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_uint64/1 Input
probe chichan/a-collection-of-data/2 Input
probe_id system/uint64/1 Input
probe_client_id system/uint64/1 Input
scores tutorial/probe_scores/1 Output

Group: models

Endpoint Name Data Format Nature
model_id system/uint64/1 Input
model chichan/a-collection-of-data/2 Input
model_client_id system/uint64/1 Input

The code for this algorithm in Python
The ruler at 80 columns indicate suggested POSIX line breaks (for readability).
The editor will automatically enlarge to accomodate the entirety of your input
Use keyboard shortcuts for search/replace and faster editing. For example, use Ctrl-F (PC) or Cmd-F (Mac) to search through this box

This algorithm computes the distance between two features using cosine angle.

Since cosineangle is a distance measure in scipy, but scores should be similarity values (with higher values being better), the negative 1 − cosineangle distance is actually returned.

Experiments

Updated Name Databases/Protocols Analyzers
smarcel/chichan/full_pre_mlbphs_projection/2/mobio-f_TT_MLBPH_PCA98_LDA300_postperf-iso mobio/2@female tutorial/eerhter_postperf_iso/1
smarcel/chichan/full_pre_mlbphs_projection/2/mobio-m_TT_MLBPH_PCA98_LDA300_postperf-iso mobio/2@male tutorial/eerhter_postperf_iso/1
smarcel/chichan/full_pre_mlbphs_projection/2/mobio-m_TT_MLBPH_PCA98_postperf-iso mobio/2@male tutorial/eerhter_postperf_iso/1
smarcel/chichan/full_pre_mlbphs_projection/2/mobio-f_TT_MLBPH_PCA98_postperf-iso mobio/2@female tutorial/eerhter_postperf_iso/1
chichan/chichan/full_pre_mlbphs_projection/2/Prep_MLBPH_XM2VTS_nouniform_PCA xm2vts/1@darkened-lp1,xm2vts/1@lp1 tutorial/eerhter_postperf/1
chichan/chichan/full_pre_mlbphs_projection/2/Prep_MLBPH_XM2VTS_LDA xm2vts/1@darkened-lp1,xm2vts/1@lp1 tutorial/eerhter_postperf/1
chichan/chichan/full_pre_mlbphs_projection/2/Prep_MLBPH_XM2VTS_no_uniform_p98LDA xm2vts/1@darkened-lp1,xm2vts/1@lp1 tutorial/eerhter_postperf/1
chichan/chichan/full_pre_mlbphs_projection/2/Prep_MLBPH_XM2VTS_PCA xm2vts/1@darkened-lp1,xm2vts/1@lp1 tutorial/eerhter_postperf/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.

Terms of Service | Contact Information | BEAT platform version 2.2.1b0 | © Idiap Research Institute - 2013-2024