Desing and Analysis of Computational Experiments as python toolbox.
Project description
# Description
This project is an adaptation from the work of Hans Bruun Nielsen, Søren Nymand and Lophaven Jacob Søndergaard.
## Notes This is a implementation that relies heavily on linear algebra solvers (least-squares solvers, Cholesky and QR decompositions, etc.). Therefore is strongly advised that your numpy library be integrated to a [BLAS library](http://markus-beuckelmann.de/blog/boosting-numpy-blas.html) (e.g.: Intel-MKL, OpenBLAS, ATLAS, etc.) in order to attain satisfactory performances of calculation.
For the sake of convenience, Anaconda handles the gritty details of how to combine numpy and those libraries natively.
## Installation
To install through PyPi Repository:
pip install pydacefit
To install via conda
conda install -c conda-forge pydace
## Usage
In progress…
## Contact/Talk to me
My e-emal is felipe.lima@eq.ufcg.edu.br. Feel free to contact me anytime.
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