def test_ncol(self):
r = npu.row(429., 5., 2., 14.)
self.assertEqual(npu.ncol(r), 4)
c = npu.col(429., 5., 2., 14.)
self.assertEqual(npu.ncol(c), 1)
m = npu.matrix_of(3, 5, 0.)
self.assertEqual(npu.ncol(m), 5)
def __init__(self, process_dim=1, noise_dim=None, drift=None, diffusion=None, **kwargs):
self._process_dim = process_dim
self._noise_dim = process_dim if noise_dim is None else noise_dim
# Note: the brackets around the lambdas below are essential, otherwise the result of the parsing will not be what we need:
self._drift = (lambda t, x: npu.row_of(self._process_dim, 0.)) if drift is None else drift
self._diffusion = (lambda t, x: npu.matrix_of(self._process_dim, self._noise_dim, 0.)) if diffusion is None else diffusion
self._to_string_helper_ItoProcess = None
self._str_ItoProcess = None
super(ItoProcess, self).__init__(process_dim=self._process_dim, noise_dim=self._noise_dim,
drift=self._drift, diffusion=self._diffusion, **kwargs)
def test_matrix_of(self):
matrix = npu.matrix_of(2, 3, 429.)
npt.assert_almost_equal(matrix, np.array([[429., 429., 429.], [429., 429., 429.]]))