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#ifndef _INSIDE_H_
#define _INSIDE_H_
#include <vector>
#include <algorithm>
#include "hg.h"
// run the inside algorithm and return the inside score
// if result is non-NULL, result will contain the inside
// score for each node
// NOTE: WeightType(0) must construct the semiring's additive identity
// WeightType(1) must construct the semiring's multiplicative identity
template<typename WeightType, typename WeightFunction>
WeightType Inside(const Hypergraph& hg,
std::vector<WeightType>* result = NULL,
const WeightFunction& weight = WeightFunction()) {
const int num_nodes = hg.nodes_.size();
std::vector<WeightType> dummy;
std::vector<WeightType>& inside_score = result ? *result : dummy;
inside_score.resize(num_nodes);
std::fill(inside_score.begin(), inside_score.end(), WeightType());
for (int i = 0; i < num_nodes; ++i) {
const Hypergraph::Node& cur_node = hg.nodes_[i];
WeightType* const cur_node_inside_score = &inside_score[i];
const int num_in_edges = cur_node.in_edges_.size();
if (num_in_edges == 0) {
*cur_node_inside_score = WeightType(1);
continue;
}
for (int j = 0; j < num_in_edges; ++j) {
const Hypergraph::Edge& edge = hg.edges_[cur_node.in_edges_[j]];
WeightType score = weight(edge);
for (int k = 0; k < edge.tail_nodes_.size(); ++k) {
const int tail_node_index = edge.tail_nodes_[k];
score *= inside_score[tail_node_index];
}
*cur_node_inside_score += score;
}
}
return inside_score.back();
}
template<typename WeightType, typename WeightFunction>
void Outside(const Hypergraph& hg,
std::vector<WeightType>& inside_score,
std::vector<WeightType>* result,
const WeightFunction& weight = WeightFunction()) {
assert(result);
const int num_nodes = hg.nodes_.size();
assert(inside_score.size() == num_nodes);
std::vector<WeightType>& outside_score = *result;
outside_score.resize(num_nodes);
std::fill(outside_score.begin(), outside_score.end(), WeightType(0));
outside_score.back() = WeightType(1);
for (int i = num_nodes - 1; i >= 0; --i) {
const Hypergraph::Node& cur_node = hg.nodes_[i];
const WeightType& head_node_outside_score = outside_score[i];
const int num_in_edges = cur_node.in_edges_.size();
for (int j = 0; j < num_in_edges; ++j) {
const Hypergraph::Edge& edge = hg.edges_[cur_node.in_edges_[j]];
const WeightType head_and_edge_weight = weight(edge) * head_node_outside_score;
const int num_tail_nodes = edge.tail_nodes_.size();
for (int k = 0; k < num_tail_nodes; ++k) {
const int update_tail_node_index = edge.tail_nodes_[k];
WeightType* const tail_outside_score = &outside_score[update_tail_node_index];
WeightType inside_contribution = WeightType(1);
for (int l = 0; l < num_tail_nodes; ++l) {
const int other_tail_node_index = edge.tail_nodes_[l];
if (update_tail_node_index != other_tail_node_index)
inside_contribution *= inside_score[other_tail_node_index];
}
*tail_outside_score += head_and_edge_weight * inside_contribution;
}
}
}
}
// this is the Inside-Outside optimization described in Li et al. (EMNLP 2009)
// for computing the inside algorithm over expensive semirings
// (such as expectations over features). See Figure 4. It is slightly different
// in that x/k is returned not (k,x)
// NOTE: RType * PType must be valid (and yield RType)
template<typename PType, typename WeightFunction, typename RType, typename WeightFunction2>
PType InsideOutside(const Hypergraph& hg,
RType* result_x,
const WeightFunction& weight1 = WeightFunction(),
const WeightFunction2& weight2 = WeightFunction2()) {
const int num_nodes = hg.nodes_.size();
std::vector<PType> inside, outside;
const PType z = Inside<PType,WeightFunction>(hg, &inside, weight1);
Outside<PType,WeightFunction>(hg, inside, &outside, weight1);
RType& x = *result_x;
x = RType();
for (int i = 0; i < num_nodes; ++i) {
const Hypergraph::Node& cur_node = hg.nodes_[i];
const int num_in_edges = cur_node.in_edges_.size();
for (int j = 0; j < num_in_edges; ++j) {
const Hypergraph::Edge& edge = hg.edges_[cur_node.in_edges_[j]];
PType prob = outside[i];
prob *= weight1(edge);
const int num_tail_nodes = edge.tail_nodes_.size();
for (int k = 0; k < num_tail_nodes; ++k)
prob *= inside[edge.tail_nodes_[k]];
prob /= z;
x += weight2(edge) * prob;
}
}
return z;
}
#endif
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