本文整理汇总了C++中context_type类的典型用法代码示例。如果您正苦于以下问题：C++ context_type类的具体用法？C++ context_type怎么用？C++ context_type使用的例子？那么, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了context_type类的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: operator
SPROUT_CONSTEXPR result_type operator()(
Expr const& expr,
context_type const& ctx
) const
{
return sprout::weed::eval(sprout::tuples::get<0>(expr.args()), ctx).success()
? result_type(true, ctx.begin(), attribute_type(), ctx)
: result_type(false, ctx.begin(), attribute_type(), ctx)
;
}示例2: operator
SPROUT_CONSTEXPR result_type operator()(
Expr const& expr,
context_type const& ctx
) const
{
return call(
sprout::tuples::get<0>(expr.args())
.template operator()(ctx.begin(), ctx.end(), ctx),
ctx
);
}示例3: dispatch
virtual void dispatch(context_type ctx,
const std::string & path,
std::string::const_iterator segment_begin,
std::string::const_iterator segment_end)
{
ctx.write_response(response_);
}示例4: call
SPROUT_CONSTEXPR typename std::enable_if<
(sizeof...(Attrs) + 2 < limit::value),
result_type
>::type call(
expr_type const& expr,
context_type const& ctx,
sprout::weed::limited::category limited_category,
Result const& res,
Head const& head,
Attrs const&... attrs
) const
{
return res.success()
? call(
expr,
res.ctx(),
limited_category,
sprout::weed::eval(expr, res.ctx()),
head,
attrs...,
res.attr()
)
: result_type(
true,
ctx.begin(),
sprout::weed::attr_cnv::times<limit::value, attr_type>(head, attrs...),
ctx
)
;
}示例5: call_1
SPROUT_CONSTEXPR result_type call_1(
expr1_type const& expr1,
expr2_type const& expr2,
context_type const& ctx,
sprout::weed::limited::category limited_category,
Result const& res,
Attrs const&... attrs
) const
{
return res.success()
? call(
expr1,
expr2,
res.ctx(),
limited_category,
sprout::weed::eval(expr1, res.ctx()),
attrs...
)
: result_type(
true,
ctx.begin(),
sprout::weed::attr_cnv::modulus<limit::value, attr_type>(attrs...),
ctx
)
;
}示例6: call_1
SPROUT_CONSTEXPR result_type call_1(
typename Expr::args_type const& args,
context_type const& ctx,
Result1 const& res
) const
{
return res.success() && !sprout::weed::eval(sprout::tuples::get<1>(args), ctx).success()
? res
: result_type(false, ctx.begin(), attribute_type(), ctx)
;
}示例7: call_inf
SPROUT_CONSTEXPR result_type call_inf(
expr_type const& expr,
context_type const& ctx,
Result const& res
) const
{
return res.success()
? call_inf(expr, res.ctx(), sprout::weed::eval(expr, res.ctx()))
: result_type(true, ctx.begin(), attribute_type(), ctx)
;
}示例8: dispatch
virtual void dispatch(context_type ctx,
const std::string & path,
std::string::const_iterator segment_begin,
std::string::const_iterator segment_end)
{
boost::shared_ptr<response_type> response(boost::make_shared<response_type>());
response->headers.at<headers::content_type>() = type_;
boost::interprocess::file_mapping mapping((path_ + std::string(segment_end, path.end())).c_str(), boost::interprocess::read_only);
response->body = boost::make_shared<boost::interprocess::mapped_region>(mapping, boost::interprocess::read_only);
ctx.write_response(response);
}示例9: eval
/**
* Evaluates a context, return the conditional probability p(y|x).
*
* This method calculates the conditional probability p(y|x) for given x and y.
*
* @param context A list of pair<string, double> indicates names of
* the contextual predicates and their values which are to be
* evaluated together.
* @param outcome The outcome label for which the conditional probability is
* calculated.
* @return The conditional probability of p(outcome|context).
* \sa eval_all()
*/
double MaxentModel::eval(const context_type& context,
const outcome_type& outcome) const{
size_t oid = m_outcome_map->id(outcome);
if (oid == m_outcome_map->null_id) {
cerr << "[MaxentModel::eval()] unknown outcome id:" << oid << endl;
return 0.0;
}
static vector<double> probs;
if (probs.size() != m_outcome_map->size())
probs.resize(m_outcome_map->size());
fill(probs.begin(), probs.end(), 0.0);
size_t pid;
for (size_t i = 0; i < context.size(); ++i) {
pid = m_pred_map->id(context[i].first);
if (pid != m_pred_map->null_id) {
std::vector<pair<size_t, size_t> >& param = (*m_params)[pid];
float fval = context[i].second;
for(size_t j = 0;j < param.size(); ++j)
probs[param[j].first] += m_theta[param[j].second] * fval;
} else {
//#warning how to deal with unseen predicts?
//m_debug.debug(0,"Predict id %d not found.",i);
}
}
/* For the rationale behind subtracting max_prob from the log-probabilities
see maxentmodel.cpp:maxent::MaxentModel::eval_all*/
// Find the maximum log-prob
double max_prob = numeric_limits<double>::min();
for (size_t i = 0; i < probs.size(); ++i) {
max_prob = max(max_prob, probs[i]);
}
double sum = 0.0;
for (size_t i = 0; i < probs.size(); ++i) {
// Subtract the maximum log-prob from the others to get them in
// the (-inf,0] range.
probs[i] = exp(probs[i] - max_prob);
sum += probs[i];
}
for (size_t i = 0; i < probs.size(); ++i) {
probs[i] /= sum;
}
return probs[oid];
}示例10: renderScene
void WglViewBase::renderScene(context_type &context, camera_type &camera)
{
#ifdef _DEBUG
{
// error-checking routine of OpenGL
const GLenum glErrorCode = glGetError();
if (GL_NO_ERROR != glErrorCode)
std::cerr << "OpenGL error at " << __LINE__ << " in " << __FILE__ << ": " << gluErrorString(glErrorCode) << std::endl;
}
#endif
GLint oldMatrixMode = 0;
glGetIntegerv(GL_MATRIX_MODE, &oldMatrixMode);
if (oldMatrixMode != GL_MODELVIEW) glMatrixMode(GL_MODELVIEW);
{
glPushMatrix();
//
glLoadIdentity();
camera.lookAt();
//
glPushMatrix();
doPrepareRendering(context, camera);
glPopMatrix();
glPushMatrix();
doRenderStockScene(context, camera);
glPopMatrix();
doRenderScene(context, camera);
glPopMatrix();
}
glFlush();
// swap buffers
context.swapBuffer();
if (oldMatrixMode != GL_MODELVIEW) glMatrixMode(oldMatrixMode);
#ifdef _DEBUG
{
// error-checking routine of OpenGL
const GLenum glErrorCode = glGetError();
if (GL_NO_ERROR != glErrorCode)
std::cerr << "OpenGL error at " << __LINE__ << " in " << __FILE__ << ": " << gluErrorString(glErrorCode) << std::endl;
}
#endif
}示例11: call
SPROUT_CONSTEXPR typename std::enable_if<
Infinity,
result_type
>::type call(
expr1_type const& expr1,
expr2_type const& expr2,
context_type const& ctx,
Result const& res
) const
{
return res.success()
? call_inf_1(expr1, expr2, res.ctx(), sprout::weed::eval(expr2, res.ctx()))
: result_type(false, ctx.begin(), attribute_type(), ctx)
;
}示例12: call_2
SPROUT_CONSTEXPR result_type call_2(
typename Expr::args_type const&,
context_type const& ctx,
Result2 const& res
) const
{
return res.success()
? result_type(
true,
res.current(),
sprout::weed::attr_cnv::bitwise_or<attr1_type, attr2_type>(res.attr()),
context_type(ctx, res.current())
)
: result_type(false, ctx.begin(), attribute_type(), ctx)
;
}示例13: call_2
SPROUT_CONSTEXPR result_type call_2(
typename Expr::args_type const&,
context_type const& ctx,
Attr1 const& attr,
Result2 const& res
) const
{
return res.success()
? result_type(
true,
res.current(),
sprout::weed::attr_cnv::shift_left(attr, res.attr()),
context_type(ctx, res.current())
)
: result_type(false, ctx.begin(), attribute_type(), ctx)
;
}示例14: eval_all
/**
* Evaluates a context, return the conditional distribution of the context.
*
* This method calculates the conditional probability p(y|x) for each possible
* outcome tag y.
*
* @param context A list of pair<string, double> indicates the contextual
* predicates and their values (must be >= 0) which are to be
* evaluated together.
* @param outcomes An array of the outcomes paired with it's probability
* predicted by the model (the conditional distribution).
* @param sort_result Whether or not the returned outcome array is sorted
* (larger probability first). Default is true.
*
* TODO: need optimized for large number of outcomes
*
* \sa eval()
*/
void MaxentModel::eval_all(const context_type& context,
std::vector<pair<outcome_type, double> >& outcomes,
bool sort_result) const {
assert(m_params);
//static vector<double> probs; //REMIND remove static here
vector<double> probs;
if (probs.size() != m_outcome_map->size())
probs.resize(m_outcome_map->size());
fill(probs.begin(), probs.end(), 0.0);
size_t pid;
for (size_t i = 0; i < context.size(); ++i) {
pid = m_pred_map->id(context[i].first);
if (pid != m_pred_map->null_id) {
std::vector<pair<size_t, size_t> >& param = (*m_params)[pid];
float fval = context[i].second;
for(size_t j = 0;j < param.size(); ++j)
probs[param[j].first] += m_theta[param[j].second] * fval;
} else {
//#warning how to deal with unseen predicts?
//m_debug.debug(0,"Predict id %d not found.",i);
}
}
double sum = 0.0;
for (size_t i = 0; i < probs.size(); ++i) {
probs[i] = exp(probs[i]);
sum += probs[i];
}
for (size_t i = 0; i < probs.size(); ++i) {
probs[i] /= sum;
}
outcomes.resize(m_outcome_map->size());
for (size_t i = 0;i < outcomes.size(); ++i) {
outcomes[i].first = (*m_outcome_map)[i];
outcomes[i].second = probs[i];
}
if (sort_result)
sort(outcomes.begin(),outcomes.end(), cmp_outcome());
}示例15: call_1
SPROUT_CONSTEXPR result_type call_1(
typename Expr::args_type const& args,
context_type const& ctx,
Result1 const& res
) const
{
return res.success()
? result_type(
true,
res.current(),
sprout::weed::attr_cnv::mem_ptr(
res.attr(), sprout::tuples::get<0>(sprout::tuples::get<1>(args).args())
),
context_type(ctx, res.current())
)
: result_type(false, ctx.begin(), attribute_type(), ctx)
;
}