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 |
|---|---|---|
| scores_dev1 | tutorial/probe_scores/1 | Input |
| scores_dev2 | tutorial/probe_scores/1 | Input |
| machine | tutorial/linear_machine/1 | Output |
xxxxxxxxxxclass Algorithm: def __init__(self): self.positives_dev1 = [] self.negatives_dev1 = [] self.positives_dev2 = [] self.negatives_dev2 = [] self.machine = None def accumulate_scores(self, inputs, input_name): positives_test = [] negatives_test = [] # accumulate the test scores data_test = inputs[input_name].data positives_test.extend([k.score for k in data_test.scores if k.template_identity == data_test.client_identity]) negatives_test.extend([k.score for k in data_test.scores if k.template_identity != data_test.client_identity]) return positives_test,negatives_test #def compute_fused_scores(self, machine, inputs, input_name1, input_name2): #while inputs[input_name].hasMoreData(): def compute_znorm(self, positives, negatives): import numpy data = numpy.concatenate((positives, negatives),axis=0) mean = numpy.mean(data, axis=0) std = numpy.std(data, axis=0) return mean, std def process(self, inputs, outputs): import numpy import bob #Loading the inputs self.positives_dev1, self.negatives_dev1 = self.accumulate_scores(inputs, "scores_dev1") self.positives_dev2, self.negatives_dev2 = self.accumulate_scores(inputs, "scores_dev2") # once all values are received, compute the fusion if not(inputs.hasMoreData()): #Preparing data for fusion positives_dev = numpy.zeros(shape=(len(self.positives_dev1),2)) negatives_dev = numpy.zeros(shape=(len(self.negatives_dev1),2)) positives_dev[:,0] = self.positives_dev1 positives_dev[:,1] = self.positives_dev2 negatives_dev[:,0] = self.negatives_dev1 negatives_dev[:,1] = self.negatives_dev2 #normalizing data subtract, divide = self.compute_znorm(positives_dev,negatives_dev) positives_dev = numpy.divide(positives_dev-subtract, divide) negatives_dev = numpy.divide(negatives_dev-subtract, divide) #Training the logistic regression machine trainer = bob.trainer.CGLogRegTrainer() machine = trainer.train(negatives_dev, positives_dev) outputs["machine"].write({ 'input_subtract': subtract, 'input_divide': divide, 'weights': machine.weights, 'biases': machine.biases, }) return TrueThe code for this algorithm in Python
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| Updated | Name | Databases/Protocols | Analyzers | |||
|---|---|---|---|---|---|---|
| smarcel/tpereira/full_isv_multi/2/btas2015_face-periocular_mobio-female_det | mobio/1@female | tutorial/eerhter_postperf_iso/1 | ||||
| tpereira/tpereira/full_isv_multi/2/btas2015_face-periocular_cpqd-smartphone-male_det | cpqd/1@smartphone_male | tutorial/eerhter_postperf_iso/1 | ||||
| tpereira/tpereira/full_isv_multi/2/btas2015_face-periocular_mobio-male_det | mobio/1@male | tutorial/eerhter_postperf_iso/1 | ||||
| tpereira/tpereira/full_isv_multi/2/btas2015_face-periocular_cpqd-smartphone-female_det | cpqd/1@smartphone_female | tutorial/eerhter_postperf_iso/1 | ||||
| tpereira/tpereira/full_isv_multi/2/btas2015_face-periocular_mobio-female_det | mobio/1@female | tutorial/eerhter_postperf_iso/1 |
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.