def setParameters(self, NoLsTests=1, NoLs=2, NoEnabled=2, BONUS1=10, BONUS2=2, LSs=(MTS_LS1, MTS_LS2, MTS_LS3), **ukwargs):
r"""Set the algorithm parameters.
Arguments:
SR (numpy.ndarray): Search range.
See Also:
:func:`NiaPy.algorithms.basic.de.DifferentialEvolution.setParameters`
"""
DifferentialEvolution.setParameters(self, itype=ukwargs.pop('itype', MtsIndividual), **ukwargs)
self.LSs, self.NoLsTests, self.NoLs, self.NoEnabled = LSs, NoLsTests, NoLs, NoEnabled
self.BONUS1, self.BONUS2 = BONUS1, BONUS2
def setParameters(self, NoGradingRuns=1, NoLs=2, NoEnabled=2, **ukwargs):
r"""Set the algorithm parameters.
**Arguments:**
SR {real} -- Normalized search range
"""
DifferentialEvolution.setParameters(self, **ukwargs)
self.LSs, self.NoGradingRuns, self.NoLs, self.NoEnabled = [
MTS_LS1, MTS_LS2, MTS_LS3
], NoGradingRuns, NoLs, NoEnabled
if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
def setParameters(self, F_l=0.0, F_u=1.0, Tao1=0.4, Tao2=0.2, **ukwargs):
r"""Set the parameters of an algorithm.
Arguments:
F_l (Optional[float]): Scaling factor lower limit.
F_u (Optional[float]): Scaling factor upper limit.
Tao1 (Optional[float]): Change rate for F parameter update.
Tao2 (Optional[float]): Change rate for CR parameter update.
See Also:
* :func:`NiaPy.algorithms.basic.DifferentialEvolution.setParameters`
"""
DifferentialEvolution.setParameters(self, itype=ukwargs.pop('itype', SolutionjDE), **ukwargs)
self.F_l, self.F_u, self.Tao1, self.Tao2 = F_l, F_u, Tao1, Tao2
def typeParameters():
d = DifferentialEvolution.typeParameters()
d['F_l'] = lambda x: isinstance(x, (float, int)) and x > 0
d['F_u'] = lambda x: isinstance(x, (float, int)) and x > 0
d['Tao1'] = lambda x: isinstance(x, (float, int)) and 0 <= x <= 1
d['Tao2'] = lambda x: isinstance(x, (float, int)) and 0 <= x <= 1
return d
def setParameters(self, F_l=0.0, F_u=2.0, Tao1=0.4, Tao2=0.6, **ukwargs):
r"""Set the parameters of an algorithm.
**Arguments:**
F_l {decimal} -- scaling factor lower limit
F_u {decimal} -- scaling factor upper limit
Tao1 {decimal} -- change rate for F parameter update
Tao2 {decimal} -- change rate for CR parameter update
"""
DifferentialEvolution.setParameters(self, **ukwargs)
self.F_l, self.F_u, self.Tao1, self.Tao2 = F_l, F_u, Tao1, Tao2
if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
def getParameters(self):
r"""TODO.
Returns:
Dict[str, Any]: TODO.
"""
d = DifferentialEvolution.getParameters(self)
d.update({
'F_l': self.F_l,
'F_u': self.F_u,
'Tao1': self.Tao1,
'Tao2': self.Tao2
})
return d
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]:
* NoLsTests (Callable[[int], bool]): TODO
* NoLs (Callable[[int], bool]): TODO
* NoEnabled (Callable[[int], bool]): TODO
See Also:
:func:`NiaPy.algorithms.basic.de.DifferentialEvolution.typeParameters`
"""
d = DifferentialEvolution.typeParameters()
d.update({
'NoLsTests': lambda x: isinstance(x, int) and x >= 0,
'NoLs': lambda x: isinstance(x, int) and x >= 0,
'NoEnabled': lambda x: isinstance(x, int) and x > 0
})
return d
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]:
* F_l (Callable[[Union[float, int]], bool])
* F_u (Callable[[Union[float, int]], bool])
* Tao1 (Callable[[Union[float, int]], bool])
* Tao2 (Callable[[Union[float, int]], bool])
See Also:
* :func:`NiaPy.algorithms.basic.DifferentialEvolution.typeParameters`
"""
d = DifferentialEvolution.typeParameters()
d['F_l'] = lambda x: isinstance(x, (float, int)) and x > 0
d['F_u'] = lambda x: isinstance(x, (float, int)) and x > 0
d['Tao1'] = lambda x: isinstance(x, (float, int)) and 0 <= x <= 1
d['Tao2'] = lambda x: isinstance(x, (float, int)) and 0 <= x <= 1
return d
def getParameters(self):
d = DifferentialEvolution.getParameters(self)
# TODO add paramters values
return d
def setParameters(self, **kwargs):
DifferentialEvolution.setParameters(self, **kwargs)
def getParameters(self):
d = DifferentialEvolution.getParameters(self)
# TODO add parameter values to dictionary
return d
def typeParameters():
return DifferentialEvolution.typeParameters()
