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)