def test_beam2_graph_search():
"""
like test_beam2_tree_search, but eliminates duplicate nodes
"""
for goal in range(1, 10):
p = AnnotatedProblem(EasyProblem(0, goal))
sol = next(beam_search(p, beam_width=2, graph=True))
assert sol.state_node.state == goal
assert p.nodes_expanded == goal * 2
def test_beam1_tree_search():
"""
Beam search with a width of 1 is like a depth first search, but with no backtracking.
"""
for goal in range(1,10):
p = AnnotatedProblem(EasyProblem(0, extra=goal))
sol = next(beam_search(p, beam_width=1, graph_search=False))
assert sol.state == goal
assert p.nodes_expanded == goal*2
assert p.goal_tests == goal+1
def test_beam2_graph_search():
"""
like test_beam2_tree_search, but eliminates duplicate nodes
"""
for goal in range(1,10):
p = AnnotatedProblem(EasyProblem(0, extra=goal))
sol = next(beam_search(p, beam_width=2, graph_search=True))
assert sol.state == goal
assert p.nodes_expanded == goal*2
assert p.goal_tests == goal+1
def test_beam2_tree_search():
"""
Beam search with a width of 2 is slightly different. It is like a fusion of
breadth and depth first searches.
"""
for goal in range(1,10):
p = AnnotatedProblem(EasyProblem(0, extra=goal))
sol = next(beam_search(p, beam_width=2, graph_search=False))
assert sol.state == goal
assert p.nodes_expanded == 2 + (goal-1)*4
assert p.goal_tests == 2 + (goal-1)*2
def test_beam1_tree_search():
"""
Beam search with a width of 1 is like a depth first search, but with no
backtracking.
"""
for goal in range(1, 10):
p = AnnotatedProblem(EasyProblem(0, goal))
sol = next(beam_search(p, beam_width=1, graph=False))
assert sol.state_node.state == goal
assert p.nodes_expanded == goal * 2
assert p.goal_tests == goal + 1
def test_beam2_tree_search():
"""
Beam search with a width of 2 is slightly different. It is like a fusion of
breadth and depth first searches.
"""
for goal in range(1, 10):
p = AnnotatedProblem(EasyProblem(0, goal))
sol = next(beam_search(p, beam_width=2, graph=False))
assert sol.state_node.state == goal
assert p.nodes_expanded == 2 + (goal - 1) * 4
assert p.goal_tests == 2 + (goal - 1) * 2
def beam_width2(problem):
return beam_search(problem, beam_width=2)
def beam_width2(problem):
return beam_search(problem, beam_width=2)
