def test_set_scalar_subset_X(matrix_type, subset_func):
    adata = ad.AnnData(matrix_type(np.zeros((10, 10))))
    orig_X_val = adata.X.copy()
    subset_idx = slice_subset(adata.obs_names)

    adata_subset = adata[subset_idx, :]

    adata_subset.X = 1

    assert adata_subset.is_view
    assert np.all(asarray(adata[subset_idx, :].X) == 1)

    assert asarray((orig_X_val != adata.X)).sum() == mul(*adata_subset.shape)
def update_cost_model(self, x, cost_x):
        """
        Updates the GP used to handle the cost.

        param x: input of the GP for the cost model.
        param x_cost: values of the time cost at the input locations.
        """

        if self.cost_type == 'evaluation_time':
            cost_evals = np.log(np.atleast_2d(np.asarray(cost_x)).T)

            if self.num_updates == 0:
                X_all = x
                costs_all = cost_evals
            else:
                X_all = np.vstack((self.cost_model.model.X,x))
                costs_all = np.vstack((self.cost_model.model.Y,cost_evals))

            self.num_updates += 1
            self.cost_model.updateModel(X_all, costs_all, None, None)