Function reference
Base Designs
Generate Random LHD
| base.LatinSquare | Generate a random (n x d) Latin square |
| base.LatinHypercube | Generate a random Latin Hypercube Design |
| base.GoodLatticePoint | Good Lattice Point (GLP) Design |
| base.UniformDesign | Generate a Uniform Design (U-type) |
Hadamard Matrices
Constructions for Hadamard matrices
| hadamard.is_Hadamard | Determine if a matrix is a Hadamard matrix. |
| hadamard.jacobsthal_matrix | Generate a Jacobsthal matrix |
| hadamard.normalize_hadamard | Normalize a Hadamard matrix |
| hadamard.sylvester | Hadamard matrix based on Sylvester’s construction |
| hadamard.paley | Paley Construction |
| hadamard.paley_design | Generate a Paley design |
Maximin LHD
Algebraic Constructions of Maximim LHD
| maximin.best_linear_permutation | Optimal linear permutation value to achieve larger L1-distance for a LHD |
| maximin.EquidistantLHD | Generate an Equidistant Latin Hypercube |
| maximin.leave_one_out | Apply the Leave-one-out Procedure to Generate a Maxmin LHD |
| maximin.maximinLHD | Generate a maximin LHD based on the L1-distance |
Projection Designs
Algebraic Constructions for Optimized Projections of LHD
| projections.best_linear_permutation | Optimal linear permutation value to minimize the uniform projection criterion |
| projections.UniformProLHD | Generate a Uniform Projection Design |
Orthogonal LHD
Algebraic Constructions of Orthogonal LHD
| orthogonal.OLHD_Butler01 | Orthogonal Latin Hypercube Design (OLHD). Based on the construction method of Butler (2001) |
| orthogonal.OLHD_Cioppa07 | Orthogonal Latin Hyercube Design. Based on the construction method of Cioppa and Lucas (2007) |
| orthogonal.OLHD_Lin09 | Orthogonal Latin Hypercube Design. Based on the construction method of Lin et al. (2009) |
| orthogonal.OLHD_Sun10 | Orthogonal Latin Hypercube Design (OLHD). Based on the construction method of Sun et al. (2010) |
| orthogonal.OLHD_Ye98 | Orthogonal Latin Hyercube Design. Based on the construction method of Ye (1998) |
| orthogonal.OA2LHD | Transform an Orthogonal Array (OA) into an LHD |
Criteria
Space-filling and Projection Criterias for LHD
| criteria.Criteria | A class representing a collection of criteria functions. |
| criteria.AvgAbsCor | Calculate the Average Absolute Correlation |
| criteria.coverage | Compute the coverage measure for a design |
| criteria.discrepancy | Discrepancy of a given sample |
| criteria.LqDistance | |
| criteria.MaxAbsCor | Calculate the Maximum Absolute Correlation |
| criteria.MeshRatio | Compute the meshratio criterion for a given design |
| criteria.MaxProCriterion | Calculate the Maximum Projection Criterion |
| criteria.phi_p | Calculate the phi_p Criterion |
| criteria.UniformProCriterion | Calculate the Uniform Projection Criterion |
Helpers
Miscellaneous Utilities for Manipulating LHDs
| helpers.alpha_allowable_perms | Generate alpha-allowable permutations |
| helpers.are_coprime | Check if two integers are coprime |
| helpers.axis_combinations | Generates all unique combinations of columns from the given array, selecting ‘k’ columns at a time. |
| helpers.check_bounds | Check conditions for bounds input |
| helpers.distance_matrix | Distance matrix based on specified distance measure |
| helpers.euler_phi | Euler’s Totient function |
| helpers.generate_primes | Gernate the first n prime numbers |
| helpers.generate_prime_powers | Generate the first n prime powers |
| helpers.is_cyclic | Verify an array is cylic |
| helpers.is_prime | Determine if a number is prime |
| helpers.is_symmetric | Verify is a matrix is symmetric |
| helpers.is_skew_symmetric | Verify is a matrix is skew-symmetric |
| helpers.is_LHD | Verify Latinhypercube sampling conditions |
| helpers.is_balanced_design | Verify a design is balanced |
| helpers.is_OABD | Verify the given array is an \(OABD_{\alpha}(n,s^m)\) |
| helpers.level_permutation | Apply level permutations to a Good lattice point (GLP) design |
| helpers.permute_columns | Randomly permute columns in a numpy ndarray |
| helpers.permute_rows | Randomly permute rows in a numpy ndarray |
| helpers.primes_range | Generate prime numbers from a specified range |
| helpers.replace_values | Replace values in a numpy array based on a provided mapping dictionary |
| helpers.scale | Sample scaling from unit hypercube to different bounds |
| helpers.swap_elements | Swap two random elements in a matrix |
| helpers.totatives | Generate all positive integers less than and coprime to N from [1,N) |
| helpers.verify_generator | Verify generator used to construct good lattice points (GLP) design |
| helpers.WilliamsTransform | Williams Transformation |
| helpers.zero_base | Normalize the columns by subtracting the minimum element of each column |