extract_event_ftrs.RdThis 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
)A data stream. This can be the output of either the gen_stream function or the stream_from_files function.
If TRUE, event class labels need to be given in details.
Event details. This is also an output of the gen_stream function. Event details are used to get the class labels of the extracted events, by matching the position.
The window length of the moving window model, default is set to 200.
The window is moved by the step_size, default is 20.
The cut-off quantile. Default is set to 0.95. Values greater than the quantile will be clustered. The rest is not clustered.
If set to a local folder, this is where the jpegs of window data and extracted events are saved for a 2D data stream.
If TRUE, the window data and the extracted events are plotted for a 2D data stream.
Related to event ages. For example if tt=10 then the event ages are 10, 20, 30 and 40.
The eps parameter in dbscan function in the package dbscan
The minPts parameter in dbscan function in the package dbscan
This parameter is set to TRUE if rolling windows are considered.
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.
cluster_idAn identification number for each event.
pixelsThe number of pixels of each event.
lengthThe length of the event.
widthThe width of the event.
total_valueThe total value of the pixels.
l2w_ratioLength to width ratio of event.
centroid_xx coordinate of event centroid.
centroid_yy coordinate of event centroid.
meanMean value of event pixels.
std_devStandard deviation of event pixels.
avg_slopeThe slope of an lm object fitted to the event pixels.
quad_1The linear coefficient of a second order polynomial fitted to event pixels using lm.
quad_2The quadratic coefficient of a second order polynomial fitted to event pixels using lm.
2sd_from_meanThe proportion of event pixels/cells that has values greater than 2 global standard deviations from the global mean of the window.
3sd_from_meanThe proportion of event pixels/cells that has values greater than 3 global standard deviations from the global mean of the window.
4sd_from_meanThe proportion of event pixels/cells that has values greater than 4 global standard deviations from the global mean of the window.
5iqr_from_medianA 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.
6iqr_from_medianThe proportion of event pixels/cells that has values greater than 6 local IQRs from the local median computed using splines.
7iqr_from_medianThe proportion of event pixels/cells that has values greater than 7 local IQRs from the local median computed using splines.
8iqr_from_medianThe proportion of event pixels/cells that has values greater than 8 local IQRs from the local median computed using splines.
iqr_from_medianLet us denote the 75th percentile of the event pixels value by x. How many local IQRs is x is away from the local median? Both local IQR and local median are computed using splines. That value is given by this feature.
sd_from_meanLet us denote the 80th percentile of the event pixels value by x. How many global standard deviations is x is away from the global mean? Here both global values are computed from window data.
# 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
#> , , 1
#>
#> cluster_id pixels length width total_value l2w_ratio centroid_x
#> [1,] 1 37 16 10 183.3779 1.6 33.56757
#> [2,] 2 59 15 10 280.4103 1.5 49.49153
#> [3,] 3 61 22 10 284.1976 2.2 200.47541
#> [4,] 1 36 16 10 181.3300 1.6 33.75000
#> [5,] 3 60 22 10 282.1391 2.2 200.33333
#> [6,] 1 37 16 10 183.3779 1.6 33.56757
#> [7,] 3 61 22 10 284.1976 2.2 200.47541
#> [8,] 1 38 16 10 185.3756 1.6 33.57895
#> [9,] 2 61 22 10 284.1976 2.2 200.47541
#> [10,] 1 38 16 10 185.3756 1.6 33.57895
#> [11,] 1 38 16 10 185.3756 1.6 33.57895
#> [12,] 3 59 19 10 268.4860 1.9 216.08475
#> [13,] 1 38 16 10 185.3756 1.6 33.57895
#> [14,] 3 60 19 10 270.4541 1.9 216.10000
#> [15,] 1 38 16 10 185.3756 1.6 33.57895
#> [16,] 3 63 23 10 276.2645 2.3 216.20635
#> centroid_y mean std_dev avg_slope quad_1 quad_2
#> [1,] 179.48649 4.956161 2.079082 0.06080882 1.121259 1.5355043
#> [2,] 30.30508 4.752717 1.442758 0.09630952 1.611567 2.0044179
#> [3,] 83.95082 4.658977 1.726299 -0.06479737 -1.720489 -5.5349682
#> [4,] 159.47222 5.036943 2.048836 0.10860294 2.002539 0.8408557
#> [5,] 64.01667 4.702318 1.707071 -0.02485216 -0.623263 -4.9002591
#> [6,] 139.48649 4.956161 2.079082 0.06080882 1.121259 1.5355043
#> [7,] 43.95082 4.658977 1.726299 -0.06479737 -1.720489 -5.5349682
#> [8,] 119.47368 4.878304 2.106205 0.06105392 1.125779 1.4891944
#> [9,] 23.95082 4.658977 1.726299 -0.06479737 -1.720489 -5.5349682
#> [10,] 99.47368 4.878304 2.106205 0.06105392 1.125779 1.4891944
#> [11,] 79.47368 4.878304 2.106205 0.06105392 1.125779 1.4891944
#> [12,] 192.05085 4.550611 1.954643 -0.16689568 -3.310419 2.8690624
#> [13,] 59.47368 4.878304 2.106205 0.06105392 1.125779 1.4891944
#> [14,] 172.00000 4.507568 1.966478 -0.16796796 -3.331688 2.9153940
#> [15,] 39.47368 4.878304 2.106205 0.06105392 1.125779 1.4891944
#> [16,] 151.92063 4.385150 1.996117 -0.16889710 -3.858846 1.8319275
#> 2sd_from_mean 3sd_from_mean 4sd_from_mean 5iqr_from_median
#> [1,] 0.8648649 0.7027027 0.5135135 0.08108108
#> [2,] 0.8983051 0.7627119 0.5423729 0.00000000
#> [3,] 0.9016393 0.6721311 0.4590164 0.13114754
#> [4,] 0.8333333 0.5555556 0.4166667 0.02777778
#> [5,] 0.8333333 0.5833333 0.3166667 0.11666667
#> [6,] 0.8648649 0.6756757 0.4864865 0.21621622
#> [7,] 0.9016393 0.6721311 0.4590164 0.11475410
#> [8,] 0.8421053 0.6842105 0.5000000 0.23684211
#> [9,] 0.9016393 0.6721311 0.4590164 0.00000000
#> [10,] 0.7894737 0.5263158 0.3947368 0.21052632
#> [11,] 0.7894737 0.5263158 0.3684211 0.21052632
#> [12,] 0.7118644 0.5084746 0.3050847 0.01694915
#> [13,] 0.7368421 0.5000000 0.3157895 0.18421053
#> [14,] 0.6833333 0.4500000 0.2833333 0.00000000
#> [15,] 0.7105263 0.4736842 0.2368421 0.18421053
#> [16,] 0.6349206 0.4126984 0.1746032 0.00000000
#> 6iqr_from_median 7iqr_from_median 8iqr_from_median iqr_from_median
#> [1,] 0.02702703 0.00000000 0 3.855885
#> [2,] 0.00000000 0.00000000 0 2.421684
#> [3,] 0.03278689 0.01639344 0 4.425198
#> [4,] 0.00000000 0.00000000 0 3.341198
#> [5,] 0.03333333 0.01666667 0 4.313005
#> [6,] 0.08108108 0.02702703 0 4.811563
#> [7,] 0.03278689 0.01639344 0 4.333460
#> [8,] 0.10526316 0.02631579 0 4.945289
#> [9,] 0.00000000 0.00000000 0 2.688216
#> [10,] 0.07894737 0.02631579 0 4.770118
#> [11,] 0.07894737 0.02631579 0 4.857381
#> [12,] 0.00000000 0.00000000 0 3.339955
#> [13,] 0.07894737 0.02631579 0 4.579405
#> [14,] 0.00000000 0.00000000 0 2.808292
#> [15,] 0.07894737 0.02631579 0 4.533396
#> [16,] 0.00000000 0.00000000 0 2.708738
#> sd_from_global_mean Class
#> [1,] 5.648562 0
#> [2,] 5.216775 1
#> [3,] 5.096371 0
#> [4,] 4.932792 0
#> [5,] 4.404409 0
#> [6,] 5.537365 0
#> [7,] 4.997283 0
#> [8,] 5.608588 0
#> [9,] 5.116656 0
#> [10,] 4.825159 0
#> [11,] 4.700423 0
#> [12,] 4.438933 0
#> [13,] 4.457583 0
#> [14,] 4.197708 0
#> [15,] 4.175066 0
#> [16,] 3.904362 0
#>
#> , , 2
#>
#> cluster_id pixels length width total_value l2w_ratio centroid_x
#> [1,] 1 108 21 20 541.6973 1.050000 34.50926
#> [2,] 2 168 17 20 941.9876 0.850000 49.79762
#> [3,] 3 143 22 20 662.8071 1.100000 200.50350
#> [4,] 1 107 21 20 539.6493 1.050000 34.57944
#> [5,] 3 139 22 20 654.6504 1.100000 200.41007
#> [6,] 1 108 21 20 541.6973 1.050000 34.50926
#> [7,] 3 143 22 20 662.8071 1.100000 200.50350
#> [8,] 1 109 21 20 543.6949 1.050000 34.50459
#> [9,] 2 143 22 20 662.8071 1.100000 200.50350
#> [10,] 1 109 21 20 543.6949 1.050000 34.50459
#> [11,] 1 110 21 20 545.6786 1.050000 34.42727
#> [12,] 3 85 19 14 402.8697 1.357143 216.04706
#> [13,] 1 110 21 20 545.6786 1.050000 34.42727
#> [14,] 3 127 23 20 611.8245 1.150000 216.29921
#> [15,] 1 111 21 20 547.6134 1.050000 34.43243
#> [16,] 3 131 23 20 619.5690 1.150000 216.35115
#> centroid_y mean std_dev avg_slope quad_1 quad_2
#> [1,] 184.72222 5.015715 1.917244 0.41199278 9.609240 0.62048193
#> [2,] 35.67857 5.607069 1.939474 0.19850540 3.745064 4.41364026
#> [3,] 89.21678 4.635015 1.925628 -0.10730131 -3.017729 -8.55573222
#> [4,] 164.76636 5.043451 1.904373 0.44722563 10.431004 -0.07922287
#> [5,] 69.20863 4.709715 1.901205 -0.07221529 -2.030974 -7.66435661
#> [6,] 144.72222 5.015715 1.917244 0.41199278 9.609240 0.62048193
#> [7,] 49.21678 4.635015 1.925628 -0.10730131 -3.017729 -8.55573222
#> [8,] 124.66972 4.988026 1.930119 0.41279276 9.627899 0.76177047
#> [9,] 29.21678 4.635015 1.925628 -0.10730131 -3.017729 -8.55573222
#> [10,] 104.66972 4.988026 1.930119 0.41279276 9.627899 0.76177047
#> [11,] 84.69091 4.960715 1.942481 0.36499864 8.513158 1.99325093
#> [12,] 193.92941 4.739644 2.227368 -0.13625636 -2.702680 0.21514681
#> [13,] 64.69091 4.960715 1.942481 0.36499864 8.513158 1.99325093
#> [14,] 177.07087 4.817516 2.281926 0.23237994 5.769599 5.55523682
#> [15,] 44.74775 4.933454 1.954846 0.36539352 8.522368 1.84655134
#> [16,] 156.94656 4.729534 2.301006 0.15455226 3.837270 3.37390474
#> 2sd_from_mean 3sd_from_mean 4sd_from_mean 5iqr_from_median
#> [1,] 0.9259259 0.7222222 0.5277778 0.055555556
#> [2,] 0.9464286 0.8511905 0.6547619 0.005952381
#> [3,] 0.8881119 0.6293706 0.4195804 0.181818182
#> [4,] 0.8878505 0.5887850 0.4112150 0.037383178
#> [5,] 0.8345324 0.5395683 0.3021583 0.158273381
#> [6,] 0.9259259 0.7129630 0.5092593 0.194444444
#> [7,] 0.8881119 0.6083916 0.3986014 0.153846154
#> [8,] 0.9174312 0.7155963 0.5229358 0.211009174
#> [9,] 0.8881119 0.6293706 0.4195804 0.006993007
#> [10,] 0.8715596 0.5779817 0.4036697 0.192660550
#> [11,] 0.8545455 0.5727273 0.3454545 0.200000000
#> [12,] 0.7294118 0.4941176 0.3058824 0.047058824
#> [13,] 0.8090909 0.5363636 0.2636364 0.145454545
#> [14,] 0.7165354 0.4645669 0.2913386 0.015748031
#> [15,] 0.7657658 0.4954955 0.2072072 0.135135135
#> [16,] 0.6641221 0.4274809 0.2290076 0.015267176
#> 6iqr_from_median 7iqr_from_median 8iqr_from_median iqr_from_median
#> [1,] 0.009259259 0.00000000 0.000000000 3.676004
#> [2,] 0.000000000 0.00000000 0.000000000 2.868293
#> [3,] 0.062937063 0.02097902 0.006993007 4.431416
#> [4,] 0.000000000 0.00000000 0.000000000 3.141142
#> [5,] 0.043165468 0.02158273 0.007194245 4.336104
#> [6,] 0.064814815 0.03703704 0.000000000 4.690779
#> [7,] 0.041958042 0.02097902 0.006993007 4.339350
#> [8,] 0.082568807 0.03669725 0.009174312 4.847355
#> [9,] 0.000000000 0.00000000 0.000000000 2.692249
#> [10,] 0.064220183 0.03669725 0.000000000 4.681272
#> [11,] 0.063636364 0.03636364 0.009090909 4.729170
#> [12,] 0.023529412 0.00000000 0.000000000 3.352369
#> [13,] 0.054545455 0.03636364 0.000000000 4.448749
#> [14,] 0.000000000 0.00000000 0.000000000 2.888270
#> [15,] 0.054054054 0.03603604 0.000000000 4.372450
#> [16,] 0.000000000 0.00000000 0.000000000 2.802021
#> sd_from_global_mean Class
#> [1,] 5.448264 0
#> [2,] 5.851952 1
#> [3,] 5.314207 0
#> [4,] 4.718896 0
#> [5,] 4.641578 0
#> [6,] 5.341459 0
#> [7,] 5.210342 0
#> [8,] 5.452707 0
#> [9,] 5.336044 0
#> [10,] 4.689473 0
#> [11,] 4.552219 0
#> [12,] 4.685819 0
#> [13,] 4.316763 0
#> [14,] 4.502564 0
#> [15,] 4.028951 0
#> [16,] 4.178036 0
#>
#> , , 3
#>
#> cluster_id pixels length width total_value l2w_ratio centroid_x
#> [1,] 1 175 21 28 905.9538 0.7500000 34.77143
#> [2,] 2 214 21 27 1283.9096 0.7777778 49.82243
#> [3,] 3 230 25 30 1103.8297 0.8333333 199.83913
#> [4,] 1 178 21 30 933.9063 0.7000000 34.80899
#> [5,] 3 224 25 30 1091.6033 0.8333333 199.79911
#> [6,] 1 180 21 30 937.9922 0.7000000 34.77222
#> [7,] 3 229 25 30 1101.8243 0.8333333 199.86463
#> [8,] 1 181 21 30 939.9898 0.7000000 34.76796
#> [9,] 2 230 25 30 1103.8297 0.8333333 199.83913
#> [10,] 1 181 21 30 939.9898 0.7000000 34.76796
#> [11,] 1 182 21 30 941.9736 0.7000000 34.71978
#> [12,] 3 85 19 14 402.8697 1.3571429 216.04706
#> [13,] 1 183 23 30 943.9425 0.7666667 34.66120
#> [14,] 3 211 23 30 1078.9145 0.7666667 216.45024
#> [15,] 1 184 23 30 945.8772 0.7666667 34.66304
#> [16,] 3 215 23 30 1086.6590 0.7666667 216.47907
#> centroid_y mean std_dev avg_slope quad_1 quad_2
#> [1,] 189.20571 5.176879 1.976714 0.4536933 10.581854 -6.1531110
#> [2,] 37.84579 5.999578 2.208274 -0.1282088 -2.814072 10.8925901
#> [3,] 94.55217 4.799260 2.013812 -0.3356108 -10.363011 -5.0421556
#> [4,] 169.56180 5.246665 1.948217 0.4888621 11.402126 -7.4518985
#> [5,] 74.57589 4.873229 1.988432 -0.2941425 -9.082549 -3.9701965
#> [6,] 149.53889 5.211068 1.966354 0.4543452 10.597061 -6.8175977
#> [7,] 54.52402 4.811460 2.009686 -0.3323418 -10.262069 -4.9984438
#> [8,] 129.48066 5.193314 1.975378 0.4552143 10.617331 -6.6641078
#> [9,] 34.55217 4.799260 2.013812 -0.3356108 -10.363011 -5.0421556
#> [10,] 109.48066 5.193314 1.975378 0.4552143 10.617331 -6.6641078
#> [11,] 89.46703 5.175679 1.984228 0.4074202 9.502590 -5.4326273
#> [12,] 193.92941 4.739644 2.227368 -0.1362564 -2.702680 0.2151468
#> [13,] 69.49727 5.158156 1.992917 0.2413005 6.160442 0.5377480
#> [14,] 182.75829 5.113339 2.320973 0.3487596 9.020325 -0.8121940
#> [15,] 49.50543 5.140637 2.001621 0.2413863 6.162632 0.5242852
#> [16,] 162.57674 5.054228 2.339103 0.2802139 7.247458 -2.8091457
#> 2sd_from_mean 3sd_from_mean 4sd_from_mean 5iqr_from_median
#> [1,] 0.9428571 0.7314286 0.5657143 0.068571429
#> [2,] 0.9485981 0.8598131 0.6962617 0.009345794
#> [3,] 0.8913043 0.6695652 0.4434783 0.191304348
#> [4,] 0.8988764 0.6292135 0.4438202 0.016853933
#> [5,] 0.8392857 0.5937500 0.3080357 0.165178571
#> [6,] 0.9333333 0.7333333 0.5666667 0.233333333
#> [7,] 0.8951965 0.6593886 0.4235808 0.170305677
#> [8,] 0.9392265 0.7403315 0.5745856 0.254143646
#> [9,] 0.8913043 0.6695652 0.4434783 0.008695652
#> [10,] 0.8839779 0.6187845 0.4364641 0.232044199
#> [11,] 0.8736264 0.6153846 0.3846154 0.236263736
#> [12,] 0.7294118 0.4941176 0.3058824 0.047058824
#> [13,] 0.8306011 0.5846995 0.3169399 0.196721311
#> [14,] 0.7535545 0.5260664 0.3364929 0.023696682
#> [15,] 0.7826087 0.5543478 0.2500000 0.184782609
#> [16,] 0.7162791 0.4837209 0.2790698 0.018604651
#> 6iqr_from_median 7iqr_from_median 8iqr_from_median iqr_from_median
#> [1,] 0.01142857 0.00000000 0.000000000 3.735735
#> [2,] 0.00000000 0.00000000 0.000000000 3.208130
#> [3,] 0.08695652 0.03913043 0.013043478 4.506284
#> [4,] 0.00000000 0.00000000 0.000000000 3.197875
#> [5,] 0.07142857 0.04017857 0.004464286 4.416744
#> [6,] 0.08888889 0.03333333 0.000000000 4.827126
#> [7,] 0.06986900 0.03930131 0.004366812 4.419719
#> [8,] 0.10497238 0.03867403 0.011049724 5.008628
#> [9,] 0.00000000 0.00000000 0.000000000 2.741086
#> [10,] 0.08839779 0.03314917 0.000000000 4.838559
#> [11,] 0.09340659 0.03296703 0.010989011 4.912924
#> [12,] 0.02352941 0.00000000 0.000000000 3.352369
#> [13,] 0.07650273 0.02732240 0.000000000 4.611791
#> [14,] 0.00000000 0.00000000 0.000000000 3.168817
#> [15,] 0.06521739 0.02717391 0.000000000 4.554401
#> [16,] 0.00000000 0.00000000 0.000000000 3.064317
#> sd_from_global_mean Class
#> [1,] 5.729361 0
#> [2,] 6.506826 1
#> [3,] 5.377321 0
#> [4,] 5.034917 0
#> [5,] 4.660514 0
#> [6,] 5.693595 0
#> [7,] 5.272369 0
#> [8,] 5.822888 0
#> [9,] 5.399608 0
#> [10,] 5.011696 0
#> [11,] 4.868097 0
#> [12,] 4.685819 0
#> [13,] 4.603014 0
#> [14,] 4.910284 0
#> [15,] 4.298551 0
#> [16,] 4.573272 0
#>
#> , , 4
#>
#> cluster_id pixels length width total_value l2w_ratio centroid_x
#> [1,] 1 165 21 27 848.4729 0.7777778 34.72727
#> [2,] 2 213 17 25 1281.8273 0.6800000 49.87324
#> [3,] 3 1065 29 121 5419.0785 0.2396694 199.97559
#> [4,] 1 177 21 29 926.9383 0.7241379 34.81356
#> [5,] 3 1251 29 141 6573.2877 0.2056738 199.99201
#> [6,] 1 179 21 29 931.0242 0.7241379 34.77654
#> [7,] 3 1272 29 142 6616.4101 0.2042254 199.96384
#> [8,] 1 180 21 29 933.0218 0.7241379 34.77222
#> [9,] 2 1277 29 142 6626.4702 0.2042254 199.93970
#> [10,] 1 180 21 29 933.0218 0.7241379 34.77222
#> [11,] 1 181 21 29 935.0056 0.7241379 34.72376
#> [12,] 3 81 19 13 380.5403 1.4615385 216.01235
#> [13,] 1 182 23 29 936.9745 0.7931034 34.66484
#> [14,] 3 215 23 32 1094.1840 0.7187500 216.40465
#> [15,] 1 184 23 30 945.8772 0.7666667 34.66304
#> [16,] 3 219 23 32 1101.9284 0.7187500 216.43379
#> centroid_y mean std_dev avg_slope quad_1 quad_2
#> [1,] 188.55152 5.142260 1.990118 0.4339769 10.121993 -4.839128212
#> [2,] 37.78873 6.017969 2.196988 0.1858477 3.506259 3.259963441
#> [3,] 142.49765 5.088337 2.202152 0.8577889 36.036966 3.411458873
#> [4,] 169.49153 5.236940 1.949406 0.4896461 11.420411 -7.313439292
#> [5,] 133.75140 5.254427 2.240238 0.8182996 35.488712 -0.008268052
#> [6,] 149.46927 5.201252 1.967443 0.4551292 10.615346 -6.679138472
#> [7,] 113.57862 5.201580 2.258817 0.5959960 25.847662 8.544109044
#> [8,] 129.41111 5.183454 1.976417 0.4560032 10.635732 -6.524764385
#> [9,] 93.47298 5.189092 2.263177 0.6671916 28.935333 5.675609227
#> [10,] 109.41111 5.183454 1.976417 0.4560032 10.635732 -6.524764385
#> [11,] 89.39779 5.165777 1.985217 0.4082091 9.520991 -5.293283927
#> [12,] 193.62963 4.698028 2.255406 -0.1606581 -3.186695 0.503404014
#> [13,] 69.42857 5.148212 1.993857 0.2414045 6.163096 0.676376407
#> [14,] 183.02791 5.089228 2.318326 0.3077255 7.959017 -0.793123111
#> [15,] 49.50543 5.140637 2.001621 0.2413863 6.162632 0.524285172
#> [16,] 162.84475 5.031637 2.335618 0.2391777 6.186095 -2.784319675
#> 2sd_from_mean 3sd_from_mean 4sd_from_mean 5iqr_from_median
#> [1,] 0.9393939 0.7212121 0.5575758 0.066666667
#> [2,] 0.9530516 0.8638498 0.6995305 0.009389671
#> [3,] 0.9032864 0.6882629 0.4892019 0.258215962
#> [4,] 0.8983051 0.6271186 0.4406780 0.016949153
#> [5,] 0.8577138 0.6171063 0.4068745 0.257394085
#> [6,] 0.9329609 0.7318436 0.5642458 0.229050279
#> [7,] 0.9103774 0.6886792 0.4960692 0.257861635
#> [8,] 0.9388889 0.7388889 0.5722222 0.250000000
#> [9,] 0.9091621 0.6961629 0.5090055 0.021143305
#> [10,] 0.8833333 0.6166667 0.4333333 0.227777778
#> [11,] 0.8729282 0.6132597 0.3812155 0.232044199
#> [12,] 0.7160494 0.4691358 0.3086420 0.049382716
#> [13,] 0.8296703 0.5824176 0.3131868 0.192307692
#> [14,] 0.7488372 0.5255814 0.3302326 0.023255814
#> [15,] 0.7826087 0.5543478 0.2500000 0.184782609
#> [16,] 0.7123288 0.4840183 0.2739726 0.018264840
#> 6iqr_from_median 7iqr_from_median 8iqr_from_median iqr_from_median
#> [1,] 0.012121212 0.00000000 0.00000000 3.723050
#> [2,] 0.000000000 0.00000000 0.00000000 3.210489
#> [3,] 0.127699531 0.05539906 0.01596244 5.073808
#> [4,] 0.000000000 0.00000000 0.00000000 3.193906
#> [5,] 0.116706635 0.05195843 0.01838529 5.026133
#> [6,] 0.089385475 0.03351955 0.00000000 4.815507
#> [7,] 0.122641509 0.05424528 0.01965409 5.061449
#> [8,] 0.105555556 0.03888889 0.01111111 4.993319
#> [9,] 0.002349256 0.00000000 0.00000000 3.164550
#> [10,] 0.088888889 0.03333333 0.00000000 4.823830
#> [11,] 0.093922652 0.03314917 0.01104972 4.897816
#> [12,] 0.024691358 0.00000000 0.00000000 3.346877
#> [13,] 0.076923077 0.02747253 0.00000000 4.597791
#> [14,] 0.000000000 0.00000000 0.00000000 3.128995
#> [15,] 0.065217391 0.02717391 0.00000000 4.554401
#> [16,] 0.000000000 0.00000000 0.00000000 3.028950
#> sd_from_global_mean Class
#> [1,] 5.729361 0
#> [2,] 6.522669 1
#> [3,] 5.890820 0
#> [4,] 4.992176 0
#> [5,] 5.212672 0
#> [6,] 5.633081 0
#> [7,] 5.892551 0
#> [8,] 5.754156 0
#> [9,] 6.032089 0
#> [10,] 4.951867 0
#> [11,] 4.809631 0
#> [12,] 4.681336 0
#> [13,] 4.551558 0
#> [14,] 4.881772 0
#> [15,] 4.298551 0
#> [16,] 4.506743 0
#>
# 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
#> , , 1
#>
#> cluster_id pixels length width height total_value l2w_ratio centroid_x
#> [1,] 2 3 2 5 4 7.026350 0.4000000 1.666667
#> [2,] 3 2 2 3 1 4.619362 0.6666667 3.500000
#> [3,] 3 7 2 9 6 16.329740 0.2222222 1.714286
#> [4,] 2 2 2 4 2 4.443618 0.5000000 2.500000
#> [5,] 4 6 2 9 7 17.318289 0.2222222 1.333333
#> [6,] 6 7 2 9 6 16.329740 0.2222222 1.714286
#> [7,] 5 2 2 4 2 4.443618 0.5000000 2.500000
#> [8,] 1 2 2 5 6 4.803209 0.4000000 7.500000
#> [9,] 1 72 2 9 4 720.000000 0.2222222 7.500000
#> [10,] 1 72 2 9 4 720.000000 0.2222222 5.500000
#> centroid_y centroid_z mean std_dev slope quad1 quad2
#> [1,] 6.333333 17.33333 2.342117 0.066946507 -0.108070035 0 0
#> [2,] 9.000000 27.00000 2.309681 0.049137050 0.069490283 0 0
#> [3,] 19.428571 11.14286 2.332820 0.164627906 -0.241653531 0 0
#> [4,] 7.500000 10.50000 2.221809 0.053628615 -0.075842315 0 0
#> [5,] 11.333333 16.00000 2.886381 0.722372738 -0.426618997 0 0
#> [6,] 19.428571 23.14286 2.332820 0.164627906 -0.241653531 0 0
#> [7,] 7.500000 22.50000 2.221809 0.053628615 -0.075842315 0 0
#> [8,] 19.000000 18.50000 2.401605 0.003888737 -0.005499505 0 0
#> [9,] 16.000000 21.50000 10.000000 0.000000000 0.000000000 0 0
#> [10,] 16.000000 21.50000 10.000000 0.000000000 0.000000000 0 0
#> sd_from_global_mean Class
#> [1,] 18.08103 0
#> [2,] 26.82941 0
#> [3,] 12.21618 0
#> [4,] 10.30462 0
#> [5,] 17.18624 0
#> [6,] 23.68555 0
#> [7,] 21.77399 0
#> [8,] 19.07910 0
#> [9,] 23.37742 0
#> [10,] 24.10146 0
#>
#> , , 2
#>
#> cluster_id pixels length width height total_value l2w_ratio centroid_x
#> [1,] 2 8 4 11 5 18.631382 0.3636364 2.625000
#> [2,] 3 4 3 6 4 9.991864 0.5000000 4.250000
#> [3,] 3 11 4 10 7 27.686329 0.4000000 2.272727
#> [4,] 2 2 2 4 2 4.443618 0.5000000 2.500000
#> [5,] 4 8 4 9 7 22.176576 0.4444444 2.000000
#> [6,] 6 11 4 10 7 27.686329 0.4000000 2.272727
#> [7,] 5 2 2 4 2 4.443618 0.5000000 2.500000
#> [8,] 1 74 4 10 8 724.803209 0.4000000 9.445946
#> [9,] 1 144 4 9 4 1440.000000 0.4444444 8.500000
#> [10,] 1 144 4 9 4 1440.000000 0.4444444 6.500000
#> centroid_y centroid_z mean std_dev slope quad1
#> [1,] 7.25000 17.00000 2.328923 0.04843969 -0.02001538 -0.04475575
#> [2,] 8.00000 26.75000 2.497966 0.37876994 0.20565760 0.29084377
#> [3,] 20.36364 12.00000 2.516939 0.36773529 0.07910776 0.17689032
#> [4,] 7.50000 10.50000 2.221809 0.05362862 -0.07584231 0.00000000
#> [5,] 11.25000 16.50000 2.772072 0.65703415 -0.18361442 -0.39665248
#> [6,] 20.36364 24.00000 2.516939 0.36773529 0.07910776 0.17689032
#> [7,] 7.50000 22.50000 2.221809 0.05362862 -0.07584231 0.00000000
#> [8,] 16.08108 21.41892 9.794638 1.24058314 3.03880820 6.79498171
#> [9,] 16.00000 21.50000 10.000000 0.00000000 0.00000000 0.00000000
#> [10,] 16.00000 21.50000 10.000000 0.00000000 0.00000000 0.00000000
#> quad2 sd_from_global_mean Class
#> [1,] 0.068672425 18.87633 0
#> [2,] 0.111180149 27.22707 0
#> [3,] -0.075534847 13.36312 0
#> [4,] 0.000000000 10.30462 0
#> [5,] 0.181847976 17.75971 0
#> [6,] -0.075534847 24.83249 0
#> [7,] 0.000000000 21.77399 0
#> [8,] 0.002749752 21.93345 0
#> [9,] 0.000000000 23.37742 0
#> [10,] 0.000000000 24.10146 0
#>
#> , , 3
#>
#> cluster_id pixels length width height total_value l2w_ratio centroid_x
#> [1,] 2 15 6 12 11 36.07217 0.5000000 3.933333
#> [2,] 3 8 6 6 7 19.15381 1.0000000 5.750000
#> [3,] 3 12 5 10 7 29.89010 0.5000000 2.500000
#> [4,] 2 7 6 8 7 15.46767 0.7500000 5.428571
#> [5,] 4 10 6 9 7 27.21067 0.6666667 2.800000
#> [6,] 6 12 5 10 7 29.89010 0.5000000 2.500000
#> [7,] 5 7 6 8 7 15.46767 0.7500000 5.428571
#> [8,] 1 74 4 10 8 724.80321 0.4000000 9.445946
#> [9,] 1 144 4 9 4 1440.00000 0.4444444 8.500000
#> [10,] 1 216 6 9 4 2160.00000 0.6666667 7.500000
#> centroid_y centroid_z mean std_dev slope quad1
#> [1,] 6.866667 17.00000 2.404811 0.22145188 2.684344e-02 1.122942e-01
#> [2,] 7.500000 26.75000 2.394226 0.27233391 -1.211868e-02 -5.025964e-02
#> [3,] 20.750000 12.00000 2.490842 0.36208921 -3.224912e-02 -1.019807e-01
#> [4,] 5.428571 13.00000 2.209668 0.09859642 -5.492709e-03 -2.264702e-02
#> [5,] 11.800000 16.60000 2.721067 0.61131389 -8.969827e-02 -3.444927e-01
#> [6,] 20.750000 24.00000 2.490842 0.36208921 -3.224912e-02 -1.019807e-01
#> [7,] 5.428571 25.00000 2.209668 0.09859642 -5.492709e-03 -2.264702e-02
#> [8,] 16.081081 21.41892 9.794638 1.24058314 3.038808e+00 6.794982e+00
#> [9,] 16.000000 21.50000 10.000000 0.00000000 0.000000e+00 0.000000e+00
#> [10,] 16.000000 21.50000 10.000000 0.00000000 -1.243191e-15 -5.200640e-15
#> quad2 sd_from_global_mean Class
#> [1,] 1.116847e-01 18.87633 0
#> [2,] -2.566593e-01 27.42589 0
#> [3,] -3.379889e-01 13.17197 0
#> [4,] 1.005986e-01 14.12775 0
#> [5,] 2.895315e-01 17.37740 0
#> [6,] -3.379889e-01 24.64133 0
#> [7,] 1.005986e-01 25.59712 0
#> [8,] 2.749752e-03 21.93345 0
#> [9,] 0.000000e+00 23.37742 0
#> [10,] 4.747513e-15 24.10146 0
#>
#> , , 4
#>
#> cluster_id pixels length width height total_value l2w_ratio centroid_x
#> [1,] 2 20 8 12 12 47.82791 0.6666667 4.750000
#> [2,] 3 9 7 6 7 22.79338 1.1666667 6.111111
#> [3,] 3 12 5 10 7 29.89010 0.5000000 2.500000
#> [4,] 2 8 8 8 7 18.15160 1.0000000 5.875000
#> [5,] 4 10 6 9 7 27.21067 0.6666667 2.800000
#> [6,] 6 12 5 10 7 29.89010 0.5000000 2.500000
#> [7,] 5 8 8 8 7 18.15160 1.0000000 5.875000
#> [8,] 1 74 4 10 8 724.80321 0.4000000 9.445946
#> [9,] 1 144 4 9 4 1440.00000 0.4444444 8.500000
#> [10,] 1 216 6 9 4 2160.00000 0.6666667 7.500000
#> centroid_y centroid_z mean std_dev slope quad1
#> [1,] 6.650000 16.75000 2.391396 0.2017860 6.123477e-04 3.968466e-03
#> [2,] 7.555556 26.66667 2.532598 0.4870484 1.278800e-01 6.766772e-01
#> [3,] 20.750000 12.00000 2.490842 0.3620892 -3.224912e-02 -1.019807e-01
#> [4,] 5.250000 13.25000 2.268950 0.1909116 4.889742e-02 2.817442e-01
#> [5,] 11.800000 16.60000 2.721067 0.6113139 -8.969827e-02 -3.444927e-01
#> [6,] 20.750000 24.00000 2.490842 0.3620892 -3.224912e-02 -1.019807e-01
#> [7,] 5.250000 25.25000 2.268950 0.1909116 4.889742e-02 2.817442e-01
#> [8,] 16.081081 21.41892 9.794638 1.2405831 3.038808e+00 6.794982e+00
#> [9,] 16.000000 21.50000 10.000000 0.0000000 0.000000e+00 0.000000e+00
#> [10,] 16.000000 21.50000 10.000000 0.0000000 -1.243191e-15 -5.200640e-15
#> quad2 sd_from_global_mean Class
#> [1,] -8.046562e-02 18.87633 0
#> [2,] 5.305460e-01 27.22707 0
#> [3,] -3.379889e-01 13.17197 0
#> [4,] 3.356362e-01 14.31890 0
#> [5,] 2.895315e-01 17.37740 0
#> [6,] -3.379889e-01 24.64133 0
#> [7,] 3.356362e-01 25.78827 0
#> [8,] 2.749752e-03 21.93345 0
#> [9,] 0.000000e+00 23.37742 0
#> [10,] 4.747513e-15 24.10146 0
#>