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Diffstat (limited to 'src/inside_outside.h')
| -rw-r--r-- | src/inside_outside.h | 111 |
1 files changed, 111 insertions, 0 deletions
diff --git a/src/inside_outside.h b/src/inside_outside.h new file mode 100644 index 00000000..9114c9d7 --- /dev/null +++ b/src/inside_outside.h @@ -0,0 +1,111 @@ +#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 |