Sampling techniques¶
- DyCors.sampling.LatinHyperCube(m, d)[source]¶
Non-symmetric Latin Hypercube [1].
Data is sampled at the center of the bins.
- Parameters
- mint
Number of sampling points.
- dint
Number of dimensions.
- Returns
- sndarray, shape (m,d,)
Sampling data. \(s \in \mathbb{R}^d : 0 \leq s \leq 1\).
References
- 1
Helton, J C and F J Davis. 2003. Latin Hypercube Sampling and the Propagation of Uncertainty in Analyses of Complex Systems. Reliability Engineering & System Safety 81 (1): 23-69.
- DyCors.sampling.SLatinHyperCube(m, d)[source]¶
Symmetric Latin Hypercube [1].
Data is sampled at the center of the bins.
- Parameters
- mint
Number of sampling points.
- dint
Number of dimensions.
- Returns
- sndarray, shape (m,d,)
Sampling data. \(s \in \mathbb{R}^d : 0 \leq s \leq 1\).
References
- 1
Helton, J C and F J Davis. 2003. Latin Hypercube Sampling and the Propagation of Uncertainty in Analyses of Complex Systems. Reliability Engineering & System Safety 81 (1): 23-69.
- DyCors.sampling.RLatinHyperCube(m, d)[source]¶
Non-symmetric random Latin Hypercube [1].
Data is randomly sampled using a uniform distribution inside each bin.
- Parameters
- mint
Number of sampling points.
- dint
Number of dimensions.
- Returns
- sndarray, shape (m,d,)
Sampling data. \(s \in \mathbb{R}^d : 0 \leq s \leq 1\).
References
- 1
Helton, J C and F J Davis. 2003. Latin Hypercube Sampling and the Propagation of Uncertainty in Analyses of Complex Systems. Reliability Engineering & System Safety 81 (1): 23-69.
- DyCors.sampling.RSLatinHyperCube(m, d)[source]¶
Symmetric random Latin Hypercube [1].
Data is randomly sampled using a uniform distribution inside each bin.
- Parameters
- mint
Number of sampling points.
- dint
Number of dimensions.
- Returns
- sndarray, shape (m,d,)
Sampling data. \(s \in \mathbb{R}^d : 0 \leq s \leq 1\).
References
- 1
Helton, J C and F J Davis. 2003. Latin Hypercube Sampling and the Propagation of Uncertainty in Analyses of Complex Systems. Reliability Engineering & System Safety 81 (1): 23-69.
- DyCors.sampling.ERLatinHyperCube(m, d)[source]¶
Non-symmetric enhanced random Latin Hypercube [1].
Data is randomly sampled using a uniform distribution inside each bin.
- Parameters
- mint
Number of sampling points.
- dint
Number of dimensions.
- Returns
- sndarray, shape (m,d,)
Sampling data. \(s \in \mathbb{R}^d : 0 \leq s \leq 1\).
References
- 1
Beachkofski, B and R Grandhi. 2002. Improved Distributed Hypercube Sampling. 43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Denver, Colorado.
- DyCors.sampling.ERSLatinHyperCube(m, d)[source]¶
Non-symmetric enhanced random symmetric Latin Hypercube [1].
Data is randomly sampled using a uniform distribution inside each bin.
- Parameters
- mint
Number of sampling points.
- dint
Number of dimensions.
- Returns
- sndarray, shape (m,d,)
Sampling data. \(s \in \mathbb{R}^d : 0 \leq s \leq 1\).
References
- 1
Beachkofski, B and R Grandhi. 2002. Improved Distributed Hypercube Sampling. 43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Denver, Colorado.