Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Speeding Up OPFython with Numba   A graph-inspired classifier, known as Optimum-Path Forest (OPF), has proven
to be a state-of-the-art algorithm comparable to Logistic Regressors, Support
Vector Machines in a wide variety of tasks. Recently, its Python-based version,
denoted as OPFython, has been proposed to provide a more friendly framework and
a faster prototyping environment. Nevertheless, Python-based algorithms are
slower than their counterpart C-based algorithms, impacting their performance
when confronted with large amounts of data. Therefore, this paper proposed a
simple yet highly efficient speed up using the Numba package, which accelerates
Numpy-based calculations and attempts to increase the algorithm's overall
performance. Experimental results showed that the proposed approach achieved
better results than the na\"ive Python-based OPF and speeded up its distance
measurement calculation.

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The categories of this paper are: plasm-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: acc-phys
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The categories of this paper are: ao-sci
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The categories of this paper are: alg-geom
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The categories of this paper are: atom-ph
3
The categories of this paper are: physics
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The categories of this paper are: hep-lat
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The categories of this paper are: chao-dyn
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The categories of this paper are: stat
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The categories of this paper are: adap-org
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The categories of this paper are: comp-gas
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The categories of this paper are: q-alg
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The categories of this paper are: funct-an
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The categories of this paper are: hep-th
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The categories of this paper are: cmp-lg