NiaPy.util
¶
Module with implementation of utility classess and functions.
-
exception
NiaPy.util.
FesException
(message='Reached the allowed number of the function evaluations!!!')[source]¶ Bases:
Exception
Exception for exceeding number of maximum function evaluations.
- Author:
Klemen Berkovič
- Date:
2018
- License:
MIT
See also
Initialize the exception.
- Parameters
message (Optional[str]) – Message show when this exception is thrown
-
exception
NiaPy.util.
GenException
(message='Reached the allowd number of the algorithm evaluations!!!')[source]¶ Bases:
Exception
Exception for exceeding number of algorithm iterations/generations.
- Author:
Klemen Berkovič
- Date:
2018
- License:
MIT
See also
Initialize the exception.
- Parameters
message (Optional[str]) – Message that is shown when this exceptions is thrown
-
NiaPy.util.
MakeArgParser
()[source]¶ Create/Make pareser for parsing string.
- Parser:
- -a or –algorithm (str):
Name of algorithm to use. Default value is jDE.
- -b or –bech (str):
Name of benchmark to use. Default values is Benchmark.
- -D (int):
Number of dimensions/components usd by benchmark. Default values is 10.
- -nFES (int):
Number of maximum funciton evaluations. Default values is inf.
- -nGEN (int):
Number of maximum algorithm iterations/generations. Default values is inf.
- -NP (int):
Number of inidividuals in population. Default values is 43.
- -r or –runType (str);
- Run type of run. Value can be:
‘’: No output durning the run. Ouput is shown only at the end of algorithm run.
log: Output is shown every time new global best solution is found
plot: Output is shown only at the end of run. Output is shown as graph ploted in mathplotlib. Graph represents convegance of algorithm over run time of algorithm.
Default value is ‘’.
- -seed (list of int or int):
Set the starting seed of algorithm run. If mutiple runs, user can provide list of ints, where each int usd use at new run. Default values is None.
- -optType (str):
- Optimization type of the run. Values can be:
min: For minimaization problems
max: For maximization problems
Default value is min.
- Returns
Parser for parsing arguments from string.
- Return type
ArgumentParser
See also
ArgumentParser
ArgumentParser.add_argument()
-
exception
NiaPy.util.
RefException
(message='Reached the reference point!!!')[source]¶ Bases:
Exception
Exception for exceeding reference value of function/fitness value.
- Author:
Klemen Berkovič
- Date:
2018
- License:
MIT
See also
Initialize the exception.
- Parameters
message (Optional[str]) – Message that is show when this exception is thrown.
-
exception
NiaPy.util.
TimeException
(message='Reached the allowd run time of the algorithm')[source]¶ Bases:
Exception
Exception for exceeding time limit.
- Author:
Klemen Berkovič
- Date:
2018
- License:
MIT
See also
Initialize the exception.
- Parameters
message (Optional[str]) – Message that is show when this exception is thrown.
-
NiaPy.util.
getArgs
(av)[source]¶ Parse arguments form inputed string.
- Parameters
av (str) – String to parse.
- Returns
Where key represents argument name and values it’s value.
- Return type
See also
NiaPy.util.argparser.MakeArgParser()
.ArgumentParser.parse_args()
-
NiaPy.util.
getDictArgs
(argv)[source]¶ Pasre input string.
- Parameters
argv (str) – Input string to parse for argumets
- Returns
Parsed input string
- Return type
See also
NiaPy.utils.getArgs()
-
NiaPy.util.
limitInversRepair
(x, Lower, Upper, **kwargs)[source]¶ Repair solution and put the solution in the random position inside of the bounds of problem.
- Parameters
x (numpy.ndarray) – Solution to check and repair if needed.
Lower (numpy.ndarray) – Lower bounds of search space.
Upper (numpy.ndarray) – Upper bounds of search space.
kwargs (Dict[str, Any]) – Additional arguments.
- Returns
Solution in search space.
- Return type
numpy.ndarray
-
NiaPy.util.
limit_repair
(x, Lower, Upper, **kwargs)[source]¶ Repair solution and put the solution in the random position inside of the bounds of problem.
- Parameters
x (numpy.ndarray) – Solution to check and repair if needed.
Lower (numpy.ndarray) – Lower bounds of search space.
Upper (numpy.ndarray) – Upper bounds of search space.
kwargs (Dict[str, Any]) – Additional arguments.
- Returns
Solution in search space.
- Return type
numpy.ndarray
-
NiaPy.util.
objects2array
(objs)[source]¶ Convert Iterable array or list to NumPy array.
- Parameters
objs (Iterable[Any]) – Array or list to convert.
- Returns
Array of objects.
- Return type
numpy.ndarray
-
NiaPy.util.
randRepair
(x, Lower, Upper, rnd=<module 'numpy.random' from '/home/docs/.pyenv/versions/3.6.12/lib/python3.6/site-packages/numpy/random/__init__.py'>, **kwargs)[source]¶ Repair solution and put the solution in the random position inside of the bounds of problem.
- Parameters
x (numpy.ndarray) – Solution to check and repair if needed.
Lower (numpy.ndarray) – Lower bounds of search space.
Upper (numpy.ndarray) – Upper bounds of search space.
rnd (mtrand.RandomState) – Random generator.
kwargs (Dict[str, Any]) – Additional arguments.
- Returns
Fixed solution.
- Return type
numpy.ndarray
-
NiaPy.util.
reflectRepair
(x, Lower, Upper, **kwargs)[source]¶ Repair solution and put the solution in search space with reflection of how much the solution violates a bound.
- Parameters
x (numpy.ndarray) – Solution to be fixed.
Lower (numpy.ndarray) – Lower bounds of search space.
Upper (numpy.ndarray) – Upper bounds of search space.
kwargs (Dict[str, Any]) – Additional arguments.
- Returns
Fix solution.
- Return type
numpy.ndarray
-
NiaPy.util.
wangRepair
(x, Lower, Upper, **kwargs)[source]¶ Repair solution and put the solution in the random position inside of the bounds of problem.
- Parameters
x (numpy.ndarray) – Solution to check and repair if needed.
Lower (numpy.ndarray) – Lower bounds of search space.
Upper (numpy.ndarray) – Upper bounds of search space.
kwargs (Dict[str, Any]) – Additional arguments.
- Returns
Solution in search space.
- Return type
numpy.ndarray