def __init__(self,
func,
y0,
rtol,
atol,
first_step=None,
safety=0.9,
ifactor=10.0,
dfactor=0.2,
max_num_steps=2**31 - 1,
**unused_kwargs):
_handle_unused_kwargs(self, unused_kwargs)
del unused_kwargs
self.func = func
self.y0 = y0
self.rtol = rtol if _is_iterable(rtol) else [rtol] * len(y0)
self.atol = atol if _is_iterable(atol) else [atol] * len(y0)
self.first_step = first_step
self.safety = _convert_to_tensor(safety,
dtype=torch.float64,
device=y0[0].device)
self.ifactor = _convert_to_tensor(ifactor,
dtype=torch.float64,
device=y0[0].device)
self.dfactor = _convert_to_tensor(dfactor,
dtype=torch.float64,
device=y0[0].device)
self.max_num_steps = _convert_to_tensor(max_num_steps,
dtype=torch.int32,
device=y0[0].device)
def __init__(self,
func,
y0,
rtol,
atol,
implicit=True,
max_order=_MAX_ORDER,
safety=0.9,
ifactor=10.0,
dfactor=0.2,
**unused_kwargs):
_handle_unused_kwargs(self, unused_kwargs)
del unused_kwargs
self.func = func
self.y0 = y0
self.rtol = rtol if _is_iterable(rtol) else [rtol] * len(y0)
self.atol = atol if _is_iterable(atol) else [atol] * len(y0)
self.implicit = implicit
self.max_order = int(max(_MIN_ORDER, min(max_order, _MAX_ORDER)))
self.safety = _convert_to_tensor(safety,
dtype=torch.float64,
device=y0[0].device)
self.ifactor = _convert_to_tensor(ifactor,
dtype=torch.float64,
device=y0[0].device)
self.dfactor = _convert_to_tensor(dfactor,
dtype=torch.float64,
device=y0[0].device)
def __init__(self, func, y0, atol, rtol, **unused_kwargs):
_handle_unused_kwargs(self, unused_kwargs)
del unused_kwargs
self.func = func
self.y0 = y0
self.atol = atol
self.rtol = rtol
def __init__(self,
func,
y0,
step_size=None,
grid_constructor=None,
**unused_kwargs):
unused_kwargs.pop('rtol', None)
unused_kwargs.pop('atol', None)
_handle_unused_kwargs(self, unused_kwargs)
del unused_kwargs
self.func = func
self.y0 = y0
if step_size is not None and grid_constructor is None:
self.grid_constructor = self._grid_constructor_from_step_size(
step_size)
elif grid_constructor is None:
self.grid_constructor = lambda f, y0, t: t
else:
raise ValueError(
"step_size and grid_constructor are exclusive arguments.")