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)
Example #10
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 def test_negative_alpha_with_scalar(self):
     x = array([[1.]])
     expected = [[-1.]]
     self.assertListEqual(scal(-1, x).tolist(), expected)
     self.assertListEqual(x.tolist(), expected)
Example #11
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 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)
Example #12
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 def test_scalar_as_ndarray(self):
     x = array([[1.]])
     expected = [[2.]]
     self.assertListEqual(scal(2, x).tolist(), expected)
     self.assertListEqual(x.tolist(), expected)