Method test_weights

numpy/lib/tests/test_function_base.py:332–374

(self)

        assert_array_equal(wsum, expected_wsum)

    def test_weights(self):
        y = np.arange(10)
        w = np.arange(10)
        actual = average(y, weights=w)
        desired = (np.arange(10) ** 2).sum() * 1. / np.arange(10).sum()
        assert_almost_equal(actual, desired)

        y1 = np.array([[1, 2, 3], [4, 5, 6]])
        w0 = [1, 2]
        actual = average(y1, weights=w0, axis=0)
        desired = np.array([3., 4., 5.])
        assert_almost_equal(actual, desired)

        w1 = [0, 0, 1]
        actual = average(y1, weights=w1, axis=1)
        desired = np.array([3., 6.])
        assert_almost_equal(actual, desired)

        # This should raise an error. Can we test for that ?
        # assert_equal(average(y1, weights=w1), 9./2.)

        # 2D Case
        w2 = [[0, 0, 1], [0, 0, 2]]
        desired = np.array([3., 6.])
        assert_array_equal(average(y1, weights=w2, axis=1), desired)
        assert_equal(average(y1, weights=w2), 5.)

        y3 = rand(5).astype(np.float32)
        w3 = rand(5).astype(np.float64)

        assert_(np.average(y3, weights=w3).dtype == np.result_type(y3, w3))

        # test weights with `keepdims=False` and `keepdims=True`
        x = np.array([2, 3, 4]).reshape(3, 1)
        w = np.array([4, 5, 6]).reshape(3, 1)

        actual = np.average(x, weights=w, axis=1, keepdims=False)
        desired = np.array([2., 3., 4.])
        assert_array_equal(actual, desired)

        actual = np.average(x, weights=w, axis=1, keepdims=True)
        desired = np.array([[2.], [3.], [4.]])
        assert_array_equal(actual, desired)

    def test_returned(self):
        y = np.array([[1, 2, 3], [4, 5, 6]])