BEAT - robertodaza/enroll-keystroke-proof1-commented/1

This algorithm is a legacy one. The API has changed since its implementation. New versions and forks will need to be updated.

This algorithm is splittable

Endpoint Groups 1

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.

Group: main

Endpoint Name	Data Format	Nature
keystroke	tutorial/atvs_keystroke/1	Input
template_id	system/text/1	Input
keystroke_model	aythamimm/keystroke_model/6	Output

import numpy
##Del toolchain es la parte del modeling el cual tiene dos entradas que son keystroke y id que conectan con la base datos de templates con cuatro salidas.
## modeling tiene una salida llamada keystroke_model.
class Algorithm:##Trabajamos con objetos

def __init__(self):
        self._features1 = [] ##variables globales definidas
        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#llamamos a la entrada y data para coger todos los valores de tutorial/atvs_keystroke/1(es como los ejemplos de cada  usuario)
        
        f1 = data.holdtime.given_name#En f1 tenemos los valores del holdtime de la parte del nombre
        f2 = data.rplatency.given_name#Igual pero con rplatency ver tutorial/atvs_keystroke/1 en data formats para tener mas claro de que tipo son las variables
        feature_vector=[]#creamos un vector
        l_f=len(f1)#comprobamos la longitud de f1 (numero de elementos) ya que esta definido como lista y no como array
        for i_k in range(l_f):#Creamos un for hasta el numero de elementos 
            feature_vector.append(float(f1[i_k]))#en feature_vector vamos poniendo los valores de f1 y transformandolos a float ya que son int32?
        
        l_f=len(f2)#igual que hicimos con f1
        for i_k in range(l_f):
            feature_vector.append(float(f2[i_k])) #creamos una fila con los holdtime del nombre seguido de los rplatency             
                
        self._features1.append(feature_vector)#en la variable global self.features1 anadimos todo lo de feature_vector por filas vamos teniendo en cada fila el   ejemplo de cada usuario 1.....6
                      
        f1 = data.holdtime.family_name#hacemos lo mismo con 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#igual con 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#igual con 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#con dni igual
        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():# En esta plataforma no existen los bucles asi que usamos esta operacion que usa la entrada template_id que es    como el usuario por el que va, comprueba si el usuario ya ha mandado todos los ejemplos de 1 hasta 6 y si es asi entra en el if.
            features11 = numpy.average(self._features1, axis=0)#guardamos en features11 el valor de usar la funcion de numpy average con axis=0 para que nos haga 
            #la media por columnas
            features12 = numpy.std(self._features1, axis=0)#desviacion tipica por columnas
            features21 = numpy.average(self._features2, axis=0)
            features22 = numpy.std(self._features2, axis=0)      
            features31 = numpy.average(self._features3, axis=0)
            features32 = numpy.std(self._features3, axis=0)      
            features41 = numpy.average(self._features4, axis=0)
            features42 = numpy.std(self._features4, axis=0)      
            features51 = numpy.average(self._features5, axis=0)
            features52 = numpy.std(self._features5, axis=0)      
            # outputs data
            outputs["keystroke_model"].write({#grabamos los datos de salida en keystroke_model con esta operacion
                'given_name_average': 		features11,#guardamos en media del nombre el valor que teniamos en features11 que tiene que ser float64 ver  aythamimm/keystroke_model/6
                'given_name_std': 			features12,
                'family_name_average': 		features21,
                'family_name_std': 			features22,
                'email_average': 			features31,
                'email_std': 				features32,
                'nationality_average': 		features41,
                'nationality_std': 			features42,
                'id_number_average': 		features51,
                'id_number_std': 			features52,
            }) 
            
            self._features1 = []#Reiniciamos las variables globales para un nuevo usuario
            self._features2 = []
            self._features3 = []
            self._features4 = []
            self._features5 = []

return True

The code for this algorithm in Python
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This algorithm is designed to be used as a simple enrollment strategy. It enrolls a model from several features by computing the average of the enrollment features.

Note

All features must have the same length.

No experiments are using this algorithm.

This algorithm was never executed.

algorithms robertodaza enroll-keystroke-proof1-commented 1

Endpoint Groups 1

Group: main

algorithms

robertodaza

enroll-keystroke-proof1-commented

1