Bob 2.0 implementation of feature projection on a linear machine

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: main

Endpoint Name Data Format Nature
features system/array_1d_floats/1 Input
scores system/float/1 Output

Unnamed group

Endpoint Name Data Format Nature
classifier tutorial/linear_machine/1 Input

The code for this algorithm in Python
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Project input features on a given linear machine (e.g., logistic regression)

Experiments

Updated Name Databases/Protocols Analyzers
pkorshunov/pkorshunov/isv-asv-pad-fusion-complete/1/asv_isv-pad_lbp_hist_ratios_lr-fusion_lr-pa_aligned avspoof/2@physicalaccess_verify_train_spoof,avspoof/2@physicalaccess_antispoofing,avspoof/2@physicalaccess_verification,avspoof/2@physicalaccess_verification_spoof,avspoof/2@physicalaccess_verify_train pkorshunov/spoof-score-fusion-roc_hist/1
pkorshunov/pkorshunov/speech-pad-simple/1/speech-pad_lbp_hist_ratios_lr-pa_aligned avspoof/2@physicalaccess_antispoofing pkorshunov/simple_antispoofing_analyzer/4
pkorshunov/pkorshunov/speech-antispoofing-baseline/1/btas2016-baseline-pa avspoof/1@physicalaccess_antispoofing pkorshunov/simple_antispoofing_analyzer/2

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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