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 |
|---|---|---|
| keystroke | tutorial/atvs_keystroke/1 | Input |
| template_id | system/text/1 | Input |
| keystroke_model | aythamimm/keystroke_model/8 | Output |
xxxxxxxxxximport numpydef mad(lst): if len(lst) < 1: return None if not(isinstance(lst[0], list)): vec=[]; m=numpy.median(lst); for i in range(len(lst)): vec.append(abs(lst[i]-m)); return numpy.median(vec) else: mad_vec=[]; for i in range(len(lst[0])): vec=[]; for j in range(len(lst)): vec.append(lst[j][i]); m=numpy.mean(vec); vec=[]; for j in range(len(lst)): vec.append(abs(lst[j][i]-m)); mad_vec.append(numpy.mean(vec)+0.00001); return mad_vecclass Algorithm: def __init__(self): self._features1 = [] self._features2 = [] self._features3 = [] self._features4 = [] self._features5 = [] def process(self, inputs, outputs): # collect all the features for the current template data = inputs['keystroke'].data f1 = data.holdtime.given_name f2 = data.rplatency.given_name feature_vector=[] l_f=len(f1) for i_k in range(l_f): feature_vector.append(float(f1[i_k])) l_f=len(f2) for i_k in range(l_f): feature_vector.append(float(f2[i_k])) self._features1.append(feature_vector) f1 = data.holdtime.family_name f2 = data.rplatency.family_name feature_vector=[] l_f=len(f1) for i_k in range(l_f): feature_vector.append(float(f1[i_k])) l_f=len(f2) for i_k in range(l_f): feature_vector.append(float(f2[i_k])) self._features2.append(feature_vector) f1 = data.holdtime.email f2 = data.rplatency.email feature_vector=[] l_f=len(f1) for i_k in range(l_f): feature_vector.append(float(f1[i_k])) l_f=len(f2) for i_k in range(l_f): feature_vector.append(float(f2[i_k])) self._features3.append(feature_vector) f1 = data.holdtime.nationality f2 = data.rplatency.nationality feature_vector=[] l_f=len(f1) for i_k in range(l_f): feature_vector.append(float(f1[i_k])) l_f=len(f2) for i_k in range(l_f): feature_vector.append(float(f2[i_k])) self._features4.append(feature_vector) f1 = data.holdtime.id_number f2 = data.rplatency.id_number feature_vector=[] l_f=len(f1) for i_k in range(l_f): feature_vector.append(float(f1[i_k])) l_f=len(f2) for i_k in range(l_f): feature_vector.append(float(f2[i_k])) self._features5.append(feature_vector) # Calculate mean and std if inputs["template_id"].isDataUnitDone(): features11 = numpy.average(self._features1, axis=0) features12 = numpy.std(self._features1, axis=0) features13 = mad(self._features1) features21 = numpy.average(self._features2, axis=0) features22 = numpy.std(self._features2, axis=0) features23 = mad(self._features2) features31 = numpy.average(self._features3, axis=0) features32 = numpy.std(self._features3, axis=0) features33 = mad(self._features3) features41 = numpy.average(self._features4, axis=0) features42 = numpy.std(self._features4, axis=0) features43 = mad(self._features4) features51 = numpy.average(self._features5, axis=0) features52 = numpy.std(self._features5, axis=0) features53 = mad(self._features5) # outputs data outputs["keystroke_model"].write({ 'given_name_average': features11, 'given_name_std': features12, 'given_name_mad': features13, 'family_name_average': features21, 'family_name_std': features22, 'family_name_mad': features23, 'email_average': features31, 'email_std': features32, 'email_mad': features33, 'nationality_average': features41, 'nationality_std': features42, 'nationality_mad': features43, 'id_number_average': features51, 'id_number_std': features52, 'id_number_mad': features53, }) self._features1 = [] self._features2 = [] self._features3 = [] self._features4 = [] self._features5 = [] return TrueThe code for this algorithm in Python
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This algorithm is designed to be used as a simple enrollment strategy of keystroke data. It enrolls a model from several features by computing the average and standard deviation of the enrollment features.
Note
All features must have the same length.
| Updated | Name | Databases/Protocols | Analyzers | |||
|---|---|---|---|---|---|---|
| robertodaza/aythamimm/atvs_keystroke_btas_benchmark/1/proof6 | atvskeystroke/1@A | robertodaza/proof0/4 | ||||
| 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 | ||||
| robertodaza/aythamimm/atvs_keystroke_btas_benchmark/1/proof3-template_ids | atvskeystroke/1@A | robertodaza/proof0/2 | ||||
| robertodaza/aythamimm/atvs_keystroke_btas_benchmark/1/proof0 | atvskeystroke/1@A | robertodaza/proof0/1 | ||||
| aythamimm/aythamimm/atvs_keystroke_btas_benchmark/1/ATVS_keystroke_beckmark_btas2015 | atvskeystroke/1@A | aythamimm/keystroke_btas15_analyzer/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.