def passed_test(dtype, as_matrix, x_is_row, stride):
"""
Run one vector scaling 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
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 scalar and vector to test
alpha = uniform(SCAL_MIN, SCAL_MAX)
x = random_vector(length, x_is_row, dtype, as_matrix)
# create copy of x to hold the expected result
x_2 = copy(x.T) if x_is_row else copy(x)
# compute the expected result
if stride == 1:
x_2 *= alpha
else:
for i in range(0, length, stride):
x_2[i, 0] *= alpha
# get the actual result
scal(alpha, x, stride)
# if x is a row vector, make x_2 a row vector as well
x_2 = x_2.T if x.shape[0] == 1 else x_2
# compare the actual result to the expected result and return result of the test
return allclose(x, x_2)
def test_float64_dtype(self):
x = array([[1., 2., 3.]], dtype='float64')
self.assertEqual(x.dtype, 'float64')
expected = [[2., 4., 6.]]
self.assertListEqual(scal(2, x,).tolist(), expected)
self.assertListEqual(x.tolist(), expected)
def test_stride_greater_than_length(self):
x = array([[1., 2., 3.]])
expected = [[2., 2., 3.]]
self.assertListEqual(scal(2, x, inc_x=3).tolist(), expected)
self.assertListEqual(x.tolist(), expected)
def test_vector_as_matrix(self):
x = asmatrix(array([[1., 2., 3.]]))
expected = [[2., 4., 6.]]
self.assertListEqual(scal(2, x).tolist(), expected)
self.assertListEqual(x.tolist(), expected)
def test_negative_alpha_with_vectors(self):
x = array([[-1., -2., 3.]])
expected = [[1.5, 3., -4.5]]
self.assertListEqual(scal(-1.5, x).tolist(), expected)
self.assertListEqual(x.tolist(), expected)
def test_alpha_equal_to_zero_with_vectors(self):
x = array([[-1., -2., 3.]])
expected = [[0., 0., 0.]]
self.assertListEqual(scal(0, x).tolist(), expected)
self.assertListEqual(x.tolist(), expected)
def test_vector_with_negatives_elements(self):
x = array([[-1., -2., 3.]])
expected = [[-2., -4., 6.]]
self.assertListEqual(scal(2, x).tolist(), expected)
self.assertListEqual(x.tolist(), expected)
def test_two_vector_as_ndarray(self):
x = array([[1.], [2.], [3.]])
expected = [[2.], [4.], [6.]]
self.assertListEqual(scal(2, x).tolist(), expected)
self.assertListEqual(x.tolist(), expected)
def test_row_vector_as_ndarray(self):
x = array([[1., 2., 3.]])
expected = [[2., 4., 6.]]
self.assertListEqual(scal(2, x).tolist(), expected)
self.assertListEqual(x.tolist(), expected)
def test_negative_alpha_with_scalar(self):
x = array([[1.]])
expected = [[-1.]]
self.assertListEqual(scal(-1, x).tolist(), expected)
self.assertListEqual(x.tolist(), expected)
def test_alpha_equal_to_zero_with_scalar(self):
x = array([[1.]])
expected = [[0.]]
self.assertListEqual(scal(0, x).tolist(), expected)
self.assertListEqual(x.tolist(), expected)
def test_scalar_as_ndarray(self):
x = array([[1.]])
expected = [[2.]]
self.assertListEqual(scal(2, x).tolist(), expected)
self.assertListEqual(x.tolist(), expected)