def fit_random(self, ntries=10, rvs_generator=None, nparams=None):

        if nparams is None:
                nparams = self.nparams
        if rvs_generator is None:
            rvs = np.random.uniform(low=-10, high=10, size=(ntries, nparams))
        else:
            rvs = rvs_generator(size=(ntries, nparams))

        results = np.array([np.r_[self.fit_minimal(rv),  rv] for rv in rvs])
        #selct best results and check how many solutions are within 1e-6 of best
        #not sure what leastsq returns
        return results
A collection of :class:`.Statement` objects. This behaves like a
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/miscmodels/nonlinls.py/NonlinearLS.fit_random--crossover with--dataset/ETHPy150Open STIXProject/python-stix/stix/campaign/__init__.py/Campaign.intended_effects