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 |
|---|---|---|
| template_id | system/array_1d_text/1 | Input |
| client_id | system/text/1 | Input |
| scores | elie_khoury/string_probe_scores/1 | Input |
Analyzers may produce any number of results. Once experiments using this analyzer are done, you may display the results or filter experiments using criteria based on them.
| Name | Type |
|---|---|
| client_id1 | string |
| template_id | string |
| mean_eer | float32 |
| std | float32 |
xxxxxxxxxximport bobimport numpyclass Algorithm: def __init__(self): self.mean_eer = [] self.mi_user= [] self.positives = [None] * 63 self.negatives = [None] * 63 self.flag=0 def process(self, inputs, output): # initialize the score vector if self.flag==0: self.flag=1 for i in range(63): self.positives[i] = [] self.negatives[i] = [] # read the input data data = inputs['scores'].data client_id = inputs['client_id'].data.text user1 = inputs['template_id'].data.text user=inputs["client_id"].data.text if user[0]=="G": self.mi_user=numpy.int32(user[8:len(user)]) if user[0]=="I": self.mi_user=numpy.int32(user[9:len(user)]) self.positives[self.mi_user-1].extend([k.score for k in data.scores if k.template_identity == data.client_identity]) self.negatives[self.mi_user-1].extend([k.score for k in data.scores if k.template_identity != data.client_identity]) if not(inputs.hasMoreData()): for iuser in range(63): positive_scores=self.positives[iuser] negative_scores=self.negatives[iuser] eer_threshold = bob.measure.eer_threshold(negative_scores, positive_scores) far, frr = bob.measure.farfrr(negative_scores, positive_scores, eer_threshold) self.mean_eer.append(numpy.float32((far+frr) / 2.)) # writes the output back to the platform output.write({ 'mean_eer': numpy.float32(numpy.average(self.mean_eer)), #average of all 63 EER 'std': numpy.float32(numpy.std(self.mean_eer)), 'client_id1': user, 'template_id': user1, }) return TrueThe code for this algorithm in Python
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| Updated | Name | Databases/Protocols | Analyzers | |||
|---|---|---|---|---|---|---|
| robertodaza/aythamimm/atvs_keystroke_btas_benchmark/1/proof5 | atvskeystroke/1@A | robertodaza/proof0/3 | ||||
| robertodaza/aythamimm/atvs_keystroke_btas_benchmark/1/proof4 | atvskeystroke/1@A | robertodaza/proof0/3 |
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.