diff --git a/csp.py b/csp.py
index 1e97d7780..8c5ecde3d 100644
--- a/csp.py
+++ b/csp.py
@@ -14,12 +14,13 @@
class CSP(search.Problem):
"""This class describes finite-domain Constraint Satisfaction Problems.
A CSP is specified by the following inputs:
- variables A list of variables; each is atomic (e.g. int or string).
+ variables A list of variables; each is atomic (e.g. int or string).
domains A dict of {var:[possible_value, ...]} entries.
neighbors A dict of {var:[var,...]} that for each variable lists
the other variables that participate in constraints.
constraints A function f(A, a, B, b) that returns true if neighbors
A, B satisfy the constraint when they have values A=a, B=b
+
In the textbook and in most mathematical definitions, the
constraints are specified as explicit pairs of allowable values,
but the formulation here is easier to express and more compact for
@@ -29,7 +30,7 @@ class CSP(search.Problem):
problem, that's all there is.
However, the class also supports data structures and methods that help you
- solve CSPs by calling a search function on the CSP. Methods and slots are
+ solve CSPs by calling a search function on the CSP. Methods and slots are
as follows, where the argument 'a' represents an assignment, which is a
dict of {var:val} entries:
assign(var, val, a) Assign a[var] = val; do other bookkeeping
@@ -307,8 +308,9 @@ def tree_csp_solver(csp):
"""[Figure 6.11]"""
assignment = {}
root = csp.variables[0]
- X, parent = topological_sort(csp.variables, root)
- for Xj in reversed(X):
+ root = 'NT'
+ X, parent = topological_sort(csp, root)
+ for Xj in reversed(X[1:]):
if not make_arc_consistent(parent[Xj], Xj, csp):
return None
for Xi in X:
@@ -318,8 +320,43 @@ def tree_csp_solver(csp):
return assignment
-def topological_sort(xs, x):
- raise NotImplementedError
+def topological_sort(X, root):
+ """Returns the topological sort of X starting from the root.
+
+ Input:
+ X is a list with the nodes of the graph
+ N is the dictionary with the neighbors of each node
+ root denotes the root of the graph.
+
+ Output:
+ stack is a list with the nodes topologically sorted
+ parents is a dictionary pointing to each node's parent
+
+ Other:
+ visited shows the state (visited - not visited) of nodes
+
+ """
+ nodes = X.variables
+ neighbors = X.neighbors
+
+ visited = defaultdict(lambda: False)
+
+ stack = []
+ parents = {}
+
+ build_topological(root, None, neighbors, visited, stack, parents)
+ return stack, parents
+
+def build_topological(node, parent, neighbors, visited, stack, parents):
+ """Builds the topological sort and the parents of each node in the graph"""
+ visited[node] = True
+
+ for n in neighbors[node]:
+ if(not visited[n]):
+ build_topological(n, node, neighbors, visited, stack, parents)
+
+ parents[node] = parent
+ stack.insert(0,node)
def make_arc_consistent(Xj, Xk, csp):
diff --git a/tests/test_csp.py b/tests/test_csp.py
index 5bed85c05..803dede74 100644
--- a/tests/test_csp.py
+++ b/tests/test_csp.py
@@ -274,6 +274,18 @@ def test_universal_dict():
def test_parse_neighbours():
assert parse_neighbors('X: Y Z; Y: Z') == {'Y': ['X', 'Z'], 'X': ['Y', 'Z'], 'Z': ['X', 'Y']}
+def test_topological_sort():
+ root = 'NT'
+ Sort, Parents = topological_sort(australia,root)
+
+ assert Sort == ['NT','SA','Q','NSW','V','WA']
+ assert Parents['NT'] == None
+ assert Parents['SA'] == 'NT'
+ assert Parents['Q'] == 'SA'
+ assert Parents['NSW'] == 'Q'
+ assert Parents['V'] == 'NSW'
+ assert Parents['WA'] == 'SA'
+
if __name__ == "__main__":
pytest.main()
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