OptimizationFunction.hpp

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#ifndef OPTIMIZATIONFUNCTIONS_HPP_
#define OPTIMIZATIONFUNCTIONS_HPP_

#include <types/MatrixTypes.hpp>

namespace mic {
namespace neural_nets {
namespace optimization {

template <typename eT=float>
class OptimizationFunction {
public:
    OptimizationFunction () { }

    virtual ~OptimizationFunction () { }

    virtual void update(mic::types::MatrixPtr<eT> p_, mic::types::MatrixPtr<eT> dp_, eT learning_rate_, eT decay_ = 0.0) {

        // Calculate the update.
        mic::types::MatrixPtr<eT> delta = calculateUpdate(p_, dp_, learning_rate_);

        //assert(std::isfinite((*delta)[i]));

        // Perform the update: x = x - delta (with optional weight decay).
        for (size_t i=0; i< (size_t)delta->size(); i++) {
            (*p_)[i] = (1.0f - decay_) * (*p_)[i] - (*delta)[i];
        }//: for
    }

    virtual void update(mic::types::MatrixPtr<eT> p_, mic::types::MatrixPtr<eT> x_, mic::types::MatrixPtr<eT> y_, eT learning_rate_ = 0.001) {
        assert(p_->rows() == y_->rows());
        assert(p_->cols() == x_->rows());
        assert(x_->cols() == y_->cols());

        // Calculate the update using hebbian "fire together, wire together".
        mic::types::MatrixPtr<eT> delta = calculateUpdate(x_, y_, learning_rate_);

        // weight += delta;
        (*p_) += (*delta);
    }


    virtual mic::types::MatrixPtr<eT> calculateUpdate(mic::types::MatrixPtr<eT> x_, mic::types::MatrixPtr<eT> dx_, eT learning_rate_) = 0;



};


} //: optimization
} //: neural_nets
} //: mic


#endif /* OPTIMIZATIONFUNCTIONS_HPP_ */