View source: R/extract_event_ftrs.R
extract_event_ftrs  R Documentation 
This function extracts events from a 2D or 3D data stream and computes a set of 30 features for 2D streams and 13 features for 3D streams, by using a moving window. 2D data streams with class labels can be generated by using the function gen_stream
. To get the class labels of the extracted events for the supervised setting, the event position is matched with the details
of the events, which is part of the output of the gen_stream
function.
extract_event_ftrs( stream, supervised = FALSE, details = NULL, win_size = 200, step_size = 20, thres = 0.95, folder = NULL, vis = FALSE, tt = 10, epsilon = 5, miniPts = 10, rolling = TRUE )
stream 
A data stream. This can be the output of either the 
supervised 
If 
details 
Event details. This is also an output of the 
win_size 
The window length of the moving window model, default is set to 
step_size 
The window is moved by the 
thres 
The cutoff quantile. Default is set to 
folder 
If set to a local folder, this is where the jpegs of window data and extracted events are saved for a 2D data stream. 
vis 
If 
tt 
Related to event ages. For example if 
epsilon 
The 
miniPts 
The 
rolling 
This parameter is set to 
An Nx22x4
array is returned for 2D data streams and an Nx13x4
array for 3D data streams. Here N
is the total number of events extracted from all windows. The second dimension has m
features and the class label for the supervised
setting. The third dimension has 4
different event ages : tt, 2tt, 3tt, 4tt
.
For example, the element at [10,6,3]
has the 6th feature, of the 10th extracted event when the age of the event is 3tt
. The features for 2D streams are listed below. For 3D streams the features cluster_id, pixels, length, width, height, total_value, l2w_ratio, centroid_x, centroid_y, centroid_z, mean, std_dev
and sd_from_global_mean
are computed.

An identification number for each event. 

The number of pixels of each event. 

The length of the event. 

The width of the event. 

The total value of the pixels. 

Length to width ratio of event. 

x coordinate of event centroid. 

y coordinate of event centroid. 

Mean value of event pixels. 

Standard deviation of event pixels. 

The slope of an 

The linear coefficient of a second order polynomial fitted to event pixels using 

The quadratic coefficient of a second order polynomial fitted to event pixels using 

The proportion of event pixels/cells that has values greater than 2 global standard deviations from the global mean of the window. 

The proportion of event pixels/cells that has values greater than 3 global standard deviations from the global mean of the window. 

The proportion of event pixels/cells that has values greater than 4 global standard deviations from the global mean of the window. 

A small portion of each window and its column medians and column IQRs are used to construct two smoothing splines: a median spline and an IQR spline. The value of the median smoothing spline at each event centroid is used as the local median for that event. Similarly, the value of the IQR smoothing spline at each event centroid is used as the local IQR for that event. This feature gives the proportion of event pixels/cells that has values greater than 5 local IQRs from the local median. 

The proportion of event pixels/cells that has values greater than 6 local IQRs from the local median computed using splines. 

The proportion of event pixels/cells that has values greater than 7 local IQRs from the local median computed using splines. 

The proportion of event pixels/cells that has values greater than 8 local IQRs from the local median computed using splines. 

Let us denote the 75th percentile of the event pixels value by 

Let us denote the 80th percentile of the event pixels value by 
# 2D data stream example out < gen_stream(1, sd=15) zz < as.matrix(out$data) features < extract_event_ftrs(zz, supervised=TRUE, details = out$details) features # 3D data stream example set.seed(1) arr < array(rnorm(12000),dim=c(40,25,30)) arr[25:33,12:20, 20:23] < 10 # getting events ftrs < extract_event_ftrs(arr, supervised=FALSE, win_size=10, step_size = 2, tt=2, thres=0.985) ftrs
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