コード例 #1
0
    def get_independencies(self, latex=False):
        """
        Computes independencies in the Bayesian Network, by checking d-seperation.

        Parameters
        ----------
        latex: boolean
            If latex=True then latex string of the independence assertion
            would be created.

        Examples
        --------
        >>> from pgmpy.models import BayesianModel
        >>> chain = BayesianModel([('X', 'Y'), ('Y', 'Z')])
        >>> chain.get_independencies()
        (X _|_ Z | Y)
        (Z _|_ X | Y)
        """
        independencies = Independencies()
        for start in (self.nodes()):
            rest = set(self.nodes()) - {start}
            for r in range(len(rest)):
                for observed in itertools.combinations(rest, r):
                    d_seperated_variables = rest - set(observed) - set(
                        self.active_trail_nodes(start, observed=observed))
                    if d_seperated_variables:
                        independencies.add_assertions(
                            [start, d_seperated_variables, observed])

        independencies.reduce()

        if not latex:
            return independencies
        else:
            return independencies.latex_string()
コード例 #2
0
    def get_independencies(self, latex=False):
        """
        Computes independencies in the Bayesian Network, by checking d-seperation.

        Parameters
        ----------
        latex: boolean
            If latex=True then latex string of the independence assertion
            would be created.

        Examples
        --------
        >>> from pgmpy.models import BayesianModel
        >>> chain = BayesianModel([('X', 'Y'), ('Y', 'Z')])
        >>> chain.get_independencies()
        (X _|_ Z | Y)
        (Z _|_ X | Y)
        """
        independencies = Independencies()
        for start in (self.nodes()):
            rest = set(self.nodes()) - {start}
            for r in range(len(rest)):
                for observed in itertools.combinations(rest, r):
                    d_seperated_variables = rest - set(observed) - set(
                        self.active_trail_nodes(start, observed=observed)[start])
                    if d_seperated_variables:
                        independencies.add_assertions([start, d_seperated_variables, observed])

        independencies.reduce()

        if not latex:
            return independencies
        else:
            return independencies.latex_string()
コード例 #3
0
ファイル: BayesianModel.py プロジェクト: Sayan-Paul/kod
    def get_independencies(self, latex=False):
        """
        Compute independencies in Bayesian Network.

        Parameters
        ----------
        latex: boolean
            If latex=True then latex string of the independence assertion
            would be created.

        Examples
        --------
        >>> from pgmpy.models import BayesianModel
        >>> student = BayesianModel()
        >>> student.add_nodes_from(['diff', 'intel', 'grades', 'letter', 'sat'])
        >>> student.add_edges_from([('diff', 'grades'), ('intel', 'grades'), ('grade', 'letter'),
        ...                         ('intel', 'sat')])
        >>> student.get_independencies()
        """
        independencies = Independencies()
        for start in (self.nodes()):
            for r in (1, len(self.nodes())):
                for observed in itertools.combinations(self.nodes(), r):
                    independent_variables = self.active_trail_nodes(start, observed=observed)
                    independent_variables = set(independent_variables) - {start}
                    if independent_variables:
                        independencies.add_assertions([start, independent_variables,
                                                       observed])

        independencies.reduce()

        if not latex:
            return independencies
        else:
            return independencies.latex_string()
コード例 #4
0
ファイル: MarkovModel.py プロジェクト: EJHortala/books-2
    def get_local_independencies(self, latex=False):
        """
        Returns all the local independencies present in the markov model.

        Local independencies are the independence assertion in the form of
        .. math:: {X \perp W - {X} - MB(X) | MB(X)}
        where MB is the markov blanket of all the random variables in X

        Parameters
        ----------
        latex: boolean
            If latex=True then latex string of the indepedence assertion would
            be created

        Examples
        --------
        >>> from pgmpy.models import MarkovModel
        >>> mm = MarkovModel()
        >>> mm.add_nodes_from(['x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7'])
        >>> mm.add_edges_from([('x1', 'x3'), ('x1', 'x4'), ('x2', 'x4'),
        ...                    ('x2', 'x5'), ('x3', 'x6'), ('x4', 'x6'),
        ...                    ('x4', 'x7'), ('x5', 'x7')])
        >>> mm.get_local_independecies()
        """
        from pgmpy.exceptions import RequiredError
        local_independencies = Independencies()

        all_vars = set(self.nodes())
        for node in self.nodes():
            markov_blanket = set(self.markov_blanket(node))
            rest = all_vars - set([node]) - markov_blanket
            try:
                local_independencies.add_assertions(
                    [node, list(rest), list(markov_blanket)])
            except RequiredError:
                pass

        local_independencies.reduce()

        if latex:
            return local_independencies.latex_string()
        else:
            return local_independencies
コード例 #5
0
ファイル: MarkovModel.py プロジェクト: ankurankan/pgmpy
    def get_local_independencies(self, latex=False):
        """
        Returns all the local independencies present in the markov model.

        Local independencies are the independence assertion in the form of
        .. math:: {X \perp W - {X} - MB(X) | MB(X)}
        where MB is the markov blanket of all the random variables in X

        Parameters
        ----------
        latex: boolean
            If latex=True then latex string of the indepedence assertion would
            be created

        Examples
        --------
        >>> from pgmpy.models import MarkovModel
        >>> mm = MarkovModel()
        >>> mm.add_nodes_from(['x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7'])
        >>> mm.add_edges_from([('x1', 'x3'), ('x1', 'x4'), ('x2', 'x4'),
        ...                    ('x2', 'x5'), ('x3', 'x6'), ('x4', 'x6'),
        ...                    ('x4', 'x7'), ('x5', 'x7')])
        >>> mm.get_local_independecies()
        """
        from pgmpy.exceptions import RequiredError
        local_independencies = Independencies()

        all_vars = set(self.nodes())
        for node in self.nodes():
            markov_blanket = set(self.markov_blanket(node))
            rest = all_vars - set([node]) - markov_blanket
            try:
                local_independencies.add_assertions([node, list(rest), list(markov_blanket)])
            except RequiredError:
                pass

        local_independencies.reduce()

        if latex:
            return local_independencies.latex_string()
        else:
            return local_independencies
コード例 #6
0
ファイル: BayesianModel.py プロジェクト: EJHortala/books-2
    def get_independencies(self, latex=False):
        """
        Compute independencies in Bayesian Network.

        Parameters
        ----------
        latex: boolean
            If latex=True then latex string of the independence assertion
            would be created.

        Examples
        --------
        >>> from pgmpy.models import BayesianModel
        >>> student = BayesianModel()
        >>> student.add_nodes_from(['diff', 'intel', 'grades', 'letter', 'sat'])
        >>> student.add_edges_from([('diff', 'grades'), ('intel', 'grades'), ('grade', 'letter'),
        ...                         ('intel', 'sat')])
        >>> student.get_independencies()
        """
        independencies = Independencies()
        for start in (self.nodes()):
            for r in (1, len(self.nodes())):
                for observed in itertools.combinations(self.nodes(), r):
                    independent_variables = self.active_trail_nodes(
                        start, observed=observed)
                    independent_variables = set(independent_variables) - {
                        start
                    }
                    if independent_variables:
                        independencies.add_assertions(
                            [start, independent_variables, observed])

        independencies.reduce()

        if not latex:
            return independencies
        else:
            return independencies.latex_string()