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.
| 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 |
| 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
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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.
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.