def allocate_observation_matrix(self):
        """!
        @brief Allocates observation matrix in line with output dynamic of the network.
        @details Matrix where state of each neuron is denoted by zero/one in line with Heaviside function on each iteration.
        
        @return (list) Observation matrix of the network dynamic.
        
        """
        number_neurons = len(self.output[0])
        observation_matrix = []
        
        for iteration in range(len(self.output)):
            obervation_column = []
            for index_neuron in range(number_neurons):
                obervation_column.append(heaviside(self.output[iteration][index_neuron]))
            
            observation_matrix.append(obervation_column)
        
        return observation_matrix
def allocate_observation_matrix(self):
        """!
        @brief Allocates observation matrix in line with output dynamic of the network.
        @details Matrix where state of each neuron is denoted by zero/one in line with Heaviside function on each iteration.
        
        @return (list) Observation matrix of the network dynamic.
        
        """
        number_neurons = len(self.output[0])
        observation_matrix = []
        
        for iteration in range(len(self.output)):
            obervation_column = []
            for index_neuron in range(number_neurons):
                obervation_column.append(heaviside(self.output[iteration][index_neuron]))
            
            observation_matrix.append(obervation_column)
        
        return observation_matrix
def allocate_observation_matrix ( self ) : number_neurons = len ( self . output [ 0 ] ) observation_matrix = [ ] for iteration in range ( len ( self . output ) ) : obervation_column = [ ] for index_neuron in range ( number_neurons ) : obervation_column . append ( heaviside ( self . output [ iteration ] [ index_neuron ] ) ) observation_matrix . append ( obervation_column ) return observation_matrix
!
        @brief Allocates observation matrix in line with output dynamic of the network.
        @details Matrix where state of each neuron is denoted by zero/one in line with Heaviside function on each iteration.
        
        @return (list) Observation matrix of the network dynamic.
!