def passed_test(output, as_matrix, x_is_row, y_is_row, stride):
"""
Run one extended-precision dot (inner) product test.
Arguments:
output: BLASpy 'output' parameter 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
stride: stride of x and y to test; if None, a random stride is assigned
Returns:
True if the expected result is within the margin of error of the actual result,
False otherwise.
"""
# generate random sizes for vector dimensions and vector stride (if necessary)
length = randint(N_MIN, N_MAX)
stride = randint(N_MIN, STRIDE_MAX) if stride is None else stride
# create random vectors to test
x = random_vector(length, x_is_row, 'float32', as_matrix)
y = random_vector(length, y_is_row, 'float32', as_matrix)
# create views of x and y that can be used to calculate the expected result
x_2 = x if x_is_row else x.T
y_2 = y.T if y_is_row else y
# compute the expected result
if stride == 1:
expected = dot(x_2, y_2)[0][0]
else:
expected = 0
for i in range(0, length, stride):
expected += x_2[0, i] * y_2[i, 0]
# get the actual result
actual = sdot(x, y, stride, stride, output)
# compare the actual result to the expected result and return result of the test
return abs(actual - expected) / expected < EPSILON
def test_unequal_strides(self):
x = array([[1., 2., 3., 4., 5., 6.]], dtype='float32')
y = array([[3., 2., 1.]], dtype='float32')
self.assertEqual(sdot(x, y, inc_x=3, inc_y=2), 7)
def test_float64_output(self):
x = array([[1., 2., 3.]], dtype='float32')
y = array([[3., 2., 1.]], dtype='float32')
self.assertEqual(sdot(x, y, output='float64'), 10)
def test_strides_less_than_length(self):
x = array([[1., 2., 3.]], dtype='float32')
y = array([[3., 2., 1.]], dtype='float32')
self.assertEqual(sdot(x, y, inc_x=2, inc_y=2), 6)
def test_strides_greater_than_length(self):
x = array([[1., 2., 3.]], dtype='float32')
y = array([[3., 2., 1.]], dtype='float32')
self.assertEqual(sdot(x, y, inc_x=3, inc_y=3), 3)
def test_vectors_as_mixed_matrices_and_ndarrays(self):
x = asmatrix(array([[1., 2., 3.]], dtype='float32'))
y = array([[3., 2., 1.]], dtype='float32')
self.assertEqual(sdot(x, y), 10)
def test_vectors_with_negatives_in_values(self):
x = array([[-1., -2., 3.]], dtype='float32')
y = array([[3., 2., 1.]], dtype='float32')
self.assertEqual(sdot(x, y), -4)
def test_row_and_col_vectors_as_ndarrays(self):
x = array([[1., 2., 3.]], dtype='float32')
y = array([[3.], [2.], [1.]], dtype='float32')
self.assertEqual(sdot(x, y), 10)
def test_two_column_vectors_as_ndarrays(self):
x = array([[1.], [2.], [3.]], dtype='float32')
y = array([[3.], [2.], [1.]], dtype='float32')
self.assertEqual(sdot(x, y), 10)
def test_scalar_as_ndarray(self):
x = array([[1.]], dtype='float32')
y = array([[2.]], dtype='float32')
self.assertEqual(sdot(x, y), 2)
