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 |
|---|---|---|
| feature_vectors | system/array_1d_floats/1 | Input |
| scores | system/array_1d_floats/1 | Output |
| Endpoint Name | Data Format | Nature |
|---|---|---|
| machine | system/array_1d_floats/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 compares an input histogram to a histogram model. It computes a score between the input histogram and the model using Chi-2 distance measurement. The output (score) is packaged into a numpy array for compatibility with the subsequent analyzer step.
| Updated | Name | Databases/Protocols | Analyzers | |||
|---|---|---|---|---|---|---|
| smarcel/ivana7c/simple-antispoofing-updated/1/replay2-antispoofing-lbp-histograms-fix | replay/3@grandtest | Kanma/iqm_spoof_eer_analyzer/1 | ||||
| sbhatta/ivana7c/simple-antispoofing-updated/1/replay2-antispoofing-lbp-histograms | replay/2@grandtest | sbhatta/iqm_spoof_eer_analyzer/9 |
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.