Forked from robertodaza/prueba1/3

This algorithm is a legacy one. The API has changed since its implementation. New versions and forks will need to be updated.
This algorithm is splittable

Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.

Group: probes

Endpoint Name Data Format Nature
comparison_ids system/array_1d_text/1 Input
keystroke tutorial/atvs_keystroke/1 Input
probe_client_id system/text/1 Input
scores elie_khoury/string_probe_scores/1 Output

Group: templates

Endpoint Name Data Format Nature
template_client_id system/text/1 Input
id system/text/1 Input
features tutorial/atvs_keystroke/1 Input

Parameters allow users to change the configuration of an algorithm when scheduling an experiment

Name Description Type Default Range/Choices
field Data field used to generate the feature template string given_name given_name, family_name, email, nationality, id_number, all_five
distance Distance to obtain the matching score string Modified Scaled Manhattan Scaled Manhattan, Modified Scaled Manhattan, Combined Manhattan-Mahalanobis, Mahalanobis + Nearest Neighbor

Algorithms may use functions and classes declared in libraries. Here you can see the libraries and import names used by this library. You don't need to import the library manually on your code, the platform will do it for you. Just use the object as it has been imported with the selected named. For example, if you choose to import a library using the name lib, then access function f within your code like lib.f().

Library Import as
robertodaza/competition--modified_scaled_distance--scaled_distance--mad-/1 prueba

The code for this algorithm in Python
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This algorithm is designed to be used as a simple enrollment strategy of keystroke data. It enrolls a model from several features by computing the average and standard deviation of the enrollment features.

Note

All features must have the same length.

Experiments

Updated Name Databases/Protocols Analyzers
robertodaza/robertodaza/example2/2/article_one_block atvskeystroke/1@A robertodaza/analyzercompetition/2
robertodaza/robertodaza/example2/2/article_one_block1 atvskeystroke/1@A robertodaza/analyzerahora/1

This table shows the number of times this algorithm has been successfully run using the given environment. Note this does not provide sufficient information to evaluate if the algorithm will run when submitted to different conditions.

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