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cff-version: 1.2.0 | ||
message: "If you use this software, please cite it as below." | ||
authors: | ||
- name: "IDAES contributors" | ||
title: "Institute for Design of Advanced Energy Systems (IDAES): advanced computational algorithms to enable the design and optimization of complex, interacting energy and process systems from individual plant components to the entire electrical grid" | ||
url: "https://github.com/IDAES/idaes-pse" | ||
license-url: "https://github.com/IDAES/idaes-pse/blob/main/LICENSE.md" | ||
preferred-citation: | ||
type: article | ||
authors: | ||
- family-names: "Lee" | ||
given-names: "Andrew" | ||
- family-names: "Ghouse" | ||
given-names: "Jaffer H" | ||
- family-names: "Eslick" | ||
given-names: "John C" | ||
- family-names: "Laird" | ||
given-names: "Carl D" | ||
- family-names: "Siirola" | ||
given-names: "John D" | ||
- family-names: "Zamarripa" | ||
given-names: "Miguel A" | ||
- family-names: "Gunter" | ||
given-names: "Dan" | ||
- family-names: "Shinn" | ||
given-names: "John H" | ||
- family-names: "Dowling" | ||
given-names: "Alexander W" | ||
- family-names: "Bhattacharyya" | ||
given-names: "Debangsu" | ||
- family-names: "Biegler" | ||
given-names: "Lorenz T" | ||
- family-names: "Burgard" | ||
given-names: "Anthony P" | ||
- family-names: "Miller" | ||
given-names: "David C" | ||
title: "The IDAES process modeling framework and model library—Flexibility for process simulation and optimization" | ||
journal: "Journal of Advanced Manufacturing and Processing" | ||
publisher: "Wiley Online Library" | ||
volume: 3 | ||
number: 3 | ||
pages: e10095 | ||
year: 2021 | ||
doi: "doi/10.1002/amp2.10095" | ||
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docs/explanations/modeling_extensions/surrogate/sampling/pysmo_custom.rst
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Custom Sampling | ||
=========================================== | ||
With this method, users can explicitly define the distribution for the sampling of each input variable explicitly. | ||
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The ``pysmo.sampling.CustomSampling`` method carries out the user-defined sampling strategy. This can be done in two modes: | ||
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* The samples can be selected from a user-provided dataset, or | ||
* The samples can be generated from a set of provided bounds. | ||
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We currently support three distributions options for sampling: | ||
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* "random", for sampling from a random distribution. | ||
* "uniform", for sampling from a uniform distribution. | ||
* "normal", for sampling from a normal (i.e. Gaussian) distribution. | ||
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.. warning:: | ||
**A note on Gaussian-based sampling** | ||
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To remain consistent with the other sampling methods and distributions, bounds are required for specifying normal distributions, rather than the mean (:math:`\bar{x}`) and standard deviation (:math:`\sigma`). For a normal distribution, 99.7% of the points/sample fall within three standard deviations of the mean. Thus, the bounds of the distribution ay be computed as: | ||
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.. math:: | ||
\begin{equation} | ||
LB = \bar{x} - 3\sigma | ||
\end{equation} | ||
.. math:: | ||
\begin{equation} | ||
UB = \bar{x} + 3\sigma | ||
\end{equation} | ||
While almost all of the points generated will typically fall between LB and UB, a few points may be generated outside the bounds (as should be expected from a normal distribution). However, users can choose to enforce the bounds as hard constraints by setting the boolean option **strictly_enforce_gaussian_bounds** to True during initialization. In that case, values exceeding the bounds are replaced by new values generated from the distributions. However, this may affect the underlying distribution. | ||
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Available Methods | ||
------------------ | ||
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.. autoclass:: idaes.core.surrogate.pysmo.sampling.CustomSampling | ||
:members: __init__, sample_points | ||
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