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Prepare release 2.3 (#500)
* Add contributor * Fix SGP doc * Bump 2.3.0
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AUTHORS.md

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SMT has been developed thanks to contributions from:
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* Alexandre Thouvenot
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* Andres Lopez Lopera
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* Antoine Averland
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* Emile Roux
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* Ewout ter Hoeven
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* Florent Vergnes

doc/_src_docs/surrogate_models/sgp.rst

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doc/_src_docs/surrogate_models/sgp.rstx

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In SMT the methods: Fully Independent Training Conditional (FITC) method and the Variational Free Energy (VFE) approximation
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are implemented inspired from inference methods developed in the GPy project [2]_
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In practice, the implementation rely on the expression of their respective negative marginal log
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In practice, the implementation relies on the expression of their respective negative marginal log
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likelihood (NMLL), which is minimised to train the methods. We have the following expressions:
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For FITC
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Limitations
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-----------
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* Inducing points location can not be optimized.
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* Inducing points location can not be optimized (a workaround is to provide inducing points as the centroids of k-means clusters over the training data).
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* Trend function is assumed to be zero.
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setup.py

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Programming Language :: C++
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Programming Language :: Python
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Programming Language :: Python :: 3
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Programming Language :: Python :: 3.7
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Programming Language :: Python :: 3.8
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Programming Language :: Python :: Implementation :: CPython
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Topic :: Software Development
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Topic :: Scientific/Engineering
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with respect to the training data. It also includes new surrogate models
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that are not available elsewhere: kriging by partial-least squares reduction
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and energy-minimizing spline interpolation.
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SMT 2.0 adds the capability to handle mixed-variable surrogate models
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and hierarchical variables.
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"""
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extra_compile_args = []

smt/__init__.py

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__version__ = "2.2.1"
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__version__ = "2.3.0"

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