126cab9311
* 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.
95 lines
3.2 KiB
Markdown
95 lines
3.2 KiB
Markdown
---
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layout: page
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title: "Bayesian Binary Sensor"
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description: "Instructions how to integrate threshold Bayesian sensors into Home Assistant."
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date: 2017-08-27 20:05
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sidebar: true
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comments: false
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sharing: true
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footer: true
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logo: home-assistant.png
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ha_category: Binary Sensor
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ha_iot_class: "Local Polling"
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ha_release: 0.53
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---
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The `bayesian` binary sensor platform observes the state from multiple sensors
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and uses Bayes' rule to estimate the probability that an event has occurred
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given the state of the observed sensors. If the estimated posterior probability
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is above the `probabiliy_threshold`, the value of the sensor is `on`.
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Otherwise, the sensor is `off`.
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This allows for the detection of complex events that may not be readily
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observable, i.e. cooking, showering, in bed, start of morning routine, etc. It
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can also be used to gain greater confidence about events that _are_ directly
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observable, but for which the sensors can be unreliable, i.e. presence.
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To enable the Bayesian sensor, add the following lines to your `configuration.yaml`:
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```yaml
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# Example configuration.yaml entry
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binary_sensor:
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name: 'currently_cooking'
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platform: 'bayesian'
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prior: 0.1
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probability_threshold: 0.7
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observations:
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- entity_id: 'switch.kitchen_lights'
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prob_given_true: 0.6
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prob_given_false: 0.2
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platform: 'state'
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to_state: 'on'
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- entity_id: 'sensor.stove_temperature'
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prob_given_true: 0.9
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platform: 'numeric_state'
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above: 100
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- entity_id: 'sensor.kitchen_motion'
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prob_given_true: 0.5
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prob_given_false: 0.2
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platform: 'state'
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to_state: 'on'
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binary_sensor:
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name: 'in_bed'
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platform: 'bayesian'
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prior: 0.25
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probability_threshold: 0.95
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observations:
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- entity_id: 'sensor.living_room_motion'
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prob_given_true: 0.4
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prob_given_false: 0.2
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platform: 'state'
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to_state: 'off'
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- entity_id: 'sensor.basement_motion'
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prob_given_true: 0.5
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prob_given_false: 0.4
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platform: 'state'
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to_state: 'off'
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- entity_id: 'sensor.bedroom_motion'
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prob_given_true: 0.5
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platform: 'state'
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to_state: 'on'
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- entity_id: 'sensor.sun'
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prob_given_true: 0.7
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platform: 'state'
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to_state: 'below_horizon'
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```
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Configuration variables:
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- **prior** (*Required*): The prior probability of the event. At any point in
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time (ignoring all external influences) how likely is this event to occur?
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- **observations** (*Required*): The observations which should influence the
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likelihood that the given event has occurred. The only supported observation
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platforms are `state` and `numeric_state`, which are modeled after their
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corresponding triggers for automations. In addition to the automation syntax,
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the observations also require `prob_given_true` (the probability of the
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observation occurring, given the event is true). The optional
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`prob_given_false` (the probability of the observation occurring, given the
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event is false) can be set as well. If `prob_given_false` is not set, it will
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default to `1 - prob_given_true`.
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- **probability_threshold** (*Optional*): The probability at which the sensor
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should trigger to `on`.
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- **name** (*Optional*): Name of the sensor to use in the frontend. Defaults to
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`Bayesian Binary`.
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