------------------------------------------------- */
|
||||
|
||||
/*
|
||||
* testTableFactor.cpp
|
||||
*
|
||||
* @date Feb 15, 2023
|
||||
* @author Yoonwoo Kim
|
||||
*/
|
||||
|
||||
#include <CppUnitLite/TestHarness.h>
|
||||
#include <gtsam/base/Testable.h>
|
||||
#include <gtsam/base/serializationTestHelpers.h>
|
||||
#include <gtsam/discrete/TableFactor.h>
|
||||
#include <gtsam/discrete/DiscreteDistribution.h>
|
||||
#include <gtsam/discrete/Signature.h>
|
||||
#include <random>
|
||||
#include <chrono>
|
||||
|
||||
using namespace std;
|
||||
using namespace gtsam;
|
||||
|
||||
vector<double> genArr(double dropout, size_t size) {
|
||||
random_device rd;
|
||||
mt19937 g(rd());
|
||||
vector<double> dropoutmask(size); // Chance of 0
|
||||
|
||||
uniform_int_distribution<> dist(1, 9);
|
||||
auto gen = [&dist, &g]() { return dist(g); };
|
||||
generate(dropoutmask.begin(), dropoutmask.end(), gen);
|
||||
|
||||
fill_n(dropoutmask.begin(), dropoutmask.size() * (dropout), 0);
|
||||
shuffle(dropoutmask.begin(), dropoutmask.end(), g);
|
||||
|
||||
return dropoutmask;
|
||||
}
|
||||
|
||||
map<double, pair<chrono::microseconds, chrono::microseconds>>
|
||||
measureTime(DiscreteKeys keys1, DiscreteKeys keys2, size_t size) {
|
||||
vector<double> dropouts = {0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9};
|
||||
map<double, pair<chrono::microseconds, chrono::microseconds>>
|
||||
measured_times;
|
||||
|
||||
for (auto dropout : dropouts) {
|
||||
vector<double> arr1 = genArr(dropout, size);
|
||||
vector<double> arr2 = genArr(dropout, size);
|
||||
TableFactor f1(keys1, arr1);
|
||||
TableFactor f2(keys2, arr2);
|
||||
DecisionTreeFactor f1_dt(keys1, arr1);
|
||||
DecisionTreeFactor f2_dt(keys2, arr2);
|
||||
|
||||
// measure time TableFactor
|
||||
auto tb_start = chrono::high_resolution_clock::now();
|
||||
TableFactor actual = f1 * f2;
|
||||
auto tb_end = chrono::high_resolution_clock::now();
|
||||
auto tb_time_diff = chrono::duration_cast<chrono::microseconds>(tb_end - tb_start);
|
||||
|
||||
// measure time DT
|
||||
auto dt_start = chrono::high_resolution_clock::now();
|
||||
DecisionTreeFactor actual_dt = f1_dt * f2_dt;
|
||||
auto dt_end = chrono::high_resolution_clock::now();
|
||||
auto dt_time_diff = chrono::duration_cast<chrono::microseconds>(dt_end - dt_start);
|
||||
|
||||
bool flag = true;
|
||||
for (auto assignmentVal : actual_dt.enumerate()) {
|
||||
flag = actual_dt(assignmentVal.first) != actual(assignmentVal.first);
|
||||
if (flag) {
|
||||
std::cout << "something is wrong: " << std::endl;
|
||||
assignmentVal.first.print();
|
||||
std::cout << "dt: " << actual_dt(assignmentVal.first) << std::endl;
|
||||
std::cout << "tb: " << actual(assignmentVal.first) << std::endl;
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (flag) break;
|
||||
measured_times[dropout] = make_pair(tb_time_diff, dt_time_diff);
|
||||
}
|
||||
return measured_times;
|
||||
}
|
||||
|
||||
void printTime(map<double, pair<chrono::microseconds, chrono::microseconds>> measured_time) {
|
||||
for (auto&& kv : measured_time) {
|
||||
cout << "dropout: " << kv.first << " | TableFactor time: "
|
||||
<< kv.second.first.count() << " | DecisionTreeFactor time: " << kv.second.second.count()
|
||||
<< endl;
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST( TableFactor, constructors)
|
||||
{
|
||||
// Declare a bunch of keys
|
||||
DiscreteKey X(0,2), Y(1,3), Z(2,2), A(3, 5);
|
||||
|
||||
// Create factors
|
||||
TableFactor f_zeros(A, {0, 0, 0, 0, 1});
|
||||
TableFactor f1(X, {2, 8});
|
||||
TableFactor f2(X & Y, "2 5 3 6 4 7");
|
||||
TableFactor f3(X & Y & Z, "2 5 3 6 4 7 25 55 35 65 45 75");
|
||||
EXPECT_LONGS_EQUAL(1,f1.size());
|
||||
EXPECT_LONGS_EQUAL(2,f2.size());
|
||||
EXPECT_LONGS_EQUAL(3,f3.size());
|
||||
|
||||
DiscreteValues values;
|
||||
values[0] = 1; // x
|
||||
values[1] = 2; // y
|
||||
values[2] = 1; // z
|
||||
values[3] = 4; // a
|
||||
EXPECT_DOUBLES_EQUAL(1, f_zeros(values), 1e-9);
|
||||
EXPECT_DOUBLES_EQUAL(8, f1(values), 1e-9);
|
||||
EXPECT_DOUBLES_EQUAL(7, f2(values), 1e-9);
|
||||
EXPECT_DOUBLES_EQUAL(75, f3(values), 1e-9);
|
||||
|
||||
// Assert that error = -log(value)
|
||||
EXPECT_DOUBLES_EQUAL(-log(f1(values)), f1.error(values), 1e-9);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST(TableFactor, multiplication) {
|
||||
DiscreteKey v0(0, 2), v1(1, 2), v2(2, 2);
|
||||
|
||||
// Multiply with a DiscreteDistribution, i.e., Bayes Law!
|
||||
DiscreteDistribution prior(v1 % "1/3");
|
||||
TableFactor f1(v0 & v1, "1 2 3 4");
|
||||
DecisionTreeFactor expected(v0 & v1, "0.25 1.5 0.75 3");
|
||||
CHECK(assert_equal(expected, static_cast<DecisionTreeFactor>(prior) *
|
||||
f1.toDecisionTreeFactor()));
|
||||
CHECK(assert_equal(expected, f1 * prior));
|
||||
|
||||
// Multiply two factors
|
||||
TableFactor f2(v1 & v2, "5 6 7 8");
|
||||
TableFactor actual = f1 * f2;
|
||||
TableFactor expected2(v0 & v1 & v2, "5 6 14 16 15 18 28 32");
|
||||
CHECK(assert_equal(expected2, actual));
|
||||
|
||||
DiscreteKey A(0, 3), B(1, 2), C(2, 2);
|
||||
TableFactor f_zeros1(A & C, "0 0 0 2 0 3");
|
||||
TableFactor f_zeros2(B & C, "4 0 0 5");
|
||||
TableFactor actual_zeros = f_zeros1 * f_zeros2;
|
||||
TableFactor expected3(A & B & C, "0 0 0 0 0 0 0 10 0 0 0 15");
|
||||
CHECK(assert_equal(expected3, actual_zeros));
|
||||
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST(TableFactor, benchmark) {
|
||||
DiscreteKey A(0, 5), B(1, 2), C(2, 5), D(3, 2), E(4, 5),
|
||||
F(5, 2), G(6, 3), H(7, 2), I(8, 5), J(9, 7), K(10, 2), L(11, 3);
|
||||
|
||||
// 100
|
||||
DiscreteKeys one_1 = {A, B, C, D};
|
||||
DiscreteKeys one_2 = {C, D, E, F};
|
||||
map<double, pair<chrono::microseconds, chrono::microseconds>> time_map_1 =
|
||||
measureTime(one_1, one_2, 100);
|
||||
printTime(time_map_1);
|
||||
// 200
|
||||
DiscreteKeys two_1 = {A, B, C, D, F};
|
||||
DiscreteKeys two_2 = {B, C, D, E, F};
|
||||
map<double, pair<chrono::microseconds, chrono::microseconds>> time_map_2 =
|
||||
measureTime(two_1, two_2, 200);
|
||||
printTime(time_map_2);
|
||||
// 300
|
||||
DiscreteKeys three_1 = {A, B, C, D, G};
|
||||
DiscreteKeys three_2 = {C, D, E, F, G};
|
||||
map<double, pair<chrono::microseconds, chrono::microseconds>> time_map_3 =
|
||||
measureTime(three_1, three_2, 300);
|
||||
printTime(time_map_3);
|
||||
// 400
|
||||
DiscreteKeys four_1 = {A, B, C, D, F, H};
|
||||
DiscreteKeys four_2 = {B, C, D, E, F, H};
|
||||
map<double, pair<chrono::microseconds, chrono::microseconds>> time_map_4 =
|
||||
measureTime(four_1, four_2, 400);
|
||||
printTime(time_map_4);
|
||||
// 500
|
||||
DiscreteKeys five_1 = {A, B, C, D, I};
|
||||
DiscreteKeys five_2 = {C, D, E, F, I};
|
||||
map<double, pair<chrono::microseconds, chrono::microseconds>> time_map_5 =
|
||||
measureTime(five_1, five_2, 500);
|
||||
printTime(time_map_5);
|
||||
// 600
|
||||
DiscreteKeys six_1 = {A, B, C, D, F, G};
|
||||
DiscreteKeys six_2 = {B, C, D, E, F, G};
|
||||
map<double, pair<chrono::microseconds, chrono::microseconds>> time_map_6 =
|
||||
measureTime(six_1, six_2, 600);
|
||||
printTime(time_map_6);
|
||||
// 700
|
||||
DiscreteKeys seven_1 = {A, B, C, D, J};
|
||||
DiscreteKeys seven_2 = {C, D, E, F, J};
|
||||
map<double, pair<chrono::microseconds, chrono::microseconds>> time_map_7 =
|
||||
measureTime(seven_1, seven_2, 700);
|
||||
printTime(time_map_7);
|
||||
// 800
|
||||
DiscreteKeys eight_1 = {A, B, C, D, F, H, K};
|
||||
DiscreteKeys eight_2 = {B, C, D, E, F, H, K};
|
||||
map<double, pair<chrono::microseconds, chrono::microseconds>> time_map_8 =
|
||||
measureTime(eight_1, eight_2, 800);
|
||||
printTime(time_map_8);
|
||||
// 900
|
||||
DiscreteKeys nine_1 = {A, B, C, D, G, L};
|
||||
DiscreteKeys nine_2 = {C, D, E, F, G, L};
|
||||
map<double, pair<chrono::microseconds, chrono::microseconds>> time_map_9 =
|
||||
measureTime(nine_1, nine_2, 900);
|
||||
printTime(time_map_9);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST( TableFactor, sum_max)
|
||||
{
|
||||
DiscreteKey v0(0,3), v1(1,2);
|
||||
TableFactor f1(v0 & v1, "1 2 3 4 5 6");
|
||||
|
||||
TableFactor expected(v1, "9 12");
|
||||
TableFactor::shared_ptr actual = f1.sum(1);
|
||||
CHECK(assert_equal(expected, *actual, 1e-5));
|
||||
|
||||
TableFactor expected2(v1, "5 6");
|
||||
TableFactor::shared_ptr actual2 = f1.max(1);
|
||||
CHECK(assert_equal(expected2, *actual2));
|
||||
|
||||
TableFactor f2(v1 & v0, "1 2 3 4 5 6");
|
||||
TableFactor::shared_ptr actual22 = f2.sum(1);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
// Check enumerate yields the correct list of assignment/value pairs.
|
||||
TEST(TableFactor, enumerate) {
|
||||
DiscreteKey A(12, 3), B(5, 2);
|
||||
TableFactor f(A & B, "1 2 3 4 5 6");
|
||||
auto actual = f.enumerate();
|
||||
std::vector<std::pair<DiscreteValues, double>> expected;
|
||||
DiscreteValues values;
|
||||
for (size_t a : {0, 1, 2}) {
|
||||
for (size_t b : {0, 1}) {
|
||||
values[12] = a;
|
||||
values[5] = b;
|
||||
expected.emplace_back(values, f(values));
|
||||
}
|
||||
}
|
||||
EXPECT(actual == expected);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
// Check pruning of the decision tree works as expected.
|
||||
TEST(TableFactor, Prune) {
|
||||
DiscreteKey A(1, 2), B(2, 2), C(3, 2);
|
||||
TableFactor f(A & B & C, "1 5 3 7 2 6 4 8");
|
||||
|
||||
// Only keep the leaves with the top 5 values.
|
||||
size_t maxNrAssignments = 5;
|
||||
auto pruned5 = f.prune(maxNrAssignments);
|
||||
|
||||
// Pruned leaves should be 0
|
||||
TableFactor expected(A & B & C, "0 5 0 7 0 6 4 8");
|
||||
EXPECT(assert_equal(expected, pruned5));
|
||||
|
||||
// Check for more extreme pruning where we only keep the top 2 leaves
|
||||
maxNrAssignments = 2;
|
||||
auto pruned2 = f.prune(maxNrAssignments);
|
||||
TableFactor expected2(A & B & C, "0 0 0 7 0 0 0 8");
|
||||
EXPECT(assert_equal(expected2, pruned2));
|
||||
|
||||
DiscreteKey D(4, 2);
|
||||
TableFactor factor(
|
||||
D & C & B & A,
|
||||
"0.0 0.0 0.0 0.60658897 0.61241912 0.61241969 0.61247685 0.61247742 0.0 "
|
||||
"0.0 0.0 0.99995287 1.0 1.0 1.0 1.0");
|
||||
|
||||
TableFactor expected3(
|
||||
D & C & B & A,
|
||||
"0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 "
|
||||
"0.999952870000 1.0 1.0 1.0 1.0");
|
||||
maxNrAssignments = 5;
|
||||
auto pruned3 = factor.prune(maxNrAssignments);
|
||||
EXPECT(assert_equal(expected3, pruned3));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
// Check markdown representation looks as expected.
|
||||
TEST(TableFactor, markdown) {
|
||||
DiscreteKey A(12, 3), B(5, 2);
|
||||
TableFactor f(A & B, "1 2 3 4 5 6");
|
||||
string expected =
|
||||
"|A|B|value|\n"
|
||||
"|:-:|:-:|:-:|\n"
|
||||
"|0|0|1|\n"
|
||||
"|0|1|2|\n"
|
||||
"|1|0|3|\n"
|
||||
"|1|1|4|\n"
|
||||
"|2|0|5|\n"
|
||||
"|2|1|6|\n";
|
||||
auto formatter = [](Key key) { return key == 12 ? "A" : "B"; };
|
||||
string actual = f.markdown(formatter);
|
||||
EXPECT(actual == expected);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
// Check markdown representation with a value formatter.
|
||||
TEST(TableFactor, markdownWithValueFormatter) {
|
||||
DiscreteKey A(12, 3), B(5, 2);
|
||||
TableFactor f(A & B, "1 2 3 4 5 6");
|
||||
string expected =
|
||||
"|A|B|value|\n"
|
||||
"|:-:|:-:|:-:|\n"
|
||||
"|Zero|-|1|\n"
|
||||
"|Zero|+|2|\n"
|
||||
"|One|-|3|\n"
|
||||
"|One|+|4|\n"
|
||||
"|Two|-|5|\n"
|
||||
"|Two|+|6|\n";
|
||||
auto keyFormatter = [](Key key) { return key == 12 ? "A" : "B"; };
|
||||
TableFactor::Names names{{12, {"Zero", "One", "Two"}},
|
||||
{5, {"-", "+"}}};
|
||||
string actual = f.markdown(keyFormatter, names);
|
||||
EXPECT(actual == expected);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
// Check html representation with a value formatter.
|
||||
TEST(TableFactor, htmlWithValueFormatter) {
|
||||
DiscreteKey A(12, 3), B(5, 2);
|
||||
TableFactor f(A & B, "1 2 3 4 5 6");
|
||||
string expected =
|
||||
"<div>\n"
|
||||
"<table class='TableFactor'>\n"
|
||||
" <thead>\n"
|
||||
" <tr><th>A</th><th>B</th><th>value</th></tr>\n"
|
||||
" </thead>\n"
|
||||
" <tbody>\n"
|
||||
" <tr><th>Zero</th><th>-</th><td>1</td></tr>\n"
|
||||
" <tr><th>Zero</th><th>+</th><td>2</td></tr>\n"
|
||||
" <tr><th>One</th><th>-</th><td>3</td></tr>\n"
|
||||
" <tr><th>One</th><th>+</th><td>4</td></tr>\n"
|
||||
" <tr><th>Two</th><th>-</th><td>5</td></tr>\n"
|
||||
" <tr><th>Two</th><th>+</th><td>6</td></tr>\n"
|
||||
" </tbody>\n"
|
||||
"</table>\n"
|
||||
"</div>";
|
||||
auto keyFormatter = [](Key key) { return key == 12 ? "A" : "B"; };
|
||||
TableFactor::Names names{{12, {"Zero", "One", "Two"}},
|
||||
{5, {"-", "+"}}};
|
||||
string actual = f.html(keyFormatter, names);
|
||||
EXPECT(actual == expected);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
int main() {
|
||||
TestResult tr;
|
||||
return TestRegistry::runAllTests(tr);
|
||||
}
|
||||
/* ************************************************************************* */
|
||||
Loading…
Reference in New Issue