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#ifndef _ONL_OPTIMIZE_H_
#define _ONL_OPTIMIZE_H_
#include <tr1/memory>
#include <string>
#include <cmath>
#include "sparse_vector.h"
struct LearningRateSchedule {
virtual ~LearningRateSchedule();
// returns the learning rate for iteration k
virtual double eta(int k) const = 0;
};
struct StandardLearningRate : public LearningRateSchedule {
StandardLearningRate(
size_t training_instances,
double eta_0 = 0.2) :
eta_0_(eta_0),
N_(static_cast<double>(training_instances)) {}
virtual double eta(int k) const;
private:
const double eta_0_;
const double N_;
};
struct ExponentialDecayLearningRate : public LearningRateSchedule {
ExponentialDecayLearningRate(
size_t training_instances,
double eta_0 = 0.2,
double alpha = 0.85 // recommended by Tsuruoka et al. (ACL 2009)
) : eta_0_(eta_0),
N_(static_cast<double>(training_instances)),
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 training_instances)
: N_(training_instances),schedule_(s),k_() {}
void UpdateWeights(const SparseVector<double>& approx_g, SparseVector<double>* weights) {
++k_;
const double eta = schedule_->eta(k_);
UpdateWeightsImpl(eta, approx_g, weights);
}
protected:
virtual void UpdateWeightsImpl(const double& eta, const SparseVector<double>& approx_g, SparseVector<double>* weights) = 0;
const size_t N_; // number of training instances
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) :
OnlineOptimizer(s, training_instances), C_(C), u_() {}
protected:
void UpdateWeightsImpl(const double& eta, const SparseVector<double>& approx_g, SparseVector<double>* weights) {
u_ += eta * C_ / N_;
(*weights) += eta * approx_g;
for (SparseVector<double>::const_iterator it = approx_g.begin(); it != approx_g.end(); ++it)
ApplyPenalty(it->first, 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)
w_i = std::max(0.0, w_i - (u_ + q_i));
else
w_i = std::max(0.0, w_i + (u_ - q_i));
q_i += w_i - z;
q_.set_value(i, q_i);
w->set_value(i, w_i);
}
const double C_; // reguarlization strength
double u_;
SparseVector<double> q_;
};
#endif
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