def test_view_delitem(attr):
    adata = gen_adata((10, 10))
    getattr(adata, attr)["to_delete"] = np.ones((10, 10))
    # Shouldn’t be a subclass, should be an ndarray
    assert type(getattr(adata, attr)["to_delete"]) is np.ndarray
    view = adata[5:7, :][:, :5]
    adata_hash = joblib.hash(adata)
    view_hash = joblib.hash(view)

    getattr(view, attr).__delitem__("to_delete")

    assert not view.is_view
    assert "to_delete" not in getattr(view, attr)
    assert "to_delete" in getattr(adata, attr)
    assert adata_hash == joblib.hash(adata)
    assert view_hash != joblib.hash(view)
def _sparse_rmsprop_update_numpy(self, var, gindexs, gvalues, mg, rms, mom,
                                   lr, decay, momentum, centered):
    mg_t = copy.deepcopy(mg)
    rms_t = copy.deepcopy(rms)
    mom_t = copy.deepcopy(mom)
    var_t = copy.deepcopy(var)
    for i in range(len(gindexs)):
      gindex = gindexs[i]
      gvalue = gvalues[i]
      rms_t[gindex] = rms[gindex] * decay + (1 - decay) * gvalue * gvalue
      denom_t = rms_t[gindex]
      if centered:
        mg_t[gindex] = mg_t[gindex] * decay + (1 - decay) * gvalue
        denom_t -= mg_t[gindex] * mg_t[gindex]
      mom_t[gindex] = momentum * mom[gindex] + lr * gvalue / np.sqrt(denom_t)
      var_t[gindex] = var[gindex] - mom_t[gindex]
    return var_t, mg_t, rms_t, mom_t