def typeParameters():
r"""TODO.
Returns:
Dict[str, Callable]:
* omega (Callable[[Union[int, float]], bool]): TODO
* mu (Callable[[float], bool]): TODO
* alpha (Callable[[float], bool]): TODO
* S_init (Callable[[Union[float, int]], bool]): TODO
* E_init (Callable[[Union[float, int]], bool]): TODO
* T_min (Callable[[Union[float, int], bool]): TODO
* T_max (Callable[[Union[float, int], bool]): TODO
See Also:
* :func:`NiaPy.algorithms.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({
'omega': lambda x: isinstance(x, (float, int)),
'mu': lambda x: isinstance(x, float) and 0 <= x <= 1,
'alpha': lambda x: isinstance(x, float) and 0 <= x <= 1,
'S_init': lambda x: isinstance(x, (float, int)) and x > 0,
'E_init': lambda x: isinstance(x, (float, int)) and x > 0,
'T_min': lambda x: isinstance(x, (float, int)) and x > 0,
'T_max': lambda x: isinstance(x, (float, int)) and x > 0
})
return d
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]:
* omega (Callable[[Union[int, float]], bool])
* mu (Callable[[float], bool])
* alpha (Callable[[float], bool])
* S_init (Callable[[Union[float, int]], bool])
* E_init (Callable[[Union[float, int]], bool])
* T_min (Callable[[Union[float, int], bool])
* T_max (Callable[[Union[float, int], bool])
See Also:
* :func:`NiaPy.algorithms.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({
'omega': lambda x: isinstance(x, (float, int)),
'mu': lambda x: isinstance(x, float) and 0 <= x <= 1,
'alpha': lambda x: isinstance(x, float) and 0 <= x <= 1,
'S_init': lambda x: isinstance(x, (float, int)) and x > 0,
'E_init': lambda x: isinstance(x, (float, int)) and x > 0,
'T_min': lambda x: isinstance(x, (float, int)) and x > 0,
'T_max': lambda x: isinstance(x, (float, int)) and x > 0
})
return d
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]:
* N_max (Callable[[Union[int, float]], bool])
* V_f (Callable[[Union[int, float]], bool])
* D_max (Callable[[Union[int, float]], bool])
* C_t (Callable[[Union[int, float]], bool])
* W_n (Callable[[Union[int, float]], bool])
* W_f (Callable[[Union[int, float]], bool])
* d_s (Callable[[Union[int, float]], boool])
* nn (Callable[[int], bool])
* Cr (Callable[[float], bool])
* Mu (Callable[[float], bool])
* epsilon (Callable[[float], bool])
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm`
"""
d = Algorithm.typeParameters()
d.update({
'N_max': lambda x: isinstance(x, (int, float)) and x > 0,
'V_f': lambda x: isinstance(x, (int, float)) and x > 0,
'D_max': lambda x: isinstance(x, (int, float)) and x > 0,
'C_t': lambda x: isinstance(x, (int, float)) and x > 0,
'W_n': lambda x: isinstance(x, (int, float)) and x > 0,
'W_f': lambda x: isinstance(x, (int, float)) and x > 0,
'd_s': lambda x: isinstance(x, (int, float)) and x > 0,
'nn': lambda x: isinstance(x, int) and x > 0,
'Cr': lambda x: isinstance(x, float) and 0 <= x <= 1,
'Mu': lambda x: isinstance(x, float) and 0 <= x <= 1,
'epsilon': lambda x: isinstance(x, float) and 0 < x < 1
})
return d
def typeParameters():
r"""Return functions for checking values of parameters.
Returns:
Dict[str, Callable]:
See Also:
* :func:`NiaPy.algorithms.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({
'n_chemotactic':
lambda x: isinstance(x, int) and x > 0,
'n_swim':
lambda x: isinstance(x, int) and x > 0,
'n_reproduction':
lambda x: isinstance(x, int) and x > 0,
'n_elimination':
lambda x: isinstance(x, int) and x > 0,
'prob_elimination':
lambda x: isinstance(x, float) and 0 <= x <= 1,
'step_size':
lambda x: isinstance(x, float) and x > 0,
'd_attract':
lambda x: isinstance(x, float) and x > 0,
'w_attract':
lambda x: isinstance(x, float) and x > 0,
'h_repel':
lambda x: isinstance(x, float) and x > 0,
'w_repel':
lambda x: isinstance(x, float) and x > 0
})
return d
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]:
* NP (Callable[[int], bool]): Checks if number of bees parameter has a proper value.
* m (Callable[[int], bool]): Checks if number of selected sites parameter has a proper value.
* e (Callable[[int], bool]): Checks if number of elite selected sites parameter has a proper value.
* nep (Callable[[int], bool]): Checks if number of elite bees parameter has a proper value.
* nsp (Callable[[int], bool]): Checks if number of other bees parameter has a proper value.
* ngh (Callable[[float], bool]): Checks if size of patches parameter has a proper value.
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({
'NP': lambda x: isinstance(x, int) and x > 0,
'm': lambda x: isinstance(x, int) and x > 0,
'e': lambda x: isinstance(x, int) and x > 0,
'nep': lambda x: isinstance(x, int) and x > 0,
'nsp': lambda x: isinstance(x, int) and x > 0,
'ngh': lambda x: isinstance(x, float) and x > 0
})
return d
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]:
* alpha (Callable): TODO
* gamma (Callable): TODO
* theta (Callable): TODO
* nl (Callable): TODO
* F (Callable[[Union[float, int]], bool]): TODO
* CR (Callable[[Union[float, int]], bool]): TODO
See Also:
* :func:`NiaPy.algorithms.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({
'alpha': lambda x: True,
'gamma': lambda x: True,
'theta': lambda x: True,
'nl': lambda x: True,
'F': lambda x: isinstance(x, (int, float)) and x > 0,
'CR': lambda x: isinstance(x, float) and 0 <= x <= 1
})
return d
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]: TODO
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters`
"""
return Algorithm.typeParameters()
def typeParameters():
r"""Return functions for checking values of parameters.
Returns:
Dict[str, Callable]:
* Limit (Callable[Union[float, numpy.ndarray[float]]]): TODO
See Also:
* :func:`NiaPy.algorithms.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({'Limit': lambda x: isinstance(x, int) and x > 0})
return d
def typeParameters():
r"""Return dict with where key of dict represents parameter name and values represent checking functions for selected parameter.
Returns:
Dict[str, Callable]:
* levy (Callable[[Union[float, int]], bool]): Levy factor.
See Also:
* :func:`NiaPy.algorithms.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({
'levy': lambda x: isinstance(x, (float, int)) and x > 0,
})
return d
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]:
* PAR (Callable[[float], bool]): Checks if partition parameter has a proper value.
* PER (Callable[[float], bool]): Checks if period parameter has a proper value.
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({
'PAR': lambda x: isinstance(x, float) and x > 0,
'PER': lambda x: isinstance(x, float) and x > 0
})
return d
def typeParameters():
r"""TODO.
Returns:
Dict[str, Callable]:
* p (function): TODO
* beta (function): TODO
See Also:
* :func:`NiaPy.algorithms.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({
'p': lambda x: isinstance(x, float) and 0 <= x <= 1,
'beta': lambda x: isinstance(x, (float, int)) and x > 0,
})
return d
def typeParameters():
r"""TODO.
Returns:
Dict[str, Callable]:
* G_0 (Callable[[Union[int, float]], bool]): TODO
* epsilon (Callable[[float], bool]): TODO
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({
'G_0': lambda x: isinstance(x, (int, float)) and x >= 0,
'epsilon': lambda x: isinstance(x, float) and 0 < x < 1
})
return d
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]:
* F (Callable[[Union[float, int]], bool]): Check for correct value of parameter.
* CR (Callable[[float], bool]): Check for correct value of parameter.
See Also:
* :func:`NiaPy.algorithms.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({
'F': lambda x: isinstance(x, (float, int)) and 0 < x <= 2,
'CR': lambda x: isinstance(x, float) and 0 <= x <= 1
})
return d
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]:
* Ts (Callable[[int], bool]): Tournament size.
* Mr (Callable[[float], bool]): Probability of mutation.
* Cr (Callable[[float], bool]): Probability of crossover.
See Also:
* :func:`NiaPy.algorithms.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({
'Ts': lambda x: isinstance(x, int) and x > 1,
'Mr': lambda x: isinstance(x, float) and 0 <= x <= 1,
'Cr': lambda x: isinstance(x, float) and 0 <= x <= 1
})
return d
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]:
* a (Callable[[Union[float, int]], bool]): TODO
* Rmin (Callable[[Union[float, int]], bool]): TODO
* Rmax (Callable[[Union[float, int]], bool]): TODO
See Also:
* :func:`NiaPy.algorithms.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({
'a': lambda x: isinstance(x, (float, int)) and x > 0,
'Rmin': lambda x: isinstance(x, (float, int)),
'Rmax': lambda x: isinstance(x, (float, int))
})
return d
def typeParameters():
r"""TODO.
Returns:
Dict[str, Callable]:
* alpha (Callable[[Union[float, int]], bool]): TODO.
* betamin (Callable[[Union[float, int]], bool]): TODO.
* gamma (Callable[[Union[float, int]], bool]): TODO.
See Also:
* :func:`NiaPy.algorithms.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({
'alpha': lambda x: isinstance(x, (float, int)) and x > 0,
'betamin': lambda x: isinstance(x, (float, int)) and x > 0,
'gamma': lambda x: isinstance(x, (float, int)) and x > 0,
})
return d
def typeParameters():
r"""TODO.
Returns:
Dict[str, Callable]:
* F (func): TODO
* R (func): TODO
* C (func): TODO
* FC (func): TODO
"""
d = Algorithm.typeParameters()
d.update({
'NP': lambda x: isinstance(x, int) and x > 0,
'F': lambda x: isinstance(x, (float, int)) and x > 0,
'R': lambda x: isinstance(x, (float, int)) and x > 0,
'C': lambda x: isinstance(x, int) and x > 0,
'FC': lambda x: isinstance(x, (float, int)) and x > 0
})
return d
def typeParameters():
r"""TODO.
Returns:
Dict[str, Callable]:
* Ts (Callable[[int], bool]): TODO
* Mr (Callable[[float], bool]): Probability of mutation.
* Cr (Callable[[float], bool]): Probability of crossover.
See Also:
* :func:`NiaPy.algorithms.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({
'Ts': lambda x: isinstance(x, int) and x > 1,
'Mr': lambda x: isinstance(x, float) and 0 <= x <= 1,
'Cr': lambda x: isinstance(x, float) and 0 <= x <= 1
})
return d
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]:
* F (Callable[[int], bool])
* R (Callable[[Union[int, float]], bool])
* C (Callable[[Union[int, float]], bool])
* FC (Callable[[Union[int, float]], bool])
"""
d = Algorithm.typeParameters()
d.update({
'NP': lambda x: isinstance(x, int) and x > 0,
'F': lambda x: isinstance(x, (float, int)) and x > 0,
'R': lambda x: isinstance(x, (float, int)) and x > 0,
'C': lambda x: isinstance(x, int) and x > 0,
'FC': lambda x: isinstance(x, (float, int)) and x > 0
})
return d
def typeParameters():
r"""Return dict with where key of dict represents parameter name and values represent checking functions for selected parameter.
Returns:
Dict[str, Callable]:
* A (Callable[[Union[float, int]], bool]): Loudness.
* r (Callable[[Union[float, int]], bool]): Pulse rate.
* Qmin (Callable[[Union[float, int]], bool]): Minimum frequency.
* Qmax (Callable[[Union[float, int]], bool]): Maximum frequency.
See Also:
* :func:`NiaPy.algorithms.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({
'A': lambda x: isinstance(x, (float, int)) and x > 0,
'r': lambda x: isinstance(x, (float, int)) and x > 0,
'Qmin': lambda x: isinstance(x, (float, int)),
'Qmax': lambda x: isinstance(x, (float, int))
})
return d
def typeParameters():
r"""Get dictionary with function for testing correctness of parameters.
Returns:
Dict[str, Callable]:
* alpha (Callable[[Union[int, float]], bool])
* gamma (Callable[[Union[int, float]], bool])
* rho (Callable[[Union[int, float]], bool])
* sigma (Callable[[Union[int, float]], bool])
See Also
* :func:`NiaPy.algorithms.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({
'alpha': lambda x: isinstance(x, (int, float)) and x >= 0,
'gamma': lambda x: isinstance(x, (int, float)) and x >= 0,
'rho': lambda x: isinstance(x, (int, float)),
'sigma': lambda x: isinstance(x, (int, float))
})
return d
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable[[Union[int, float]], bool]]:
* NP (Callable[[int], bool])
* C1 (Callable[[Union[int, float]], bool])
* C2 (Callable[[Union[int, float]], bool])
* w (Callable[[float], bool])
* vMin (Callable[[Union[int, float]], bool])
* vMax (Callable[[Union[int, float], bool])
"""
d = Algorithm.typeParameters()
d.update({
'C1': lambda x: isinstance(x, (int, float)) and x >= 0,
'C2': lambda x: isinstance(x, (int, float)) and x >= 0,
'w': lambda x: isinstance(x, float) and x >= 0,
'vMin': lambda x: isinstance(x, (int, float)),
'vMax': lambda x: isinstance(x, (int, float))
})
return d
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]:
* jumpLength (Callable[[float], bool]): Checks if jump length parameter has a proper value.
* pheromone (Callable[[Union[int, float]], bool]): Checks if pheromone parameter has a proper value.
* evaporation (Callable[[Union[int, float]], bool]): Checks if evaporation parameter has a proper value.
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({
'jumpLength':
lambda x: isinstance(x, float) and 0 < x < 1,
'pheromone':
lambda x: isinstance(x, (float, int)) and x >= 0,
'evaporation':
lambda x: isinstance(x, (float, int)) and 0 <= x < 1,
})
return d
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]:
* lt (Callable[[int], bool]): Checks if life time parameter has a proper value.
* al (Callable[[int], bool]): Checks if area limit parameter has a proper value.
* lsc (Callable[[int], bool]): Checks if local seeding changes parameter has a proper value.
* gsc (Callable[[int], bool]): Checks if global seeding changes parameter has a proper value.
* tr (Callable[[float], bool]): Checks if transfer rate parameter has a proper value.
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({
'lt': lambda x: isinstance(x, int) and x > 0,
'al': lambda x: isinstance(x, int) and x > 0,
'lsc': lambda x: isinstance(x, int) and x > 0,
'gsc': lambda x: isinstance(x, int) and x > 0,
'tr': lambda x: isinstance(x, float) and 0 <= x <= 1,
})
return d
def test_type_parameters_fine(self):
d = Algorithm.typeParameters()
self.assertIsNotNone(d)