Source code for mot.optimize.base

__author__ = 'Robbert Harms'
__date__ = '2018-08-01'
__maintainer__ = 'Robbert Harms'
__email__ = 'robbert.harms@maastrichtuniversity.nl'
__licence__ = 'LGPL v3'


return_code_labels = {
    0: ['default', 'no return code specified'],
    1: ['found zero', 'sum of squares/evaluation below underflow limit'],
    2: ['converged', 'the relative error in the sum of squares/evaluation is at most tol'],
    3: ['converged', 'the relative error of the parameter vector is at most tol'],
    4: ['converged', 'both errors are at most tol'],
    5: ['trapped', 'by degeneracy; increasing epsilon might help'],
    6: ['exhausted', 'number of function calls exceeding preset patience'],
    7: ['failed', 'ftol<tol: cannot reduce sum of squares any further'],
    8: ['failed', 'xtol<tol: cannot improve approximate solution any further'],
    9: ['failed', 'gtol<tol: cannot improve approximate solution any further'],
    10: ['NaN', 'Function value is not-a-number or infinite'],
    11: ['exhausted', 'temperature decreased to 0.0']
}


class OptimizeResults(dict):
    """Represents the optimization results.

    Attributes:
        x (ndarray): the optimized parameter maps, an (d, p) array with for d problems a value for every p parameters
        status (ndarray): the return codes, an (d,) vector with for d problems the status return code
    """
    def __getattr__(self, name):
        try:
            return self[name]
        except KeyError:
            raise AttributeError(name)

    __setattr__ = dict.__setitem__
    __delattr__ = dict.__delitem__

    def __repr__(self):
        if self.keys():
            m = max(map(len, list(self.keys()))) + 1
            return '\n'.join([k.rjust(m) + ': ' + repr(v)
                              for k, v in sorted(self.items())])
        else:
            return self.__class__.__name__ + "()"

    def __dir__(self):
        return list(self.keys())