def initial_state(self,
name,
lagged_values,
lagged_times,
machine_params=None,
machine_state=None,
**ignore):
#
machine = self.machine_type(params=deepcopy(machine_params),
state=deepcopy(machine_state),
hyper_params=deepcopy(
self.machine_hyper_params))
chronological_values = list(reversed(lagged_values))
chronological_times = list(reversed(lagged_times))
as_process = is_process(chronological_values)
values = list(np.diff(
[0.] +
chronological_values)) if as_process else chronological_values
dts = list(
np.diff([chronological_times[0] - 1.0] + chronological_times))
for value, dt in zip(values, dts):
machine.update(value=value, dt=dt)
return {
't': lagged_times[0],
'machine': machine,
'as_process': True,
'dt': approx_dt(lagged_times),
'name': name
}
def initial_state(self, name, **ignore):
""" Decide if it is a process or not, and create initial sketch of CDF of values or changes in values """
# This is one off. Restarting may change the classification !
values = self.get_lagged_values(name=name)
times = self.get_lagged_times(name=name)
digest = TDigest()
as_process = is_process(values)
data = np.diff(list(values) +
[0., 0.]) if is_process(values) else values
for value in data:
digest.update(value)
return {
't': times[0],
'digest': digest,
'as_process': as_process,
'dt': approx_dt(times),
'name': name
}
def sample(self,
lagged_values,
lagged_times=None,
name=None,
delay=None,
**ignored):
""" Override default sampler so that we put the stream in the fit queue """
if is_process(lagged_values):
if name not in self.stream_state:
self.stream_state.update({
name:
self.initial_state(name=name,
lagged_values=lagged_values,
lagged_times=lagged_times,
machine_state=self.machine_state,
machine_params=self.machine_params)
})
state = self.stream_state[name]
state = self.update_state(state=state,
lagged_values=lagged_values,
lagged_times=lagged_times)
samples = self.sample_using_state(state=state,
lagged_values=lagged_values,
lagged_times=lagged_times,
name=name,
delay=delay)
self.stream_state[name] = state
if name not in self.fit_queue:
self.fit_queue.append(name)
return samples
else:
# Flee cases where it isn't really a process
for delay in self.DELAYS:
horizon = self.horizon_name(name=name, delay=delay)
self.withdraw(horizon=horizon)