home-assistant.github.io/source/_components/binary_sensor.bayesian.markdown at 126cab931165898f9cb12a9ca98d854676b83bea

jeena/home-assistant.github.io

Jeff McGehee 126cab9311 Add Bayesian Binary Sensor documentation (#3268 )

* Add Bayesian Binary Sensor documentation

Why:

* I have a PR that is in progress on the main Hass repo.

This change addresses the need by:

* `binary_sensor.bayesian.markdown` in the `_components` directory.

* bump to proper release, add more info about the `observation` configuration.

2017-08-30 20:42:35 +02:00

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layout	title	description	date	sidebar	comments	sharing	footer	logo	ha_category	ha_iot_class	ha_release
page	Bayesian Binary Sensor	Instructions how to integrate threshold Bayesian sensors into Home Assistant.	2017-08-27 20:05	true	false	true	true	home-assistant.png	Binary Sensor	Local Polling	0.53

The bayesian binary sensor platform observes the state from multiple sensors and uses Bayes' rule to estimate the probability that an event has occurred given the state of the observed sensors. If the estimated posterior probability is above the probabiliy_threshold, the value of the sensor is on. Otherwise, the sensor is off.

This allows for the detection of complex events that may not be readily observable, i.e. cooking, showering, in bed, start of morning routine, etc. It can also be used to gain greater confidence about events that are directly observable, but for which the sensors can be unreliable, i.e. presence.

To enable the Bayesian sensor, add the following lines to your configuration.yaml:

# Example configuration.yaml entry
binary_sensor:
  name: 'currently_cooking'
  platform: 'bayesian'
  prior: 0.1
  probability_threshold: 0.7
  observations:
    - entity_id: 'switch.kitchen_lights'
      prob_given_true: 0.6
      prob_given_false: 0.2
      platform: 'state'
      to_state: 'on'
    - entity_id: 'sensor.stove_temperature'
      prob_given_true: 0.9
      platform: 'numeric_state'
      above: 100
    - entity_id: 'sensor.kitchen_motion'
      prob_given_true: 0.5
      prob_given_false: 0.2
      platform: 'state'
      to_state: 'on'

binary_sensor:
  name: 'in_bed'
  platform: 'bayesian'
  prior: 0.25
  probability_threshold: 0.95
  observations:
    - entity_id: 'sensor.living_room_motion'
      prob_given_true: 0.4
      prob_given_false: 0.2
      platform: 'state'
      to_state: 'off'
    - entity_id: 'sensor.basement_motion'
      prob_given_true: 0.5
      prob_given_false: 0.4
      platform: 'state'
      to_state: 'off'
    - entity_id: 'sensor.bedroom_motion'
      prob_given_true: 0.5
      platform: 'state'
      to_state: 'on'
    - entity_id: 'sensor.sun'
      prob_given_true: 0.7
      platform: 'state'
      to_state: 'below_horizon'

Configuration variables:

prior (Required): The prior probability of the event. At any point in time (ignoring all external influences) how likely is this event to occur?
observations (Required): The observations which should influence the likelihood that the given event has occurred. The only supported observation platforms are state and numeric_state, which are modeled after their corresponding triggers for automations. In addition to the automation syntax, the observations also require prob_given_true (the probability of the observation occurring, given the event is true). The optional prob_given_false (the probability of the observation occurring, given the event is false) can be set as well. If prob_given_false is not set, it will default to 1 - prob_given_true.
probability_threshold (Optional): The probability at which the sensor should trigger to on.
name (Optional): Name of the sensor to use in the frontend. Defaults to Bayesian Binary.

3.2 KiB Raw Blame History

3.2 KiB

Raw Blame History