コード例 #1
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def revise(csp, Xi, Xj, removals):
    "Return true if we remove a value."
    revised = False
    for x in csp.curr_domains[Xi][:]:
        # If Xi=x conflicts with Xj=y for every possible y, eliminate Xi=x
        if every(lambda y: not csp.constraints(Xi, x, Xj, y),
                 csp.curr_domains[Xj]):
            csp.prune(Xi, x, removals)
            revised = True
    return revised
コード例 #2
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 def add(self, node_spec):
     """Add a node to the net. Its parents must already be in the
     net, and its variable must not."""
     node = BayesNode(*node_spec)
     assert node.variable not in self.variables
     assert every(lambda parent: parent in self.variables, node.parents)
     self.nodes.append(node)
     self.variables.append(node.variable)
     for parent in node.parents:
         self.variable_node(parent).children.append(node)
コード例 #3
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ファイル: probability.py プロジェクト: hegc/aima-python
 def add(self, node_spec):
     """Add a node to the net. Its parents must already be in the
     net, and its variable must not."""
     node = BayesNode(*node_spec)
     assert node.variable not in self.variables
     assert every(lambda parent: parent in self.variables, node.parents)
     self.nodes.append(node)
     self.variables.append(node.variable)
     for parent in node.parents:
         self.variable_node(parent).children.append(node)
コード例 #4
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ファイル: csp.py プロジェクト: hegc/aima-python
def revise(csp, Xi, Xj, removals):
    "Return true if we remove a value."
    revised = False
    for x in csp.curr_domains[Xi][:]:
        # If Xi=x conflicts with Xj=y for every possible y, eliminate Xi=x
        if every(lambda y: not csp.constraints(Xi, x, Xj, y),
                 csp.curr_domains[Xj]):
            csp.prune(Xi, x, removals)
            revised = True
    return revised
コード例 #5
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ファイル: logic.py プロジェクト: riteshkasat/aima-python
def is_definite_clause(s):
    """returns True for exprs s of the form A & B & ... & C ==> D,
    where all literals are positive.  In clause form, this is
    ~A | ~B | ... | ~C | D, where exactly one clause is positive.
    >>> is_definite_clause(expr('Farmer(Mac)'))
    True
    """
    if is_symbol(s.op):
        return True
    elif s.op == '==>':
        antecedent, consequent = s.args
        return (is_symbol(consequent.op) and every(
            lambda arg: is_symbol(arg.op), conjuncts(antecedent)))
    else:
        return False
コード例 #6
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ファイル: logic.py プロジェクト: hegc/aima-python
def is_definite_clause(s):
    """returns True for exprs s of the form A & B & ... & C ==> D,
    where all literals are positive.  In clause form, this is
    ~A | ~B | ... | ~C | D, where exactly one clause is positive.
    >>> is_definite_clause(expr('Farmer(Mac)'))
    True
    """
    if is_symbol(s.op):
        return True
    elif s.op == '==>':
        antecedent, consequent = s.args
        return (is_symbol(consequent.op) and
                every(lambda arg: is_symbol(arg.op), conjuncts(antecedent)))
    else:
        return False
コード例 #7
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    def __init__(self, X, parents, cpt):
        """X is a variable name, and parents a sequence of variable
        names or a space-separated string.  cpt, the conditional
        probability table, takes one of these forms:

        * A number, the unconditional probability P(X=true). You can
          use this form when there are no parents.

        * A dict {v: p, ...}, the conditional probability distribution
          P(X=true | parent=v) = p. When there's just one parent.

        * A dict {(v1, v2, ...): p, ...}, the distribution P(X=true |
          parent1=v1, parent2=v2, ...) = p. Each key must have as many
          values as there are parents. You can use this form always;
          the first two are just conveniences.

        In all cases the probability of X being false is left implicit,
        since it follows from P(X=true).

        >>> X = BayesNode('X', '', 0.2)
        >>> Y = BayesNode('Y', 'P', {T: 0.2, F: 0.7})
        >>> Z = BayesNode('Z', 'P Q',
        ...    {(T, T): 0.2, (T, F): 0.3, (F, T): 0.5, (F, F): 0.7})
        """
        if isinstance(parents, str):
            parents = parents.split()

        # We store the table always in the third form above.
        if isinstance(cpt, (float, int)):  # no parents, 0-tuple
            cpt = {(): cpt}
        elif isinstance(cpt, dict):
            # one parent, 1-tuple
            if cpt and isinstance(list(cpt.keys())[0], bool):
                cpt = dict(((v, ), p) for v, p in list(cpt.items()))

        assert isinstance(cpt, dict)
        for vs, p in list(cpt.items()):
            assert isinstance(vs, tuple) and len(vs) == len(parents)
            assert every(lambda v: isinstance(v, bool), vs)
            assert 0 <= p <= 1

        self.variable = X
        self.parents = parents
        self.cpt = cpt
        self.children = []
コード例 #8
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ファイル: probability.py プロジェクト: hegc/aima-python
    def __init__(self, X, parents, cpt):
        """X is a variable name, and parents a sequence of variable
        names or a space-separated string.  cpt, the conditional
        probability table, takes one of these forms:

        * A number, the unconditional probability P(X=true). You can
          use this form when there are no parents.

        * A dict {v: p, ...}, the conditional probability distribution
          P(X=true | parent=v) = p. When there's just one parent.

        * A dict {(v1, v2, ...): p, ...}, the distribution P(X=true |
          parent1=v1, parent2=v2, ...) = p. Each key must have as many
          values as there are parents. You can use this form always;
          the first two are just conveniences.

        In all cases the probability of X being false is left implicit,
        since it follows from P(X=true).

        >>> X = BayesNode('X', '', 0.2)
        >>> Y = BayesNode('Y', 'P', {T: 0.2, F: 0.7})
        >>> Z = BayesNode('Z', 'P Q',
        ...    {(T, T): 0.2, (T, F): 0.3, (F, T): 0.5, (F, F): 0.7})
        """
        if isinstance(parents, str):
            parents = parents.split()

        # We store the table always in the third form above.
        if isinstance(cpt, (float, int)):  # no parents, 0-tuple
            cpt = {(): cpt}
        elif isinstance(cpt, dict):
            # one parent, 1-tuple
            if cpt and isinstance(list(cpt.keys())[0], bool):
                cpt = dict(((v,), p) for v, p in list(cpt.items()))

        assert isinstance(cpt, dict)
        for vs, p in list(cpt.items()):
            assert isinstance(vs, tuple) and len(vs) == len(parents)
            assert every(lambda v: isinstance(v, bool), vs)
            assert 0 <= p <= 1

        self.variable = X
        self.parents = parents
        self.cpt = cpt
        self.children = []
コード例 #9
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 def goal_test(self, state):
     "The goal is to assign all vars, with all constraints satisfied."
     assignment = dict(state)
     return (len(assignment) == len(self.vars) and every(
         lambda var: self.nconflicts(var, assignment[var], assignment) == 0,
         self.vars))
コード例 #10
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ファイル: csp.py プロジェクト: hegc/aima-python
 def goal_test(self, state):
     "The goal is to assign all variables, with all constraints satisfied."
     assignment = dict(state)
     return (len(assignment) == len(self.variables) and
             every(lambda variables: self.nconflicts(variables, assignment[variables], assignment) == 0, self.variables))