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.]]))