Bayesian Optimization is a pure Python implementation of bayesian global
optimization with gaussian processes.
This is a constrained global optimization package built upon bayesian inference
and gaussian process, that attempts to find the maximum value of an unknown
function in as few iterations as possible. This technique is particularly suited
for optimization of high cost functions, situations where the balance between
exploration and exploitation is important.
WWW: https://github.com/fmfn/BayesianOptimization
This is the FreeBSD Ports Collection. For an easy to use
WEB-based interface to it, please see:
https://www.FreeBSD.org/ports
For general information on the Ports Collection, please see the
FreeBSD Handbook ports section which is available from:
https://www.FreeBSD.org/doc/en_US.ISO8859-1/books/handbook/ports.html
for the latest official version
or:
The ports(7) manual page (man ports).
These will explain how to use ports and packages.
If you would like to search for a port, you can do so easily by
saying (in /usr/ports):
make search name="<name>"
or:
make search key="<keyword>"
which will generate a list of all ports matching <name> or <keyword>.
make search also supports wildcards, such as:
make search name="gtk*"
For information about contributing to FreeBSD ports, please see the Porter's
Handbook, available at:
https://www.FreeBSD.org/doc/en_US.ISO8859-1/books/porters-handbook/
NOTE: This tree will GROW significantly in size during normal usage!
The distribution tar files can and do accumulate in /usr/ports/distfiles,
and the individual ports will also use up lots of space in their work
subdirectories unless you remember to "make clean" after you're done
building a given port. /usr/ports/distfiles can also be periodically
cleaned without ill-effect.
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