def instantiate_model(self): m = MultinomialModel(NDieModel(n=6)) return GaussianRandomWalkModel(m, diagonal=False, random_walk_idxs=[1, 2, 4], model_transformation=(from_simplex, to_simplex), scale_mult='n_meas')
def instantiate_model(self): m = MultinomialModel(NDieModel(n=6)) mult = lambda eps: eps['n_meas']**2 return GaussianRandomWalkModel(m, diagonal=True, random_walk_idxs=[1, 2, 4], model_transformation=(from_simplex, to_simplex), scale_mult=mult)
def instantiate_model(self): m = MultinomialModel(NDieModel(n=6)) cov = np.random.random(size=(3, 3)) cov = np.dot(cov, cov.T) return GaussianRandomWalkModel(m, fixed_covariance=cov, diagonal=False, random_walk_idxs=np.s_[:6:2], model_transformation=(from_simplex, to_simplex), scale_mult='n_meas')
def test_indexing(self): model = lambda slice: GaussianRandomWalkModel( MultinomialModel(NDieModel(n=6)), random_walk_idxs=slice) assert (model('all').n_modelparams == 12) assert (model(np.s_[:6]).n_modelparams == 12) assert (model(np.s_[:6:2]).n_modelparams == 9) assert (model([2, 3, 4]).n_modelparams == 9) self.assertRaises(IndexError, model, np.s_[:7]) self.assertRaises(IndexError, model, np.s_[6:]) self.assertRaises(IndexError, model, [1, 2, 8])
def instantiate_model(self): m = BinomialModel(CoinModel()) return GaussianRandomWalkModel(m, fixed_covariance=np.array([0.01]), diagonal=True)