def test_view_of_view(matrix_type, subset_func, subset_func2):
    adata = gen_adata((30, 15), X_type=matrix_type)
    adata.raw = adata
    if subset_func is single_subset:
        pytest.xfail("Other subset generating functions have trouble with this")
    var_s1 = subset_func(adata.var_names, min_size=4)
    var_view1 = adata[:, var_s1]
    var_s2 = subset_func2(var_view1.var_names)
    var_view2 = var_view1[:, var_s2]
    assert var_view2._adata_ref is adata
    obs_s1 = subset_func(adata.obs_names, min_size=4)
    obs_view1 = adata[obs_s1, :]
    obs_s2 = subset_func2(obs_view1.obs_names)
    assert adata[obs_s1, :][:, var_s1][obs_s2, :]._adata_ref is adata

    view_of_actual_copy = adata[:, var_s1].copy()[obs_s1, :].copy()[:, var_s2].copy()

    view_of_view_copy = adata[:, var_s1][obs_s1, :][:, var_s2].copy()

    assert_equal(view_of_actual_copy, view_of_view_copy, exact=True)
def testMinimizeSparseResourceVariableCentered(self, dtype):
    with self.cached_session():
      var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype)
      x = constant_op.constant([[4.0], [5.0]], dtype=dtype)
      pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x)
      loss = pred * pred
      sgd_op = rmsprop.RMSPropOptimizer(
          learning_rate=1.0,
          decay=0.1,
          momentum=0.0,
          epsilon=1.0,
          centered=True).minimize(loss)
      variables.global_variables_initializer().run()
      # Fetch params to validate initial values
      self.assertAllCloseAccordingToType([[1.0, 2.0]], var0.eval())
      # Run 1 step of sgd
      sgd_op.run()
      # Validate updated params
      self.assertAllCloseAccordingToType(
          [[-7/3.0, -4/3.0]], var0.eval(), atol=0.01)