cdt/generators/cyclic_graph_generator.py
def init_variables(self, verbose=False):
        """Redefine the causes of the graph."""
        # Resetting adjacency matrix
        for i in range(self.nodes):
            for j in np.random.choice(range(self.nodes),
                                      np.random.randint(
                                          0, self.parents_max + 1),
                                      replace=False):
                if i != j:
                    self.adjacency_matrix[j, i] = 1

        try:
            assert any([sum(self.adjacency_matrix[:, i]) ==
                        self.parents_max for i in range(self.nodes)])
            self.g = nx.DiGraph(self.adjacency_matrix)
            assert list(nx.simple_cycles(self.g))
            assert any(len(i) == 2 for i in nx.simple_cycles(self.g))

        except AssertionError:
            if verbose:
                print("Regenerating, graph non valid...")
            self.init_variables()

        if verbose:
            print("Matrix generated ! \
              Number of cycles: {}".format(len(list(nx.simple_cycles(self.g)))))

        for i in range(self.nodes):
            self.data.iloc[:, i] = scale(self.initial_generator(self.points))

        # Mechanisms
        self.cfunctions = [self.mechanism(int(sum(self.adjacency_matrix[:, i])),
                                          self.points, self.noise, noise_coeff=self.noise_coeff) for i in range(self.nodes)]
def init_variables(self, verbose=False):
        """Redefine the causes of the graph."""
        # Resetting adjacency matrix
        for i in range(self.nodes):
            for j in np.random.choice(range(self.nodes),
                                      np.random.randint(
                                          0, self.parents_max + 1),
                                      replace=False):
                if i != j:
                    self.adjacency_matrix[j, i] = 1

        try:
            assert any([sum(self.adjacency_matrix[:, i]) ==
                        self.parents_max for i in range(self.nodes)])
            self.g = nx.DiGraph(self.adjacency_matrix)
            assert list(nx.simple_cycles(self.g))
            assert any(len(i) == 2 for i in nx.simple_cycles(self.g))

        except AssertionError:
            if verbose:
                print("Regenerating, graph non valid...")
            self.init_variables()

        if verbose:
            print("Matrix generated ! \
              Number of cycles: {}".format(len(list(nx.simple_cycles(self.g)))))

        for i in range(self.nodes):
            self.data.iloc[:, i] = scale(self.initial_generator(self.points))

        # Mechanisms
        self.cfunctions = [self.mechanism(int(sum(self.adjacency_matrix[:, i])),
                                          self.points, self.noise, noise_coeff=self.noise_coeff) for i in range(self.nodes)]
def init_variables ( self , verbose = False ) : # Resetting adjacency matrix for i in range ( self . nodes ) : for j in np . random . choice ( range ( self . nodes ) , np . random . randint ( 0 , self . parents_max + 1 ) , replace = False ) : if i != j : self . adjacency_matrix [ j , i ] = 1 try : assert any ( [ sum ( self . adjacency_matrix [ : , i ] ) == self . parents_max for i in range ( self . nodes ) ] ) self . g = nx . DiGraph ( self . adjacency_matrix ) assert list ( nx . simple_cycles ( self . g ) ) assert any ( len ( i ) == 2 for i in nx . simple_cycles ( self . g ) ) except AssertionError : if verbose : print ( "Regenerating, graph non valid..." ) self . init_variables ( ) if verbose : print ( "Matrix generated ! \
              Number of cycles: {}" . format ( len ( list ( nx . simple_cycles ( self . g ) ) ) ) ) for i in range ( self . nodes ) : self . data . iloc [ : , i ] = scale ( self . initial_generator ( self . points ) ) # Mechanisms self . cfunctions = [ self . mechanism ( int ( sum ( self . adjacency_matrix [ : , i ] ) ) , self . points , self . noise , noise_coeff = self . noise_coeff ) for i in range ( self . nodes ) ]
Redefine the causes of the graph.
Redefine the causes of the graph .