Пример #1
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    def test_row_row_as_ndarray_no_A(self):
        x = array([[1., 2.]])
        y = array([[3., 4.]])

        expected = [[6., 10.],
                    [0., 16.]]
        self.assertListEqual(syr2(x, y).tolist(), expected)
Пример #2
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    def test_matrix_col_col_as_ndarray_no_A(self):
        x = array([[1.],
                   [2.]])
        y = array([[3.],
                   [4.]])

        expected = [[6., 10.],
                    [0., 16.]]
        self.assertListEqual(syr2(x, y).tolist(), expected)
Пример #3
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    def test_as_matrix_mixed_no_A(self):
        x = asmatrix(array([[1.],
                            [2.]]))
        y = array([[3.],
                   [4.]])

        expected = [[6., 10.],
                    [0., 16.]]
        self.assertListEqual(syr2(x, y).tolist(), expected)
Пример #4
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    def test_row_row_as_ndarray_provide_A(self):
        A = array([[1., 2.],
                   [2., 3.]])
        x = array([[1., 2.]])
        y = array([[3., 4.]])

        expected = [[7., 12.],
                    [2., 19.]]
        self.assertListEqual(syr2(x, y, A).tolist(), expected)
        self.assertListEqual(A.tolist(), expected)
def passed_test(dtype, as_matrix, x_is_row, y_is_row, provide_A, stride, uplo):
    """
    Run one symmetric rank-2 update test.

    Arguments:
        dtype:        either 'float64' or 'float32', the NumPy dtype to test
        as_matrix:    True to test a NumPy matrix, False to test a NumPy ndarray
        x_is_row:     True to test a row vector as parameter x, False to test a column vector
        y_is_row:     True to test a row vector as parameter y, False to test a column vector
        provide_A:    True if A is to be provided to the BLASpy function, False otherwise
        stride:       stride of x and y to test; if None, a random stride is assigned
        uplo:         BLASpy uplo parameter to test

    Returns:
        True if the expected result is within the margin of error of the actual result,
        False otherwise.
    """

    # generate random sizes for matrix/vector dimensions and vector stride (if necessary)
    n = randint(N_MIN, N_MAX)
    stride = randint(N_MIN, STRIDE_MAX) if stride is None else stride
    n_A = n / stride + (n % stride > 0)

    # create random scalars, vectors, and matrices to test
    alpha = uniform(SCAL_MIN, SCAL_MAX)
    x = random_vector(n, x_is_row, dtype, as_matrix)
    y = random_vector(n, y_is_row, dtype, as_matrix)
    A = random_symmetric_matrix(n_A, dtype, as_matrix) if provide_A else None

    # create copies/views of A, x, and y that can be used to calculate the expected result
    x_2 = x.T if x_is_row else x
    y_2 = y.T if y_is_row else y
    A_2 = zeros((n_A, n_A)) if A is None else copy(A)

    # compute the expected result
    if stride == 1:
        A_2 += alpha * dot(x_2, y_2.T)
        A_2 += alpha * dot(y_2, x_2.T)
    else:
        for i in range(0, n_A):
            for j in range(0, n_A):
                A_2[i, j] += alpha * (x_2[i * stride, 0] * y_2[j * stride, 0])
                A_2[i, j] += alpha * (y_2[i * stride, 0] * x_2[j * stride, 0])

    # get the actual result
    A = syr2(x, y, A, uplo, alpha, inc_x=stride, inc_y=stride)

    # make A and A_2 triangular so that they can be compared
    A = triu(A) if uplo == 'u' else tril(A)
    A_2 = triu(A_2) if uplo == 'u' else tril(A_2)

    # compare the actual result to the expected result and return result of the test
    return allclose(A, A_2, RTOL, ATOL)
Пример #6
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    def test_lower_triangle_ignored_with_uplo_u(self):
        A = array([[1., 2.],
                   [-100., 3.]])
        x = array([[1.],
                   [2.]])
        y = array([[3.],
                   [4.]])

        expected = [[7., 12.],
                    [-100., 19.]]
        self.assertListEqual(syr2(x, y, A, uplo='u').tolist(), expected)
        self.assertListEqual(A.tolist(), expected)
Пример #7
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    def test_upper_triangle_ignored_with_uplo_L(self):
        A = array([[1., 55.],
                   [2., 3.]])
        x = array([[1.],
                   [2.]])
        y = array([[3.],
                   [4.]])

        expected = [[7., 55.],
                    [12., 19.]]
        self.assertListEqual(syr2(x, y, A, uplo='L').tolist(), expected)
        self.assertListEqual(A.tolist(), expected)
Пример #8
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    def test_strides_less_than_length_no_A(self):
        x = array([[1.],
                   [2.],
                   [3.],
                   [4.]])
        y = array([[3.],
                   [4.],
                   [5.],
                   [6.]])

        expected = [[6., 14.],
                    [0., 30.]]
        self.assertListEqual(syr2(x, y, inc_x=2, inc_y=2).tolist(), expected)
Пример #9
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    def test_alpha(self):
        A = array([[1., 2.],
                   [2., 3.]])
        x = array([[1.],
                   [2.]])
        y = array([[3.],
                   [4.]])
        alpha = 2.5

        expected = [[16., 27.],
                    [2., 43.]]
        self.assertListEqual(syr2(x, y, A, alpha=alpha).tolist(), expected)
        self.assertListEqual(A.tolist(), expected)
Пример #10
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    def test_unequal_strides(self):
        A = array([[1., 2.],
                   [2., 3.]])
        x = array([[1.],
                   [2.],
                   [3.],
                   [4.]])
        y = array([[3.],
                   [5.]])

        expected = [[7., 16.],
                    [2., 33.]]
        self.assertListEqual(syr2(x, y, A, inc_x=2, inc_y=1).tolist(), expected)
        self.assertListEqual(A.tolist(), expected)
Пример #11
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    def test_strides_greater_than_length_provide_A(self):
        A = array([[3.]])
        x = array([[1.],
                   [2.],
                   [3.],
                   [4.]])
        y = array([[3.],
                   [4.],
                   [5.],
                   [6.]])

        expected = [[9.]]
        self.assertListEqual(syr2(x, y, A, inc_x=4, inc_y=4).tolist(), expected)
        self.assertListEqual(A.tolist(), expected)
Пример #12
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    def test_float64_dtype(self):
        A = array([[1., 2.],
                   [2., 3.]], dtype='float64')
        x = array([[1.],
                   [2.]], dtype='float64')
        y = array([[3.],
                   [4.]], dtype='float64')
        self.assertEqual(A.dtype, 'float64')
        self.assertEqual(x.dtype, 'float64')
        self.assertEqual(y.dtype, 'float64')

        expected = [[7., 12.],
                    [2., 19.]]
        self.assertListEqual(syr2(x, y, A).tolist(), expected)
        self.assertListEqual(A.tolist(), expected)
Пример #13
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    def test_strides_less_than_length_provide_A(self):
        A = array([[1., 2.],
                   [2., 3.]])
        x = array([[1.],
                   [2.],
                   [3.],
                   [4.]])
        y = array([[3.],
                   [4.],
                   [5.],
                   [6.]])

        expected = [[7., 16.],
                    [2., 33.]]
        self.assertListEqual(syr2(x, y, A, inc_x=2, inc_y=2).tolist(), expected)
        self.assertListEqual(A.tolist(), expected)
Пример #14
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 def test_scalars_as_ndarray_no_A(self):
     x = array([[2.]])
     y = array([[3.]])
     self.assertEqual(syr2(x, y), 12)
Пример #15
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 def test_scalars_as_ndarray_provide_A(self):
     A = array([[1.]])
     x = array([[2.]])
     y = array([[3.]])
     self.assertEqual(syr2(x, y, A), 13)
     self.assertEqual(A, 13)