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 |
xxxxxxxxxximport numpyclass Algorithm: def __init__(self): self.data = [] self.machine = None def process(self, inputs, outputs): if self.machine == None: inputs['machine'].next() self.machine = inputs["machine"].data.value sample = inputs['feature_vectors'].data.value tmp = numpy.square(sample - self.machine) tmp = numpy.nan_to_num(numpy.divide(tmp, self.machine)) score = -numpy.sum(tmp) score = numpy.array([score]) outputs["scores"].write({ 'value': score }) return TrueThe code for this algorithm in Python
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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.