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
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)
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)
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
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
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
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 = []
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 = []
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))
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))