teda (version 0.1.1)

teda_r: Create teda recursive object from observation (+ state)

Description

A recursive method that takes the state variables of previous mean, previous variance, and the current timestep position, along with the current observation. It returns a teda recursive object. Currently only a univariate implementation.

Usage

teda_r(curr_observation, previous_mean = curr_observation, previous_var = 0, k = 1, dist_type = "Euclidean")

Arguments

curr_observation

A single observation, the most recent in a series

previous_mean

The mean value returned by the previous call to this function, if no previous calls, default value is used.

previous_var

The variance value returned by the previous call to this function, if no previous calls, default value is used.

k

The count of observations processed by the recursive function, including the current observation

dist_type

A string representing the distance metric to use, default value (and currently only supported value) is "Euclidean"

Value

The teda recursive object

Details

The function has two intended ways of use: on the first pass, it only takes the observation value as a paramter and the rest are provided by defaults, on all other passes, it takes the current observation, the previous mean and variance values, and the current k (number of observations) which includes the current observation.

On return, the teda recursive object holds:

• the current observation
• the current mean
• the current variance
• the current observation's eccentricity
• the current observation's typicality
• the current observation's normalised eccentricity
• the current observation's normalised typicality
• whether the current observation is an outlier
• the current outlier threshold
• the next timestep value, k+1

It provides generic functions for print and summary, at this moment both provide the same outout.

References

Bezerra, C.G., Costa, B.S.J., Guedes, L.A. and Angelov, P.P., 2016, May. A new evolving clustering algorithm for online data streams. In Evolving and Adaptive Intelligent Systems (EAIS), 2016 IEEE Conference on (pp. 162-168). IEEE. DOI: 10.1109/EAIS.2016.7502508

See Also

Other TEDA.functions: teda_b

Examples

vec = c(20, 12, 10, 20)
a = teda_r(vec[1])
b = teda_r(vec[2],
           a$curr_mean,
           a$curr_var,
           a$next_k)
c = teda_r(vec[3],
           b$curr_mean,
           b$curr_var,
           b$next_k)

d = teda_r(vec[4],
           c$curr_mean,
           c$curr_var,
           c$next_k)
summary(d)