static void call_op(
void(*operation_ptr)(void),
some& value,
some& context,
std::false_type
)
{
if(operation_ptr)
{
assert(!value.empty());
typedef void(*operation_type)(T&, some&);
operation_type operation = operation_type(operation_ptr);
operation(some_cast<T>(value), context);
}
}
void operation_unary(Parser& parser,
const feature_set_type& feats,
const Theta& theta,
const state_type& state,
const symbol_type& label)
{
state_type state_new = parser.state_allocator_.allocate();
state_new.step() = state.step() + 1;
state_new.next() = state.next();
state_new.unary() = state.unary() + 1;
state_new.operation() = operation_type(operation_type::UNARY, state.operation().closure() + 1);
state_new.label() = label;
state_new.head() = symbol_type::EPSILON;
state_new.span() = state.span();
state_new.stack() = state.stack();
state_new.derivation() = state;
state_new.reduced() = state_type();
state_new.feature_vector() = parser.feature_vector_allocator_.allocate();
state_new.feature_state() = feats.apply(state_new.operation(),
state_new.label(),
state.feature_state(),
*state_new.feature_vector());
const size_type index_operation = theta.index_operation(state_new.operation());
const size_type offset_operation = index_operation * theta.hidden_;
const size_type offset_classification = theta.offset_classification(label);
const size_type offset_category = theta.offset_category(label);
state_new.layer(theta.hidden_) = (theta.Bu_.block(offset_category, 0, theta.hidden_, 1)
+ (theta.Wu_.block(offset_category, 0, theta.hidden_, theta.hidden_)
* state.layer(theta.hidden_))
).array().unaryExpr(model_type::activation());
const double score = (theta.Wc_.block(offset_classification, offset_operation, 1, theta.hidden_) * state_new.layer(theta.hidden_)
+ theta.Bc_.block(offset_classification, index_operation, 1, 1))(0, 0);
state_new.score() = trance::dot_product(theta.Wfe_, *state_new.feature_vector()) + state.score() + score;
parser.agenda_[state_new.step()].push_back(state_new);
}
