56 for (
int rep = 0; rep <
n_repeats; ++rep) {
58 uint64_t rep_seed = seed + rep;
70 explicit Folds(
const std::vector<std::vector<int>>& user_folds)
74 folds.emplace_back(user_folds);
82 explicit Folds(
const std::vector<std::vector<std::vector<int>>>& user_folds)
97 size_t rep_idx = 0)
const;
108 size_t rep_idx = 0)
const;
128 const Eigen::MatrixXd& y,
130 size_t rep_idx = 0)
const
135 auto x_test =
subset(x.derived(), test_idx);
136 Eigen::MatrixXd y_test = y(test_idx, Eigen::all);
138 auto x_train =
subset(x.derived(), train_idx);
139 Eigen::MatrixXd y_train = y(train_idx, Eigen::all);
141 return std::make_tuple(x_train, y_train, x_test, y_test);
165 std::vector<std::vector<std::vector<int>>>
185 static std::vector<std::vector<int>> createFolds(
int n_samples,
Manages data partitioning for cross-validation.
std::size_t n_folds
Number of folds.
Folds(const std::vector< std::vector< int > > &user_folds)
Constructor for user-provided folds.
Folds(int n_samples, int n_folds, int n_repeats=1, uint64_t seed=42)
Constructor for generating random folds with optional repetitions.
size_t numEvals() const
Get the total number of folds (repetitions * folds)
size_t numFolds() const
Get the number of folds.
std::vector< int > getTrainingIndices(size_t fold_idx, size_t rep_idx=0) const
Get training indices for a specific fold and repetition.
const std::vector< int > & getTestIndices(size_t fold_idx, size_t rep_idx=0) const
Get test indices for a specific fold and repetition.
size_t numRepetitions() const
Get the number of repetitions.
auto split(Eigen::EigenBase< T > &x, const Eigen::MatrixXd &y, size_t fold_idx, size_t rep_idx=0) const
Split data into training and test sets for a specific fold and repetition.
Folds(const std::vector< std::vector< std::vector< int > > > &user_folds)
Constructor for user-provided repeated folds.
Folds()
Default constructor.
std::vector< std::vector< std::vector< int > > > folds
Indices for each fold in each repetition.
std::size_t n_repeats
Number of repetitions.
Namespace containing SLOPE regression implementation.
T subset(const Eigen::EigenBase< T > &x, const std::vector< int > &indices)
Extract a subset of rows from an Eigen matrix.
Various utility functions.