coopihc.inference.ContinuousKalmanUpdate.ContinuousKalmanUpdate

class ContinuousKalmanUpdate(*args, **kwargs)[source]

Bases: coopihc.inference.BaseInferenceEngine.BaseInferenceEngine

An inference engine which estimates the new state according to a continuous kalman filter, where state transition dynamics and kalman gains are provided externally.

Methods

`add_observation`	add observation
`bind`	Bind function to the engine with a given name.
`default_value`	Apply this decorator to use self.agent_observation as default value to infer from if agent_observation = None
`infer`
`render`
`reset`	reset _summary_
`set_K`	Set the Kalman gain
`set_forward_model_dynamics`	set forward model dynamics

Attributes

`action`	The agent's last action
`host`
`observation`	The last observation.
`role`
`state`	The current agent state
`unwrapped`

property action

The agent’s last action

Returns: agent action
Return type: State

add_observation(observation)

add observation

Add an observation to a buffer. If the buffer does not exist, create a naive buffer. The buffer has a size given by buffer length

Parameters: observation (State) – observation produced by an engine

bind(func, as_name=None)

Bind function to the engine with a given name. If as_name is None, then the func name is used.

Parameters

func (function) – function to bind
as_name (string, optional) – name of resulting method, defaults to None

Returns

bound method

Return type

method

default_value(): Apply this decorator to use self.agent_observation as default value to infer from if agent_observation = None

property observation

The last observation.

Returns: last observation
Return type: State

reset(random=True)

reset _summary_

Empty the buffer

Parameters: random (bool, optional) – whether to randomize parameters internal to the inference engine. This is provided in case of subclass the BaseInferenceEngine, defaults to True.

set_K(K)[source]

Set the Kalman gain

Parameters: K (numpy.ndarray) – Kalman Gain

set_forward_model_dynamics(A, B, C)[source]

set forward model dynamics

Call this externally to supply the linear dynamic matrices that describe the deterministic part of the state transitions:

\[\begin{split}\begin{align*} d\hat{x} = A\hat{x}dt + Budt \\ dy = C \hat{x} dt \end{align*}\end{split}\]

Parameters

A (numpy.ndarray) – see equation above
B (numpy.ndarray) – see equation above
C (numpy.ndarray) – see equation above

property state

The current agent state

Returns: agent state
Return type: State