13 #ifndef MLPACK_METHODS_REGULARIZED_SVD_REGULARIZED_FUNCTION_SVD_HPP
14 #define MLPACK_METHODS_REGULARIZED_SVD_REGULARIZED_FUNCTION_SVD_HPP
44 double Evaluate(
const arma::mat& parameters)
const;
53 double Evaluate(
const arma::mat& parameters,
54 const size_t i)
const;
63 void Gradient(
const arma::mat& parameters,
64 arma::mat& gradient)
const;
70 const arma::mat&
Dataset()
const {
return data; }
82 double Lambda()
const {
return lambda; }
85 size_t Rank()
const {
return rank; }
89 const arma::mat& data;
91 arma::mat initialPoint;
106 namespace optimization {
115 arma::mat& parameters);
double Lambda() const
Return the regularization parameters.
The core includes that mlpack expects; standard C++ includes and Armadillo.
double Optimize(arma::mat &iterate)
Optimize the given function using stochastic gradient descent.
double Evaluate(const arma::mat ¶meters) const
Evaluates the cost function over all examples in the data.
size_t NumItems() const
Return the number of items in the data.
size_t Rank() const
Return the rank used for the factorization.
size_t NumUsers() const
Return the number of users in the data.
void Gradient(const arma::mat ¶meters, arma::mat &gradient) const
Evaluates the full gradient of the cost function over all the training examples.
const arma::mat & Dataset() const
Return the dataset passed into the constructor.
size_t NumFunctions() const
Return the number of training examples. Useful for SGD optimizer.
RegularizedSVDFunction(const arma::mat &data, const size_t rank, const double lambda)
Constructor for RegularizedSVDFunction class.
const arma::mat & GetInitialPoint() const
Return the initial point for the optimization.