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algorithm python » algorithm within (Expand Search), algorithms within (Expand Search), algorithm both (Expand Search)
python function » protein function (Expand Search)
where function » sphere function (Expand Search), gene function (Expand Search), wave function (Expand Search)
algorithms mc » algorithms hamc (Expand Search), algorithms _ (Expand Search), algorithms a (Expand Search)
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<b>Opti2Phase</b>: Python scripts for two-stage focal reducer
Published 2025“…</li></ul><p dir="ltr">The scripts rely on the following Python packages. Where available, repository links are provided:</p><ol><li><b>NumPy</b>, version 1.22.1</li><li><b>SciPy</b>, version 1.7.3</li><li><b>PyGAD</b>, version 3.0.1 — https://pygad.readthedocs.io/en/latest/#</li><li><b>bees-algorithm</b>, version 1.0.2 — https://pypi.org/project/bees-algorithm</li><li><b>KrakenOS</b>, version 1.0.0.19 — https://github.com/Garchupiter/Kraken-Optical-Simulator</li><li><b>matplotlib</b>, version 3.5.2</li></ol><p dir="ltr">All scripts are modular and organized to reflect the design stages described in the manuscript.…”
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An expectation-maximization algorithm for finding noninvadable stationary states.
Published 2020“…<p><i>(a)</i> Noninvadable states by definition can only exist in the region Ω of resource space where the growth rate <i>dN</i><sub><i>i</i></sub>/<i>dt</i> of each species <i>i</i> is zero or negative. …”
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Python implementation of the Trajectory Adaptive Multilevel Sampling algorithm for rare events and improvements
Published 2021“…<div>This directory contains Python 3 scripts implementing the Trajectory Adaptive Multilevel Sampling algorithm (TAMS), a variant of Adaptive Multilevel Splitting (AMS), for the study of rare events. …”
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As for Fig 2, we present failure rates as a function of the cohort size (vertical axis) versus the number of distractors (horizontal axis), for the Smyth and McClave baseline algorithm from [76].
Published 2020“…We note that the middle row is a special case: here, <i>f</i><sub>target</sub> only corresponds to the proportions of the embedded cohort, while “success” for these two panels is defined as recovering maximally diverse cohorts, as this particular algorithm is designed to do. The bottom row shows the same simulations as the middle row, but presents successes and failures when the targets that generated the embedded cohort are applied instead of the balanced targets that are part of the baseline’s objective function. …”
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Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results
Published 2025“…This algorithm conducts a series of procedures: (1) fragmentation of the molecules into functional groups from SMILES, (2) calculation of activity coefficients under predetermined temperature and mole fraction conditions by employing universal quasi-chemical functional group activity coefficient (UNIFAC) model, and (3) regression of NRTL model parameters by employing UNIFAC model simulation results in the differential evolution algorithm (DEA) and Nelder–Mead method (NMM). …”
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