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#ifndef _ONL_OPTIMIZE_H_
#define _ONL_OPTIMIZE_H_
#include <tr1/memory>
#include <set>
#include <string>
#include <cmath>
#include "sparse_vector.h"
struct LearningRateSchedule {
virtual ~LearningRateSchedule();
// returns the learning rate for the kth iteration
virtual double eta(int k) const = 0;
};
// TODO in the Tsoruoaka et al. (ACL 2009) paper, they use N
// to mean the batch size in most places, but it doesn't completely
// make sense to me in the learning rate schedules-- this needs
// to be worked out to make sure they didn't mean corpus size
// in some places and batch size in others (since in the paper they
// only ever work with batch sizes of 1)
struct StandardLearningRate : public LearningRateSchedule {
StandardLearningRate(
size_t batch_size, // batch size, not corpus size!
double eta_0 = 0.2) :
eta_0_(eta_0),
N_(static_cast<double>(batch_size)) {}
virtual double eta(int k) const;
private:
const double eta_0_;
const double N_;
};
struct ExponentialDecayLearningRate : public LearningRateSchedule {
ExponentialDecayLearningRate(
size_t batch_size, // batch size, not corpus size!
double eta_0 = 0.2,
double alpha = 0.85 // recommended by Tsuruoka et al. (ACL 2009)
) : eta_0_(eta_0),
N_(static_cast<double>(batch_size)),
alpha_(alpha) {
assert(alpha > 0);
assert(alpha < 1.0);
}
virtual double eta(int k) const;
private:
const double eta_0_;
const double N_;
const double alpha_;
};
class OnlineOptimizer {
public:
virtual ~OnlineOptimizer();
OnlineOptimizer(const std::tr1::shared_ptr<LearningRateSchedule>& s,
size_t batch_size,
const std::vector<int>& frozen_feats = std::vector<int>())
: N_(batch_size),schedule_(s),k_() {
for (int i = 0; i < frozen_feats.size(); ++i)
frozen_.insert(frozen_feats[i]);
}
void ResetEpoch() { k_ = 0; ResetEpochImpl(); }
void UpdateWeights(const SparseVector<double>& approx_g, int max_feat, SparseVector<double>* weights) {
++k_;
const double eta = schedule_->eta(k_);
UpdateWeightsImpl(eta, approx_g, max_feat, weights);
}
protected:
virtual void ResetEpochImpl();
virtual void UpdateWeightsImpl(const double& eta, const SparseVector<double>& approx_g, int max_feat, SparseVector<double>* weights) = 0;
const size_t N_; // number of training instances per batch
std::set<int> frozen_; // frozen (non-optimizing) features
private:
std::tr1::shared_ptr<LearningRateSchedule> schedule_;
int k_; // iteration count
};
class CumulativeL1OnlineOptimizer : public OnlineOptimizer {
public:
CumulativeL1OnlineOptimizer(const std::tr1::shared_ptr<LearningRateSchedule>& s,
size_t training_instances, double C,
const std::vector<int>& frozen) :
OnlineOptimizer(s, training_instances, frozen), C_(C), u_() {}
protected:
void ResetEpochImpl() { u_ = 0; }
void UpdateWeightsImpl(const double& eta, const SparseVector<double>& approx_g, int max_feat, SparseVector<double>* weights) {
u_ += eta * C_ / N_;
for (SparseVector<double>::const_iterator it = approx_g.begin();
it != approx_g.end(); ++it) {
if (frozen_.count(it->first) == 0)
weights->add_value(it->first, eta * it->second);
}
for (int i = 1; i < max_feat; ++i)
if (frozen_.count(i) == 0) ApplyPenalty(i, weights);
}
private:
void ApplyPenalty(int i, SparseVector<double>* w) {
const double z = w->value(i);
double w_i = z;
double q_i = q_.value(i);
if (w_i > 0.0)
w_i = std::max(0.0, w_i - (u_ + q_i));
else if (w_i < 0.0)
w_i = std::min(0.0, w_i + (u_ - q_i));
q_i += w_i - z;
if (q_i == 0.0)
q_.erase(i);
else
q_.set_value(i, q_i);
if (w_i == 0.0)
w->erase(i);
else
w->set_value(i, w_i);
}
const double C_; // reguarlization strength
double u_;
SparseVector<double> q_;
};
#endif
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