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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.
Parameters allow users to change the configuration of an algorithm when scheduling an experiment
|Data field used to generate the feature template
|given_name, family_name, email, nationality, id_number, all_five
|Distance to obtain the matching score
|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
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.
All features must have the same length.
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.