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8da0849
Splt the plot_neighbors example into two.
AnnaBobasheva Jul 15, 2025
616e199
Merge branch 'tslearn-team:main' into example_amendment
AnnaBobasheva Jul 22, 2025
5d8f993
Merge branch 'tslearn-team:main' into example_amendment
AnnaBobasheva Jul 23, 2025
c17d280
Fixed: using local datasets to build examples
AnnaBobasheva Jul 23, 2025
3b0fdac
Merge branch 'tslearn-team:main' into example_amendment
AnnaBobasheva Jul 25, 2025
f112ce5
try with Trace
rtavenar Aug 11, 2025
f994daa
Merge branch 'tslearn-team:main' into example_amendment
AnnaBobasheva Aug 13, 2025
185ed87
Update sonarqube.yaml
charavelg Aug 19, 2025
19faa20
Update sonarqube.yaml
charavelg Aug 20, 2025
7c57d33
Merge branch 'tslearn-team:main' into example_amendment
AnnaBobasheva Aug 21, 2025
c17cc7b
Merge branch 'tslearn-team:main' into example_amendment
AnnaBobasheva Sep 10, 2025
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30 changes: 30 additions & 0 deletions docs/datasets/Chinatown/Chinatown.txt
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# PedestrianCountingSystem dataset

The City of Melbourne, Australia has developed an automated pedestrian counting system to better understand pedestrian activity within the municipality, such as how people use different city locations at different time of the day. The data analysis can facility decision making and urban planning for the future.

We extract data of 10 locations for the whole year 2017. We make two datasets from these data.

## MelbournePedestrian (not this file) and Chinatown

Data are pedestrian count in Chinatown-Swanston St (North for 12
months of the year 2017. Classes are based on whether data are from
a normal day or a weekend day.

- Class 1: Weekend
- Class 2: Weekday

Train size: 20

Test size: 343

Missing value: No

Number of classses: 2

Time series length: 24

There is nothing to infer from the order of examples in the train and test set.

Data source: City of Melbourne (see [1]). Data edited by Hoang Anh Dau.

[1] http://www.pedestrian.melbourne.vic.gov.au/#date=11-06-2018&time=4
399 changes: 399 additions & 0 deletions docs/datasets/Chinatown/Chinatown_TEST.arff
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381 changes: 381 additions & 0 deletions docs/datasets/Chinatown/Chinatown_TEST.ts
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343 changes: 343 additions & 0 deletions docs/datasets/Chinatown/Chinatown_TEST.txt
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78 changes: 78 additions & 0 deletions docs/datasets/Chinatown/Chinatown_TRAIN.arff
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%# PedestrianCountingSystem dataset
%
%The City of Melbourne, Australia has developed an automated pedestrian counting system to better understand pedestrian activity within the municipality, such as how people use different city locations at different time of the day. The data analysis can facility decision making and urban planning for the future.
%
%We extract data of 10 locations for the whole year 2017. We make two datasets from these data.
%
%## MelbournePedestrian (not this problem) and
%
%## Chinatown
%
%Data are pedestrian count in Chinatown-Swanston St (North for 12 months of the year 2017. Classes are based on whether data are from a normal day or a weekend day.
%
%- Class 1: Weekend
%- Class 2: Weekday
%
%Train size: 20
%
%Test size: 343
%
%Missing value: No
%
%Number of classses: 2
%
%Time series length: 24
%
%There is nothing to infer from the order of examples in the train and test set.
%
%Data source: City of Melbourne (see [1]). Data edited by Hoang Anh Dau.
%
%[1] http://www.pedestrian.melbourne.vic.gov.au/#date=11-06-2018&time=4
@Relation Chinatown
@attribute att1 numeric
@attribute att2 numeric
@attribute att3 numeric
@attribute att4 numeric
@attribute att5 numeric
@attribute att6 numeric
@attribute att7 numeric
@attribute att8 numeric
@attribute att9 numeric
@attribute att10 numeric
@attribute att11 numeric
@attribute att12 numeric
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@attribute att16 numeric
@attribute att17 numeric
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@attribute att21 numeric
@attribute att22 numeric
@attribute att23 numeric
@attribute att24 numeric
@attribute target {1,2}

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58 changes: 58 additions & 0 deletions docs/datasets/Chinatown/Chinatown_TRAIN.ts
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## PedestrianCountingSystem dataset
#
#The City of Melbourne, Australia has developed an automated pedestrian counting system to better understand pedestrian activity within the municipality, such as how people use different city locations at different time of the day. The data analysis can facility decision making and urban planning for the future.
#
#We extract data of 10 locations for the whole year 2017. We make two datasets from these data.
#
### MelbournePedestrian (not this file) and Chinatown
#
#Data are pedestrian count in Chinatown-Swanston St (North for 12
#months of the year 2017. Classes are based on whether data are from
#a normal day or a weekend day.
#
#- Class 1: Weekend
#- Class 2: Weekday
#
#Train size: 20
#
#Test size: 343
#
#Missing value: No
#
#Number of classses: 2
#
#Time series length: 24
#
#There is nothing to infer from the order of examples in the train and test set.
#
#Data source: City of Melbourne (see [1]). Data edited by Hoang Anh Dau.
#
#[1] http://www.pedestrian.melbourne.vic.gov.au/#date=11-06-2018&time=4
@problemName Chinatown
@timeStamps false
@missing false
@univariate true
@equalLength true
@seriesLength 24
@classLabel true 1 2
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20 changes: 20 additions & 0 deletions docs/datasets/Chinatown/Chinatown_TRAIN.txt
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2.0000000e+00 1.4400000e+02 7.3000000e+01 2.1000000e+01 1.6000000e+01 1.0000000e+01 1.2000000e+01 2.6000000e+01 1.0000000e+02 1.7700000e+02 2.2000000e+02 3.7100000e+02 5.9900000e+02 1.2800000e+03 1.3460000e+03 1.4610000e+03 1.3670000e+03 1.3190000e+03 1.2050000e+03 1.2500000e+03 1.2690000e+03 9.8100000e+02 8.0600000e+02 5.5000000e+02 3.2000000e+02
2.0000000e+00 1.4100000e+02 6.3000000e+01 5.1000000e+01 1.4000000e+01 1.6000000e+01 1.4000000e+01 2.8000000e+01 1.0300000e+02 1.7200000e+02 2.0500000e+02 4.4200000e+02 6.0100000e+02 1.1650000e+03 1.2280000e+03 9.1200000e+02 8.7200000e+02 8.4700000e+02 1.2270000e+03 1.5610000e+03 1.6110000e+03 1.3860000e+03 1.0070000e+03 8.1000000e+02 6.8000000e+02
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2.0000000e+00 6.9000000e+01 5.4000000e+01 3.5000000e+01 6.0000000e+00 7.0000000e+00 1.5000000e+01 1.9000000e+01 7.6000000e+01 2.1200000e+02 2.3800000e+02 3.6400000e+02 7.1700000e+02 1.2230000e+03 1.1660000e+03 9.9400000e+02 9.3100000e+02 9.2900000e+02 1.0930000e+03 1.2750000e+03 1.1610000e+03 1.0830000e+03 7.2600000e+02 4.4100000e+02 2.4100000e+02
2.0000000e+00 1.4200000e+02 1.0400000e+02 5.5000000e+01 3.3000000e+01 5.1000000e+01 1.3000000e+01 3.6000000e+01 7.7000000e+01 1.8500000e+02 2.2200000e+02 4.3700000e+02 7.3900000e+02 1.3260000e+03 1.3720000e+03 9.7900000e+02 9.4200000e+02 1.0190000e+03 1.2500000e+03 1.6630000e+03 1.7810000e+03 1.5130000e+03 1.1880000e+03 1.0000000e+03 6.3800000e+02
2.0000000e+00 2.5600000e+02 1.7100000e+02 1.0400000e+02 6.7000000e+01 5.1000000e+01 2.6000000e+01 2.5000000e+01 4.1000000e+01 1.7000000e+02 1.9200000e+02 3.3400000e+02 8.0100000e+02 1.3410000e+03 1.4680000e+03 1.3950000e+03 1.2210000e+03 1.1680000e+03 1.2840000e+03 1.4000000e+03 1.3210000e+03 1.0990000e+03 7.9100000e+02 4.9800000e+02 2.7200000e+02
2.0000000e+00 1.3000000e+02 6.8000000e+01 5.2000000e+01 2.3000000e+01 1.0000000e+01 1.6000000e+01 2.8000000e+01 1.2800000e+02 2.2600000e+02 2.7100000e+02 4.0200000e+02 6.5200000e+02 1.2820000e+03 1.3530000e+03 1.0840000e+03 1.1240000e+03 9.2000000e+02 1.0630000e+03 1.1600000e+03 1.1840000e+03 8.9300000e+02 7.2000000e+02 5.4100000e+02 3.0400000e+02
2.0000000e+00 7.8000000e+01 6.0000000e+01 5.1000000e+01 1.7000000e+01 1.0000000e+01 1.2000000e+01 2.2000000e+01 7.3000000e+01 1.7200000e+02 1.8600000e+02 3.1800000e+02 4.1400000e+02 1.0030000e+03 1.1530000e+03 9.8100000e+02 8.4600000e+02 8.7200000e+02 1.0510000e+03 1.2000000e+03 1.2760000e+03 1.0040000e+03 8.4100000e+02 5.2500000e+02 3.1500000e+02
2.0000000e+00 9.5000000e+01 6.3000000e+01 4.5000000e+01 2.6000000e+01 3.0000000e+00 2.3000000e+01 2.8000000e+01 1.0800000e+02 2.0900000e+02 2.4100000e+02 3.7200000e+02 5.4900000e+02 1.2060000e+03 1.2230000e+03 1.1560000e+03 1.1020000e+03 1.0830000e+03 1.1070000e+03 1.1090000e+03 1.1930000e+03 9.0000000e+02 6.6000000e+02 4.4200000e+02 2.2600000e+02
2.0000000e+00 8.2000000e+01 9.3000000e+01 5.1000000e+01 2.8000000e+01 3.9000000e+01 1.7000000e+01 3.0000000e+01 1.3800000e+02 2.2500000e+02 2.6100000e+02 3.5800000e+02 6.2100000e+02 1.0740000e+03 1.1760000e+03 9.1400000e+02 8.5800000e+02 8.3000000e+02 9.2800000e+02 9.5400000e+02 8.7100000e+02 7.3000000e+02 5.0600000e+02 2.6200000e+02 1.5000000e+02
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# PedestrianCountingSystem dataset

The City of Melbourne, Australia has developed an automated pedestrian counting system to better understand pedestrian activity within the municipality, such as how people use different city locations at different time of the day. The data analysis can facility decision making and urban planning for the future.

We extract data of 10 locations for the whole year 2017. We make two datasets from these data.

## MelbournePedestrian (not this file) and
## Chinatown

Data are pedestrian count in Chinatown-Swanston St (North for 12 months of the year 2017. Classes are based on whether data are from a normal day or a weekend day.

- Class 1: Weekend
- Class 2: Weekday

Train size: 20

Test size: 343

Missing value: No

Number of classses: 2

Time series length: 24

There is nothing to infer from the order of examples in the train and test set.

Data source: City of Melbourne (see [1]). Data edited by Hoang Anh Dau.

[1] http://www.pedestrian.melbourne.vic.gov.au/#date=11-06-2018&time=4
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