Computes the similarity between two histograms

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 system/array_1d_floats/1 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 system/array_1d_floats/1 Input
model_client_id system/uint64/1 Input

The code for this algorithm in Python
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This algorithm computes the similarity between two histograms using bob.math.chi_square. For two given histograms H and M, the χ2 distance is defined as:

χ2(H, M) = i((Hi − Mi)2)/((Hi + Mi))

Since χ2 is a distance measure, but scores should be similarity values (with higher values being better), the negative χ2 distance is actually returned.

No experiments are using this algorithm.

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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