def test_simple_nested_approximated(self):
# weights = [AdaptType(1), AdaptType("k"), AdaptType("k"), AdaptType(1)]
query = [0, 0, 0, 1, 0, 1]
edges = [(0, 4), (1, 2), (1, 3), (2, 3), (3, 2), (3, 3)]
weights = [AdaptType("INF")]*len(query)
ctl_edges = [(0, 1), (1, 2), (2, 1), (2, 2)]
transitions = [
(0, [DifferenceConstraint("i", None, "k", DifferenceConstraint.DCType.RESET)], 1, [0]),
(1, [DifferenceConstraint("i", None, "1", DifferenceConstraint.DCType.DEC),
DifferenceConstraint("j", "i", "0", DifferenceConstraint.DCType.RESET)], 2, [1, 2]),
(2, [DifferenceConstraint("j", None, "1", DifferenceConstraint.DCType.DEC)], 2, [3]),
(2, [], 1, [])
]
bound_infer = self.ALG(Graph(edges, weights, query), TransitionGraph(ctl_edges, transitions))
bound_infer.attach_weights()
print("The Reachability Bounds Expected for Vertices in the Simple Nested While Graph are: [1, k, k, k!] ")
print("The Reachability Bounds Calculated for Vertices in This Graph are: ", bound_infer.get_weights())
adapt_search = AdaptSearchAlgRefined(bound_infer.graph)
adapt_search.search_adapt()
print("The Adaptivity Expected for Simple Nested While Algorithm is: 2 + k! ")
print("The Adaptivity From This Graph is: ", adapt_search.get_adapt())
def seq_multivar(self):
query = [1, 1, 1, 1]
edges = [(0, 1), (0, 2), (1, 2), (1, 3), (2, 3)]
weights = [AdaptType("INF")] * len(query)
# adapt_search = self.ALG(Graph(edges, weights, query))
ctl_edges = [(2, 3), (1, 2), (0, 1), (3, 4)]
transitions = [
(2, [
DifferenceConstraint("z", None, "Q",
DifferenceConstraint.DCType.RESET)
], 3, [2]),
(1, [
DifferenceConstraint("y", None, "Q",
DifferenceConstraint.DCType.RESET)
], 2, [1]),
(0, [
DifferenceConstraint("x", None, "Q",
DifferenceConstraint.DCType.RESET)
], 1, [0]),
(3, [
DifferenceConstraint("w", None, "Q",
DifferenceConstraint.DCType.RESET)
], -1, [3])
]
AdaptEstimate.adapt_estimate(Graph(edges, weights, query),
TransitionGraph(ctl_edges, transitions))
print(
"The Reachability Bounds Expected for Vertices in Pure Sequence Graph are: [1,1,1,1] "
)
print("The Adaptivity Expected for Simple Seq Algorithm is: 4 ")
def abscfg_parse(self):
with open(self.args.abs_cfg, "r") as graphdata:
# _ = [graphdata.readline() for _ in range(3)]
n = int(graphdata.readline())
edges = [[(n - 1) if int(v) == -1 else int(v)
for v in e.split(",")]
for e in graphdata.readline().split(";")[:-1]]
transitions = []
for l in graphdata.readlines():
l1, dc, l2, v = l.split(";")
if dc == "":
dc_set = []
v_set = [int(v)]
# transitions.append((int(l1), [ ], int(l2), [int(v)]))
else:
v_set = [int(v)]
(var, avar, c, ctype) = dc.split(",")
print((var, avar, c, ctype))
dc_type = DifferenceConstraint.DCType.RESET if ctype == "RESET" else DifferenceConstraint.DCType.INC if ctype == "INC" else DifferenceConstraint.DCType.DEC
avar = None if avar == "" else avar
c = None if c == "" else int(c) if isinstance(c,
int) else c
dc_set = [DifferenceConstraint(var, avar, c, dc_type)]
transitions.append((int(l1), dc_set, int(l2), v_set))
# transitions.append((int(l1), [ DifferenceConstraint(var, avar, c, dc_type) ], int(l2), [int(v)]))
print(n, edges, transitions)
return TransitionGraph(edges, transitions)
pass
def test_nested_while(self):
# weights = [AdaptType(1), AdaptType(1), AdaptType("k"), AdaptType("k"), AdaptType("k"), AdaptType("k * k"), AdaptType("k * k")]
query = [0, 1, 0, 0, 1, 0, 1]
edges = [(0, 2), (1, 4), (1, 6), (2, 2), (2, 3), (3, 5), (5, 5), (6, 4), (6, 6)]
weights = [AdaptType("INF")]*len(query)
ctl_edges = [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (6, 2), (7, 8), (8, 6)]
transitions = [
(0, [DifferenceConstraint("i", None, "k", DifferenceConstraint.DCType.RESET)], 1, [0]),
(1, [DifferenceConstraint("x", None, "Q", DifferenceConstraint.DCType.RESET)], 2, [1]),
(1, [], 1, []),
(1, [DifferenceConstraint("i", None, "1", DifferenceConstraint.DCType.DEC)], 1, [2]),
(1, [DifferenceConstraint("j", None, "k", DifferenceConstraint.DCType.RESET)], 1, [3]),
(1, [DifferenceConstraint("y", "x", "Q", DifferenceConstraint.DCType.RESET)], 1, [4]),
(1, [], 1, []),
(1, [], 1, []),
(1, [DifferenceConstraint("j", None, "1", DifferenceConstraint.DCType.DEC)], 2, [5]),
(1, [DifferenceConstraint("x", None, "Q", DifferenceConstraint.DCType.RESET)], 2, [6])
]
bound_infer = self.ALG(Graph(edges, weights, query), TransitionGraph(ctl_edges, transitions))
bound_infer.attach_weights()
print("The Reachability Bounds Expected for Vertices in the Simple Nested While Graph are: [1, 1, k, k, k, k^2, k^2] ")
print("The Reachability Bounds Calculated for Vertices in This Graph are: ", bound_infer.get_weights())
adapt_search = AdaptSearchAlgRefined(bound_infer.graph)
adapt_search.search_adapt()
print("The Adaptivity Expected for Simple Nested While Algorithm is: 2 + k * k ")
print("The Adaptivity From This Graph is: ", adapt_search.get_adapt())
def nested_while_multivaldep(self):
# weights = [AdaptType(1), AdaptType(1), AdaptType("k"), AdaptType("k"), AdaptType("k"), AdaptType("k * k"), AdaptType("k * k")]
query = [0, 1, 1, 0, 1, 0, 0, 1]
edges = [(0, 3), (1, 4), (1, 7), (2, 4), (4, 4), (4, 7), (7, 4),
(7, 7)]
weights = [AdaptType("INF")] * len(query)
ctl_edges = [(8, 9), (7, 8), (9, 7), (6, 7), (5, 6), (4, 5), (3, 4),
(7, 3), (2, 3), (1, 2), (0, 1), (3, 10)]
transitions = [
(7, [
DifferenceConstraint("j", None, " 1 ",
DifferenceConstraint.DCType.DEC)
], 8, [6]), (6, [], 7, []),
(9, [
DifferenceConstraint("x", None, "Q",
DifferenceConstraint.DCType.RESET)
], 7, [7]),
(6, [
DifferenceConstraint("j", None, "k",
DifferenceConstraint.DCType.RESET)
], 7, [5]),
(5, [
DifferenceConstraint("z", None, "Q",
DifferenceConstraint.DCType.RESET)
], 6, [4]),
(4, [
DifferenceConstraint("i", None, " 1 ",
DifferenceConstraint.DCType.DEC)
], 5, [3]), (2, [], 3, []), (6, [], 2, []),
(2, [
DifferenceConstraint("z", None, "Q",
DifferenceConstraint.DCType.RESET)
], 3, [2]),
(1, [
DifferenceConstraint("x", None, "Q",
DifferenceConstraint.DCType.RESET)
], 2, [1]),
(0, [
DifferenceConstraint("i", None, "k",
DifferenceConstraint.DCType.RESET)
], 1, [0]), (2, [], -1, [])
]
AdaptEstimate.adapt_estimate(Graph(edges, weights, query),
TransitionGraph(ctl_edges, transitions))
# bound_infer = self.ALG(Graph(edges, weights, query), TransitionGraph(ctl_edges, transitions))
# bound_infer.attach_weights()
print(
"The Reachability Bounds Expected for Vertices in the Simple Nested While Graph are: [1, 1, k, k, k, k^2, k^2] "
)
print(
"The Adaptivity Expected for Simple Nested While Algorithm is: 1 + k + k * k "
)
def multiple_round(self):
query = [0, 0, 1, 0, 0]
edges = [(0, 2), (0, 3), (0, 4), (1, 2), (1, 3), (2, 3), (3, 2),
(3, 3), (4, 4)]
weights = [AdaptType("INF")] * len(query)
ctl_edges = [(4, 5), (3, 4), (2, 3), (5, 2), (1, 2), (0, 1), (2, 7)]
transitions = [
(4, [
DifferenceConstraint("l", None, "INF",
DifferenceConstraint.DCType.RESET)
], 5, [3]),
(3, [
DifferenceConstraint("a", None, "Q",
DifferenceConstraint.DCType.RESET)
], 4, [2]), (2, [], 3, []),
(5, [
DifferenceConstraint("i", None, " 1 ",
DifferenceConstraint.DCType.DEC)
], 2, [4]),
(1, [
DifferenceConstraint("l", None, " 0 ",
DifferenceConstraint.DCType.RESET)
], 2, [1]),
(0, [
DifferenceConstraint("i", None, " k ",
DifferenceConstraint.DCType.RESET)
], 1, [0]), (2, [], -1, [])
]
AdaptEstimate.adapt_estimate(Graph(edges, weights, query),
TransitionGraph(ctl_edges, transitions))
# bound_infer = self.ALG(Graph(edges, weights, query), TransitionGraph(ctl_edges, transitions))
# bound_infer.attach_weights()
print(
"The Reachability Bounds Expected for Vertices in the Multiple Round Graph are: [1, 1, k, k, k] "
)
# print("The Reachability Bounds Calculated for Vertices in This Graph are: ", bound_infer.get_weights())
# adapt_search = AdaptSearchAlgRefined(bound_infer.graph)
# adapt_search.search_adapt()
print("The Adaptivity Expected for multiple Round Algorithm is: k ")
def test_simple_while(self):
weights = [AdaptType(0), AdaptType(0), AdaptType(0), AdaptType(0)]
query = [0, 1, 1, 0]
edges = [(0, 2), (1, 2), (1, 3)]
# adapt_search = self.ALG(Graph(edges, weights, query))
ctl_edges = [(0, 1), (1, 1)]
transitions = [(0, [DifferenceConstraint("x", None, "k", DifferenceConstraint.DCType.RESET)], 1, [0]),
(1, [DifferenceConstraint("x", None, "1", DifferenceConstraint.DCType.DEC)], 1, [1, 2, 3])]
bound_infer = self.ALG(Graph(edges, weights, query), TransitionGraph(ctl_edges, transitions))
bound_infer.attach_weights()
print("The Reachability Bounds Expected for Vertices in Simple while are: [1, k, k,k] ")
print("The Reachability Bounds Calculated for Vertices in This Graph are: ", bound_infer.get_weights())
adapt_search = AdaptSearchAlgRefined(bound_infer.graph)
adapt_search.search_adapt()
print("The Adaptivity Expected for Simple While Algorithm is: 1 ")
print("The Adaptivity From This Graph is: ", adapt_search.get_adapt())
def test_multiple_constriants(self):
query = [0, 0, 0, 1, 0, 1]
edges = [(0, 2), (1, 2), (1,4), (2,2), (2, 3), (3, 4), (4, 4), (4, 5)]
weights = [AdaptType("INF")]*len(query)
ctl_edges = [(0, 1), (1, 1), (1, 2)]
transitions = [(0,
[DifferenceConstraint("i", None, "k", DifferenceConstraint.DCType.RESET),
DifferenceConstraint("l", None, "Q", DifferenceConstraint.DCType.RESET)
], 1, [0, 1]),
(1, [DifferenceConstraint("i", None, "1", DifferenceConstraint.DCType.DEC),
DifferenceConstraint("a", None, "Q", DifferenceConstraint.DCType.RESET),
DifferenceConstraint("l", "a", "0", DifferenceConstraint.DCType.INC)], 1, [2, 3, 4]),
(1, [DifferenceConstraint("b", "l", "Q", DifferenceConstraint.DCType.RESET)], 2, [5])]
bound_infer = self.ALG(Graph(edges, weights, query), TransitionGraph(ctl_edges, transitions))
bound_infer.attach_weights()
print("The Reachability Bounds Expected for Vertices in the Two Round Graph are: [1, 1, k, k, k, 1] ")
print("The Reachability Bounds Calculated for Vertices in This Graph are: ", bound_infer.get_weights())
adapt_search = AdaptSearchAlgRefined(bound_infer.graph)
adapt_search.search_adapt()
print("The Adaptivity Expected for Two Round Algorithm is: 2 ")
print("The Adaptivity From This Graph is: ", adapt_search.get_adapt())
def if_valdep(self):
query = [1, 0, 1, 1]
edges = [(0, 2), (1, 2), (1, 3)]
weights = [AdaptType("INF")] * len(query)
ctl_edges = [(0, 1), (1, 2), (2, 3), (2, 4), (3, 5), (4, 5)]
transitions = [
(0, [
DifferenceConstraint("x", None, "Q",
DifferenceConstraint.DCType.RESET)
], 1, [0]),
(0, [
DifferenceConstraint("z", None, "Q",
DifferenceConstraint.DCType.RESET)
], 1, [1]),
(0, [], 1, []),
(0, [], 1, []),
(0, [
DifferenceConstraint("y", None, "Q",
DifferenceConstraint.DCType.RESET)
], 1, [2]),
(0, [
DifferenceConstraint("y", None, "Q",
DifferenceConstraint.DCType.RESET)
], 1, [3]),
]
AdaptEstimate.adapt_estimate(Graph(edges, weights, query),
TransitionGraph(ctl_edges, transitions))
print(
"The Reachability Bounds Expected for Mutli-path Dependency in If branch Algorithm are: [1,1,1,1] "
)
# adapt_search = AdaptSearchAlgRefined(bound_infer.graph)
# adapt_search.search_adapt()
print(
"The Adaptivity Expected for Mutli-path Dependency in If branch Algorithm is: 2 "
)
def if_ctldep(self):
query = [1, 1, 1, 1]
edges = [(0, 1), (1, 2), (1, 3)]
weights = [AdaptType("INF")] * len(query)
ctl_edges = [(0, 1), (1, 2), (2, 3), (2, 4), (3, 5), (4, 5)]
transitions = [
(0, [
DifferenceConstraint("x", None, "Q",
DifferenceConstraint.DCType.RESET)
], 1, [0]),
(0, [
DifferenceConstraint("z", None, "Q",
DifferenceConstraint.DCType.RESET)
], 1, [1]),
(0, [], 1, []),
(0, [], 1, []),
(0, [
DifferenceConstraint("y", None, "Q",
DifferenceConstraint.DCType.RESET)
], 1, [2]),
(0, [
DifferenceConstraint("w", None, "Q",
DifferenceConstraint.DCType.RESET)
], 1, [3]),
]
AdaptEstimate.adapt_estimate(Graph(edges, weights, query),
TransitionGraph(ctl_edges, transitions))
# bound_infer = self.ALG(Graph(edges, weights, query), TransitionGraph(ctl_edges, transitions))
# bound_infer.attach_weights()
print(
"The Reachability Bounds Expected for Vertices in If Branch with Control Dependency Graph are: [1, 1, 1, 1] "
)
print(
"The Adaptivity Expected for Mutli-path Dependency in If branch Algorithm is: 3 "
)
def while_valctldep(self):
query = [1, 1, 1, 1]
edges = [(0, 2), (0, 3), (1, 2), (2, 3), (3, 2)]
weights = [AdaptType("INF")] * len(query)
ctl_edges = [(3, 4), (2, 3), (4, 2), (1, 2), (0, 1), (2, 5)]
transitions = [
(3, [
DifferenceConstraint("x", None, "Q",
DifferenceConstraint.DCType.RESET)
], 4, [2]), (2, [], 3, []),
(4, [
DifferenceConstraint("z", None, "Q",
DifferenceConstraint.DCType.RESET)
], 2, [3]),
(1, [
DifferenceConstraint("z", None, "Q",
DifferenceConstraint.DCType.RESET)
], 2, [1]),
(0, [
DifferenceConstraint("x", None, "Q",
DifferenceConstraint.DCType.RESET)
], 1, [0]), (2, [], -1, [])
]
AdaptEstimate.adapt_estimate(Graph(edges, weights, query),
TransitionGraph(ctl_edges, transitions))
print(
"The Reachability Bounds Expected for Vertices in While with Control and Value Dependency Overlapped are: [1, 1, INF, INF] "
)
# print("The Reachability Bounds Calculated for Vertices in This Graph are: ", bound_infer.get_weights())
# adapt_search = AdaptSearchAlgRefined(bound_infer.graph)
# adapt_search.search_adapt()
print(
"The Adaptivity Expected for While with Control and Value Dependency Overlapped Algorithm is: INF "
)
def while_multivaldep(self):
query = [0, 1, 1, 0, 1, 1, 1]
edges = [(0, 3), (0, 4), (1, 4), (2, 4), (4, 6), (6, 4), (4, 5),
(5, 4)]
weights = [AdaptType("INF")] * len(query)
ctl_edges = [(6, 7), (5, 6), (4, 5), (3, 4), (7, 3), (2, 3), (1, 2),
(0, 1), (3, 8)]
transitions = [
(6, [
DifferenceConstraint("x", None, "Q",
DifferenceConstraint.DCType.RESET)
], 7, [5]),
(5, [
DifferenceConstraint("y", None, "Q",
DifferenceConstraint.DCType.RESET)
], 6, [4]),
(4, [
DifferenceConstraint("i", None, "1",
DifferenceConstraint.DCType.DEC)
], 5, [3]), (3, [], 4, []),
(7, [
DifferenceConstraint("z", None, "Q",
DifferenceConstraint.DCType.RESET)
], 3, [6]),
(2, [
DifferenceConstraint("z", None, "Q",
DifferenceConstraint.DCType.RESET)
], 3, [2]),
(1, [
DifferenceConstraint("x", None, "Q",
DifferenceConstraint.DCType.RESET)
], 2, [1]),
(0, [
DifferenceConstraint("i", None, "k",
DifferenceConstraint.DCType.RESET)
], 1, [0]), (3, [], -1, [])
]
AdaptEstimate.adapt_estimate(Graph(edges, weights, query),
TransitionGraph(ctl_edges, transitions))
print(
"The Reachability Bounds Expected for Vertices in while Multi Variable Dependency: [1, 1, 1, k, k, k, k] "
)
print(
"The Adaptivity Expected for while with (Multi Variable Dependency) Algorithm is: 1 + 2 * k"
)
def while_multipath_ctldep(self):
query = [1, 0, 1, 1, 0, 1]
edges = [(0, 2), (0, 3), (0, 4), (1, 2), (1, 3), (2, 2), (2, 5),
(3, 3), (3, 5), (4, 4)]
weights = [AdaptType("INF")] * len(query)
ctl_edges = [(3, 4), (3, 5), (4, 6), (5, 6), (2, 3), (6, 2), (2, 7),
(1, 2), (0, 1), (7, 8)]
transitions = [
(3, [], 4, []), (3, [], 5, []),
(4, [
DifferenceConstraint("y", None, "Q",
DifferenceConstraint.DCType.RESET)
], 6, [2]),
(5, [
DifferenceConstraint("y", None, "Q",
DifferenceConstraint.DCType.RESET)
], 6, [3]), (2, [], 3, []),
(6, [
DifferenceConstraint("x", None, " 1 ",
DifferenceConstraint.DCType.DEC)
], 2, [4]), (2, [], 7, []),
(1, [
DifferenceConstraint("y", None, " 0 ",
DifferenceConstraint.DCType.RESET)
], 2, [1]),
(0, [
DifferenceConstraint("x", None, "Q",
DifferenceConstraint.DCType.RESET)
], 1, [0]),
(7, [
DifferenceConstraint("y", None, "Q",
DifferenceConstraint.DCType.RESET)
], -1, [5])
]
AdaptEstimate.adapt_estimate(Graph(edges, weights, query),
TransitionGraph(ctl_edges, transitions))
print(
"The Reachability Bounds Expected for Vertices in while with If Banch inside are: [1, 1, Q, Q, Q, 1] "
)
print(
"The Adaptivity Expected for while with If Banch (Multi-Path While Loop) Algorithm is: 2 + Q"
)
def test_while_multipath_if(self):
query = [0, 1, 0, 1, 1, 1]
edges = [(0, 4), (1, 2), (1, 3), (2, 3), (3, 2), (3, 3)]
weights = [AdaptType("INF")]*len(query)
ctl_vertices_num = 4
ctl_edges = [(0, 1), (1, 2), (2, 3), (2, 3), (3, 1)]
transitions = [
(0, [DifferenceConstraint("i", None, "k", DifferenceConstraint.DCType.RESET),
DifferenceConstraint("x", None, "Q", DifferenceConstraint.DCType.RESET)], 1, [0, 1]),
(1, [DifferenceConstraint("i", None, "1", DifferenceConstraint.DCType.DEC)], 2, [2]),
(2, [DifferenceConstraint("y", None, "Q", DifferenceConstraint.DCType.RESET)], 3, [3]),
(2, [DifferenceConstraint("y", None, "Q", DifferenceConstraint.DCType.RESET)], 3, [4]),
(3, [DifferenceConstraint("x", None, "Q", DifferenceConstraint.DCType.RESET)], 2, [5])
]
is_scc = [False, True, True, True, True]
bound_infer = self.ALG(Graph(edges, weights, query), TransitionGraph(ctl_edges, transitions))
bound_infer.attach_weights()
print("The Reachability Bounds Expected for Vertices in the Multiple Path Simple While Graph are: [1, 1, k, k/2, k/2, k] ")
print("The Reachability Bounds Calculated for Vertices in This Graph are: ", bound_infer.get_weights())