#include <SensorEvent.h>

Inheritance diagram for SensorEvent:

Public Member Functions
	SensorEvent (std::string name="sensor_event")

AIMValue	setProperty (const std::string &key, AIMValue value)

void	doTick ()

Public Member Functions inherited from AIM
	AIM (std::string name="")

	AIM (const AIM &other)

AIM &	operator= (const AIM &rhs)

virtual void	addDataSubscriber (const std::string &key, AIM *subscriber)

virtual void	removeDataSubscriber (const std::string &key)

virtual AIMValue	getProperty (std::string key) const

virtual void	sendData ()

virtual void	addData (const AIMData &data)

virtual std::string	exportProperties ()

virtual std::vector< std::pair < std::string, AIMValue > >	getOutputData (bool clear_output=false)

virtual std::vector< std::pair < std::string, AIMValue > >	getInputData (bool clear_input=false)

virtual AIM *	getDataSubscriber (std::string subscriber)

virtual const std::map < std::string, AIMValue > &	getProperties ()

virtual std::string	getName () const

virtual std::vector< AIMValue >	getRequirements () const

virtual bool	requirementsAreUpdated (bool reset=true) const

virtual void	setProperties (const std::map< std::string, AIMValue > &properties)

Static Public Attributes
static const std::string	PROP_VARIANCE = "variance"

static const std::string	PROP_TIME_WINDOW = "time window"

static const std::string	PROP_LOWEST_VARIANCE = "lowest variance"

static const std::string	PROP_VARIANCE_THRESHOLD = "variance threshold"

static const std::string	PROP_IGNORE_START_UP_TIME = "ignore start up time"

static const std::string	PROP_HIGHEST_VARIANCE = "highest variance"

static const std::string	PROP_DATA_BUFFER = "data buffer"

static const std::string	PROP_STD_SCORE_MEAN = "standard score mean"

static const std::string	PROP_STD_SCORE_VARIANCE = "standard score variance"

static const std::string	PROP_STD_SCORE_COUNT = "standard score count"

static const std::string	PROP_FEATURE_NORMALIZATION = "normalization feature"

static const std::string	FEATURE_NORMALIZATION_STD = "standard score normalization"

static const std::string	FEATURE_NORMALIZATION_MINMAX = "min max normalization"

static const std::string	PROP_LEARN_VARIANCE_BUFFER = "learn variance buffer"

static const std::string	PROP_LEARN_RATE = "learn rate"

static const std::string	PROP_LEARN_RATE_USE_NORM = "learn rate use normalization"

static const std::string	PROP_LEARN_RATE_NORM_CNT = "learn rate normalization count"

static const std::string	PROP_LEARN_PERCENTILE = "learn percentile"

static const std::string	PROP_LEARN_VARIANCE = "learn variance"

static const std::string	PROP_FUNC_LEARN_VARIANCE = "func learn variance"

static const std::string	PROP_USE_ON_OFF_THRESHOLD = "use on off threshold"

static const std::string	PROP_VARIANCE_THRESHOLD_OFF = "variance threshold off"

Static Public Attributes inherited from AIM
static const std::string	VERSION = AIM_VERSION

Additional Inherited Members
Public Types inherited from AIM
typedef std::pair< std::string, AIMValue >	AIMData

typedef std::queue< AIMData >	AIMDataQueue

typedef std::queue< AIMValue >	AIMValueQueue

typedef std::map< std::string, AIMValueQueue >	InputDataQueue

Protected Member Functions inherited from AIM
void	setDataSubscribers (const std::map< std::string, AIM * > &subscribers)

void	setInputDataQueue (const InputDataQueue &inputDataQueue)

void	setOutputDataQueue (const AIMDataQueue &outputDataQueue)

Protected Attributes inherited from AIM
std::string	name

std::vector< AIMValue >	requirements

bool	requirementsUpdated

std::map< std::string, AIM * >	dataSubscribers

std::map< std::string, AIMValue >	properties

InputDataQueue	inputDataQueue

AIMDataQueue	outputDataQueue

Detailed Description

SensorEvent.h

Created on: March 21, 2013 Author: Ted Schmidt

This SensorEvent module is a filter that looks for significant changes in sensor data and returns the data if there is such a change.
It measures the variance in the input data and based on the learned or set variance it determines if there is an event.
A time window can be specified to compute the variance in the sensor data on.
The lowest variance ever found will be used as reference threshold.
Removing the lowest variance value from the properties, will automatically re-calibrate the SensorEvent module.
This module accepts multi-dimensional input, in the form of a vector with AIMValues.
The dimensions should not change for a sensor stream.
Manually set the variance:
{"variance":vector<double>(0.1)} the variance can be either an integer, float, or double
Use the learned variance by removing the learned variance
{"variance":null} set an empty AIMValue object
Set the time window:
{"time window":1.3} the max time window in seconds to learn and compute the variance over, can be either integer or double.
Set the threshold:
{"variance threshold" : 0.01} The threshold to send an event. This is the percentage of the normalized variance values.
Returns:
It returns the input data if the normalized variance is higher then the threshold value.
The input vector is returned in the data field and the normalized variance in the variance field

Constructor & Destructor Documentation

SensorEvent::SensorEvent ( std::string name = "sensor_event" )

The SensorEvent constructor

By default the variance threshold is set to 0.05 (5%) of the observed maximum variance in the time window, minus the observed noise observed. By default the start up time is ignored and the module will look for events even when the buffer is not full.

Member Function Documentation

void SensorEvent::doTick ( )

virtual

Do an AIM tick

This method is called to tick the AI-module to do its processing. During this tick the AI module grabs it's input data, does its processing, and sends the data to its data receivers. This method can be used in combination with AIM priorities, to streamline the data flow and timers for timed AI modules.

Reimplemented from AIM.

AIMValue SensorEvent::setProperty	(	const std::string &	key,
		AIMValue	value
	)

virtual

Set the properties

Reimplemented from AIM.

Member Data Documentation

const std::string SensorEvent::FEATURE_NORMALIZATION_MINMAX = "min max normalization"

static

Feature type for the normalization feature: min max

const std::string SensorEvent::FEATURE_NORMALIZATION_STD = "standard score normalization"

static

Feature type for the normalization feature: standard score / z-score

const std::string SensorEvent::PROP_DATA_BUFFER = "data buffer"

static

Property key for the data buffer

const std::string SensorEvent::PROP_FEATURE_NORMALIZATION = "normalization feature"

static

Property key for the normalization feature

property for the normalization feature

const std::string SensorEvent::PROP_FUNC_LEARN_VARIANCE = "func learn variance"

static

Property key to run the learning function, needs as input a map with start_time and end_time epoch values

const std::string SensorEvent::PROP_HIGHEST_VARIANCE = "highest variance"

static

Property key for the highest observed variance vector

const std::string SensorEvent::PROP_IGNORE_START_UP_TIME = "ignore start up time"

static

Property key to ignore waiting for full buffers, if set to true, it will look for events even on not full buffers

const std::string SensorEvent::PROP_LEARN_PERCENTILE = "learn percentile"

static

Property key for the percentile to use for learning

const std::string SensorEvent::PROP_LEARN_RATE = "learn rate"

static

Property key for the variance learn rate

const std::string SensorEvent::PROP_LEARN_RATE_NORM_CNT = "learn rate normalization count"

static

Property key for the normalization count

const std::string SensorEvent::PROP_LEARN_RATE_USE_NORM = "learn rate use normalization"

static

Property key for using normalizaion for the learn rate

const std::string SensorEvent::PROP_LEARN_VARIANCE = "learn variance"

static

Property key to enabled disable the learning of the variance

const std::string SensorEvent::PROP_LEARN_VARIANCE_BUFFER = "learn variance buffer"

static

Property key for the variance buffer for learning

const std::string SensorEvent::PROP_LOWEST_VARIANCE = "lowest variance"

static

Property key for the lowest observed variance vector

const std::string SensorEvent::PROP_STD_SCORE_COUNT = "standard score count"

static

Property key for the standard score count

const std::string SensorEvent::PROP_STD_SCORE_MEAN = "standard score mean"

static

Property key for the standard score mean

properties for standard score normalization

const std::string SensorEvent::PROP_STD_SCORE_VARIANCE = "standard score variance"

static

Property key for the standard score variance

const std::string SensorEvent::PROP_TIME_WINDOW = "time window"

static

Property key for the buffer time window to compute the variance over

const std::string SensorEvent::PROP_USE_ON_OFF_THRESHOLD = "use on off threshold"

static

Property key to enabled disable the use of the on off threshold

const std::string SensorEvent::PROP_VARIANCE = "variance"

static

Property key for a predefined lowest variance vector

properties for min max normalization

const std::string SensorEvent::PROP_VARIANCE_THRESHOLD = "variance threshold"

static

Property key for the variance threshold value, between 0-1, where 0 is lowest variance observed and 1 the highest

const std::string SensorEvent::PROP_VARIANCE_THRESHOLD_OFF = "variance threshold off"

static

Property key for the off threshold variance

The documentation for this class was generated from the following files:

modules/modules/SensorEvent/inc/SensorEvent.h
modules/modules/SensorEvent/src/SensorEvent.cpp

Public Member Functions

Static Public Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation