pub(all) suberror CodaError {
  Invalid(String)
  Shape(String)
  Budget(String)
  Numerical(String)
} derive(
@moonbitlang/core/debug.Debug
Debug)

pub struct Basis {
  contrasts : Array[Array[Double]]
  parts : Int
}

pub struct Cholesky {
  lower : Array[Array[Double]]
  logdet : Double
}

pub struct Clusters {
  features : Array[String]
  basis : Basis
  centers : Array[Array[Double]]
  assignments : Array[Int]
  counts : Array[Int]
  inertia : Double
  iterations : Int
  converged : Bool
}

pub struct Dataset {
  features : Array[String]
  samples : Array[String]
  cells : Array[Array[Double]]
}

pub struct Gaussian {
  features : Array[String]
  basis : Basis
  location : Array[Double]
  covariance : Array[Array[Double]]
  factor : Cholesky
  ridge : Double
}

pub struct Medoid {
  sample : String
  index : Int
  objective : Double
}

pub struct Neighbor {
  sample : String
  index : Int
  separation : Double
}

pub struct Pca {
  features : Array[String]
  basis : Basis
  location : Array[Double]
  axes : Array[Array[Double]]
  variances : Array[Double]
  total : Double
}

pub struct Replacement {
  values : Array[Double]
  count : Int
  mass : Double
}

pub struct Spectrum {
  values : Array[Double]
  vectors : Array[Array[Double]]
  residual : Double
}

fn add_pseudocount(x : Array[Double], amount : Double) -> Array[Double] raise CodaError

fn alr(x : Array[Double], reference : Int) -> Array[Double] raise CodaError

fn alr_covariance(data : Dataset, reference : Int, ddof? : Int) -> Array[Array[Double]] raise CodaError

fn alr_inverse(z : Array[Double], reference : Int) -> Array[Double] raise CodaError

fn amalgamate(x : Array[Double], groups : Array[Array[Int]]) -> Array[Double] raise CodaError

fn balance(x : Array[Double], numerator : Array[Int], denominator : Array[Int]) -> Double raise CodaError

fn basis(rows : Array[Array[Double]]) -> Basis raise CodaError

fn center(data : Dataset) -> Array[Double] raise CodaError

fn center_distance(left : Dataset, right : Dataset) -> Double raise CodaError

fn centralize(data : Dataset) -> Dataset raise CodaError

fn cholesky(matrix : Array[Array[Double]]) -> Cholesky raise CodaError

fn closure(x : Array[Double]) -> Array[Double] raise CodaError

fn clr(x : Array[Double]) -> Array[Double] raise CodaError

fn clr_covariance(data : Dataset, ddof? : Int) -> Array[Array[Double]] raise CodaError

fn clr_inverse(z : Array[Double]) -> Array[Double] raise CodaError

fn dataset(features : Array[String], samples : Array[String], rows : Array[Array[Double]]) -> Dataset raise CodaError

fn distance(x : Array[Double], y : Array[Double]) -> Double raise CodaError

fn gaussian(data : Dataset, ridge : Double, basis? : Basis) -> Gaussian raise CodaError

fn geometric_mean(x : Array[Double]) -> Double raise CodaError

fn helmert_basis(parts : Int) -> Basis raise CodaError

fn ilr(x : Array[Double], b : Basis) -> Array[Double] raise CodaError

fn ilr_covariance(data : Dataset, b : Basis, ddof? : Int) -> Array[Array[Double]] raise CodaError

fn ilr_inverse(z : Array[Double], b : Basis) -> Array[Double] raise CodaError

fn inner(x : Array[Double], y : Array[Double]) -> Double raise CodaError

fn interpolate(x : Array[Double], y : Array[Double], t : Double) -> Array[Double] raise CodaError

fn kmeans(data : Dataset, count : Int, max_iterations? : Int) -> Clusters raise CodaError

fn medoid(data : Dataset) -> Medoid raise CodaError

fn multiplicative_replace(x : Array[Double], delta? : Double) -> Replacement raise CodaError

fn nearest(data : Dataset, query : Array[Double], count : Int) -> Array[Neighbor] raise CodaError

fn pairwise_distances(data : Dataset) -> Array[Array[Double]] raise CodaError

fn pca(data : Dataset, components : Int, basis? : Basis) -> Pca raise CodaError

fn permute(x : Array[Double], order : Array[Int]) -> Array[Double] raise CodaError

fn perturb(x : Array[Double], y : Array[Double]) -> Array[Double] raise CodaError

fn perturb_inverse(x : Array[Double], y : Array[Double]) -> Array[Double] raise CodaError

fn positive(x : Array[Double]) -> Unit raise CodaError

fn power(x : Array[Double], exponent : Double) -> Array[Double] raise CodaError

fn process_json(text : String) -> String

fn sbp_basis(partition : Array[Array[Int]]) -> Basis raise CodaError

fn subcomposition(x : Array[Double], indices : Array[Int]) -> Array[Double] raise CodaError

fn symmetric_eigen(matrix : Array[Array[Double]]) -> Spectrum raise CodaError

fn total_variance(data : Dataset, ddof? : Int) -> Double raise CodaError

fn variation_matrix(data : Dataset, ddof? : Int) -> Array[Array[Double]] raise CodaError

fn weighted_center(data : Dataset, weights : Array[Double]) -> Array[Double] raise CodaError

fn weighted_dispersion(data : Dataset, weights : Array[Double]) -> Double raise CodaError