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* Update Bayesian Binary Sensor configuration * Clarify observation platform example differences * Update binary_sensor.bayesian.markdown
134 lines
4.1 KiB
Markdown
134 lines
4.1 KiB
Markdown
---
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layout: page
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title: "Bayesian Binary Sensor"
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description: "Instructions on 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: Utility
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ha_iot_class: "Local Polling"
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ha_release: 0.53
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ha_qa_scale: internal
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---
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The `bayesian` binary sensor platform observes the state from multiple sensors and uses [Bayes' rule](https://en.wikipedia.org/wiki/Bayes%27_theorem) to estimate the probability that an event has occurred given the state of the observed sensors. If the estimated posterior probability is above the `probability_threshold`, the sensor is `on` otherwise it is `off`.
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This allows for the detection of complex events that may not be readily observable, e.g., cooking, showering, in bed, the start of a 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, e.g., presence.
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## {% linkable_title Configuration %}
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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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- platform: bayesian
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prior: 0.1
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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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```
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{% configuration %}
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prior:
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description: >
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The prior probability of the event. At any point in time
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(ignoring all external influences) how likely is this event to occur?
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required: true
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type: float
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probability_threshold:
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description: The probability at which the sensor should trigger to `on`.
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required: false
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type: float
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default: 0.5
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name:
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description: Name of the sensor to use in the frontend.
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required: false
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type: string
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default: Bayesian Binary Sensor
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observations:
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description: The observations which should influence the likelihood that the given event has occurred.
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required: true
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type: list
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keys:
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entity_id:
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description: Name of the entity to monitor.
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required: true
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type: string
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prob_given_true:
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description: The probability of the observation occurring, given the event is `true`.
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required: true
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type: float
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prob_given_false:
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description: The probability of the observation occurring, given the event is `false` can be set as well.
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required: false
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type: float
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default: "`1 - prob_given_true` if `prob_given_false` is not set"
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platform:
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description: >
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The only supported observation platforms are `state` and `numeric_state`,
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which are modeled after their corresponding triggers for automations,
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requiring `below` and/or `above` instead of `to_state`.
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required: true
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type: string
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to_state:
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description: The target state.
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required: true
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type: string
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{% endconfiguration %}
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## {% linkable_title Full examples %}
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The following is an example for the `state` observation platform.
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```yaml
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# Example configuration.yaml entry
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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: 'sun.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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Next up an example which targets the `numeric_state` observation platform,
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as seen in the configuration it requires `below` and/or `above` instead of `to_state`.
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```yaml
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# Example configuration.yaml entry
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binary_sensor:
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name: 'Heat On'
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platform: 'bayesian'
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prior: 0.2
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probability_threshold: 0.9
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observations:
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- entity_id: 'sensor.outside_air_temperature_fahrenheit'
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prob_given_true: 0.95
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platform: 'numeric_state'
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below: 50
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```
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