def before_integrate(self, t):
prev_f = collections.deque(maxlen=self.max_order + 1)
prev_t = collections.deque(maxlen=self.max_order + 1)
phi = collections.deque(maxlen=self.max_order)
t0 = t[0]
f0 = self.func(tf.cast(t0, self.y0[0].dtype), self.y0)
prev_t.appendleft(t0)
prev_f.appendleft(f0)
phi.appendleft(f0)
first_step = _select_initial_step(self.func,
t[0],
self.y0,
2,
self.rtol[0],
self.atol[0],
f0=f0)
first_step = move_to_device(first_step, t.device)
first_step = tf.cast(first_step, t[0].dtype)
self.vcabm_state = _VCABMState(self.y0,
prev_f,
prev_t,
next_t=t[0] + first_step,
phi=phi,
order=1)
def before_integrate(self, t):
f0 = self.func(tf.cast(t[0], self.y0[0].dtype), self.y0)
if self.first_step is None:
first_step = _select_initial_step(self.func, t[0], self.y0, 1, self.rtol[0], self.atol[0], f0=f0)
first_step = move_to_device(tf.cast(first_step, t.dtype), t.device)
else:
first_step = _convert_to_tensor(self.first_step, dtype=t.dtype, device=t.device)
self.rk_state = _RungeKuttaState(self.y0, f0, t[0], t[0], first_step, interp_coeff=[self.y0] * 5)
def before_integrate(self, t):
if self.first_step is None:
first_step = _convert_to_tensor(_select_initial_step(self.func, t[0], self.y0, 4, self.rtol, self.atol),
device=t.device)
else:
first_step = _convert_to_tensor(0.01, dtype=t.dtype, device=t.device)
self.rk_state = _RungeKuttaState(
self.y0,
cast_double(self.func(t[0], self.y0)), t[0], t[0], first_step,
tuple(map(lambda x: [x] * 7, self.y0))
)
def before_integrate(self, t):
if self.first_step is None:
first_step = _convert_to_tensor(_select_initial_step(self.func, t[0], self.y0, 4, self.rtol, self.atol),
device=t.device)
else:
first_step = _convert_to_tensor(self.first_step, dtype=t.dtype, device=t.device)
self.rk_state = _RungeKuttaState(
self.y0,
self.func(t[0], self.y0), t[0], t[0], first_step,
[self.y0] * 7
)