(result: Any)

(result: Any)

(line: str)

(rows: &[OhlcvRow], keep_last: bool)

(pages: list[dict[str, Any]], repeated_margin_lines: set[str])

( corr_mat: &DMatrix<f64>, max_num_clusters: usize, repeat: usize, )

(corr_mat: &DMatrix<f64>, repeat: usize)

(columns: &OhlcvColumns)

(n: usize, k: usize)

( start: usize, n: usize, k: usize, current: &mut Vec<usize>, out: &mut Vec<Vec<usize>>, )

( schema: &DecisionSchema, objective: &dyn IntegerObjective, adapter: &dyn SolverAdapter, )

( prices: &DMatrix<f64>, returns_method: ReturnsMethod, resample_by: Option<&str>, )

(close: &[f64], timeline_signal: &[f64])

(corr: &DMatrix<f64>)

(x: &[f64], y: &[f64])

(cov: &DMatrix<f64>)

(covariance: &DMatrix<f64>)

(returns: &DMatrix<f64>)

(returns: &DMatrix<f64>)

(returns: &DMatrix<f64>)

(returns: &DMatrix<f64>)

(prices: &DMatrix<f64>)

(returns: &DMatrix<f64>)

(&self)

()

(start_url: str)

(pages: list[dict[str, Any]])

(df: &DataFrame)

( schema: &TradingTrajectorySchema, step: usize, trades: &mut Vec<i64>, inventory_path: &mut V

( atoms: Vec<A>, cfg: HpcParallelConfig, callback: F, )

(arr: &[f64], enc: &[(f64, char)])

( values: &[Vec<i64>], depth: usize, current: &mut [i64], visit: &mut dyn FnMut(&[i64]) -> Res

( build_classifier: &F, params: &ParamSet, splits: &[(Vec<usize>, Vec<usize>)], x: &[Vec<f64>]

( splits: &[SplitDefinition], evaluator: &mut E, )

( events: Vec<(NaiveDateTime, Event)>, )

(path: Path, out_path: Path | None = None)

( keys: &[String], idx: usize, grid: &BTreeMap<String, Vec<HyperParamValue>>, current: &mut Pa

( param_grid: &BTreeMap<String, Vec<HyperParamValue>>, )

(returns: &DMatrix<f64>, span: usize)

(pdf_path: Path)

(criterion_dir: Path)

(enc: &[(f64, char)], value: f64)

( samples: &[f64], fit_runs: usize, epsilon: f64, max_iter: usize, )

()

(n: usize)

(resample_by: Option<&str>)

(resample_by: Option<&str>)

(resample_by: Option<&str>)

(resample_by: Option<&str>)

(moduleName: string)

(x: &[Vec<f64>], num_values: usize)

(&mut self, trades_path: &str)

( predictor: &F, x: &[Vec<f64>], feature_values: &[Vec<f64>], )

( feature_values: &[Vec<f64>], partial_dep: &[Vec<f64>], )

(&self, price: f64)

( feature_values: &[Vec<f64>], partial_dep: &[Vec<f64>], )

( feature_rows: &[Vec<f64>], variance_thresh: f64, )

( pairs: &[(usize, usize)], predictor: &F, x: &[Vec<f64>], num_values: usize, feature_valu

(&self, price: f64)

(x: &[Vec<f64>], y: &[f64], min_length: usize)

( series: &[f64], model: &str, lags: SadfLags, add_const: bool, )

( build_classifier: F, param_grid: &BTreeMap<String, Vec<HyperParamValue>>, data: SearchData<'_>,

(n: usize)

( corr_mat: &DMatrix<f64>, kept_clusters: &BTreeMap<usize, Vec<usize>>, top_clusters: &BTreeMap<us

(cov: &DMatrix<f64>, indices: &[usize])

(x: f64, samples: &[f64], bandwidth: f64)

(x: &[f64], y: &[f64])

(data: &DMatrix<f64>, k: usize, seed: u64)

(n: usize, clusters: &BTreeMap<usize, Vec<usize>>)

(start: f64, end: f64, n: usize)

()

()

()

()

()

()

()

()

()

()

()

()

()

()

()

()

()

()

()

(rows_per_symbol: int, symbols: list[str])

(streams: usize, events: usize)

(message: &str, start: usize, window: usize)

(matrix: DMatrix<f64>)

(returns: &DMatrix<f64>)

(sorted: &[f64])

(&self)

(n: usize, k: usize)

(v: &[f64])