def biasedMixtures(self, role=None, strategy=None, bias=.9): """ Gives mixtures where the input strategy has %bias weight for its role. Probability for that role's remaining strategies is distributed uniformly, as is probability for all strategies of other roles. Returns a list even when a single role & strategy are specified, since the main use case is starting replicator dynamics from several mixtures. """ assert 0 <= bias <= 1, "probabilities must be between zero and one" if self.maxStrategies == 1: return [self.uniformMixture()] if role == None: return flatten([self.biasedMixtures(r, strategy, bias) for r \ in filter(lambda r: self.numStrategies[self.index(r)] \ > 1, self.roles)]) if strategy == None: return flatten([self.biasedMixtures(role, s, bias) for s in \ self.strategies[role]]) i = self.array_index(role, strategy, dtype=float) m = 1. - self.mask - i m /= m.sum(1).reshape(m.shape[0], 1) m[self.index(role)] *= (1. - bias) m += i*bias return [m]
def is_mixed_profile(prof): if not isinstance(prof, h_dict): return False flat = flatten([v.values() for v in prof.values()]) return all([prob >= 0 for prob in flat]) and \ np.allclose(sum(flat), len(prof))
def is_pure_profile(prof): if not isinstance(prof, h_dict): return False flat = flatten([v.values() for v in prof.values()]) return all([isinstance(count, int) and count >= 0 for count in flat])
