def test_average_9(): """test_average_9 """ data = torch.arange(6).reshape((3, 2)) weights = torch.Tensor([1. / 4, -1. / 4]) with pytest.raises(ZeroDivisionError) as error: pm.average(data, weights=weights, axis=1) assert error.match("Weights sum to zero, can't be normalized")
def test_average_8(): """test_average_8 """ data = torch.arange(6).reshape((3, 2)) weights = torch.Tensor([1. / 4, 3. / 4]) with pytest.raises(ValueError) as error: pm.average(data, weights=weights, axis=0) assert error.match("Length of weights not compatible with specified axis.")
def test_average_3(): """test_average_3 """ data = torch.arange(6).reshape((3, 2)) weights = torch.Tensor([1. / 4, 3. / 4]) with pytest.raises(TypeError) as error: pm.average(data, weights=weights) assert error.match( "Axis must be specified when shapes of a and weights differ.")
def test_average_6(): """test_average_6 """ data = torch.arange(6).reshape((3, 2)) weights = torch.Tensor([[1, 2], [3, 4]]) with pytest.raises(TypeError) as error: pm.average(data, weights=weights, axis=1) assert error.match( "1D weights expected when shapes of a and weights differ.")
def test_average_5(): """test_average_5 """ data = torch.arange(6).reshape((3, 2)) result = pm.average(data, axis=1) expected_result = torch.Tensor([0.5, 2.5, 4.5]) assert (result == expected_result).all()
def test_average_4(): """test_average_4 """ a = torch.ones(5, dtype=torch.int16) w = torch.ones(5, dtype=torch.float32) avg = pm.average(a, weights=w) assert avg.dtype == torch.float64
def test_average_11(): """test_average_11 """ data = torch.arange(6, dtype=torch.float).reshape((3, 2)) weights = torch.Tensor([1. / 4, 3. / 4]) result_avg = pm.average(data, axis=1, weights=weights) expected_avg = torch.Tensor([0.75, 2.75, 4.75]) assert (result_avg == expected_avg).all()
def test_average_7(): """test_average_7 """ data = torch.arange(6).reshape((3, 2)) weights = torch.Tensor([1. / 4, 3. / 4]) result = pm.average(data, axis=1, weights=weights) expected_result = torch.Tensor([0.75, 2.75, 4.75]) assert (result == expected_result).all()
def test_average_10(): """test_average_10 """ data = torch.arange(6).reshape((3, 2)) weights = torch.Tensor([1. / 4, 3. / 4]) result_avg, result_scl = pm.average(data, axis=1, weights=weights, returned=True) expected_avg = torch.Tensor([0.75, 2.75, 4.75]) expected_scl = torch.Tensor([1., 1., 1.]) assert (result_avg == expected_avg).all() assert (result_scl == expected_scl).all()
def test_average_0(): """test_average_0 """ X = np.random.rand(5, 5) avg_np, _ = np.average(X, axis=1, returned=True) X_th = torch.tensor(X) avg_th, _ = pm.average(X_th, axis=1, returned=True) assert (X == X_th.numpy()).all() assert (avg_np == avg_th.numpy()).all() X -= avg_np[:, None] X_th -= avg_th[:, None] assert (X == X_th.numpy()).all()
def test_average_2(): """test_average_2 """ assert pm.average(torch.arange(1, 11), weights=torch.arange(10, 0, -1)) == 4
def test_average_1(): """test_average_1 """ data = torch.arange(1, 5) assert pm.average(data) == 2.5