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import stormpy
from helpers.helper import get_example_path
import math
class TestMatrix:
def test_sparse_matrix(self):
model = stormpy.parse_explicit_model(get_example_path("dtmc", "die.tra"), get_example_path("dtmc", "die.lab"))
matrix = model.transition_matrix
assert type(matrix) is stormpy.storage.SparseMatrix
assert matrix.nr_rows == model.nr_states
assert matrix.nr_columns == model.nr_states
assert matrix.nr_entries == 27 #model.nr_transitions
for e in matrix:
assert e.value() == 0.5 or e.value() == 0 or (e.value() == 1 and e.column > 6)
def test_backward_matrix(self):
model = stormpy.parse_explicit_model(get_example_path("dtmc", "die.tra"), get_example_path("dtmc", "die.lab"))
matrix = model.backward_transition_matrix
assert type(matrix) is stormpy.storage.SparseMatrix
assert matrix.nr_rows == model.nr_states
assert matrix.nr_columns == model.nr_states
assert matrix.nr_entries == 20 #model.nr_transitions
for e in matrix:
assert e.value() == 0.5 or e.value() == 0 or (e.value() == 1 and e.column > 6)
def test_change_sparse_matrix(self):
model = stormpy.parse_explicit_model(get_example_path("dtmc", "die.tra"), get_example_path("dtmc", "die.lab"))
matrix = model.transition_matrix
for e in matrix:
assert e.value() == 0.5 or e.value() == 0 or e.value() == 1
i = 0
for e in matrix:
e.set_value(i)
i += 0.1
i = 0
for e in matrix:
assert e.value() == i
i += 0.1
def test_change_sparse_matrix_modelchecking(self):
import stormpy.logic
model = stormpy.parse_explicit_model(get_example_path("dtmc", "die.tra"), get_example_path("dtmc", "die.lab"))
matrix = model.transition_matrix
# Check matrix
for e in matrix:
assert e.value() == 0.5 or e.value() == 0 or e.value() == 1
# First model checking
formulas = stormpy.parse_properties("P=? [ F \"one\" ]")
result = stormpy.model_checking(model, formulas[0])
resValue = result.at(model.initial_states[0])
assert math.isclose(resValue, 0.16666666666666663)
# Change probabilities
i = 0
for e in matrix:
if e.value() == 0.5:
if i % 2 == 0:
e.set_value(0.3)
else:
e.set_value(0.7)
i += 1
for e in matrix:
assert e.value() == 0.3 or e.value() == 0.7 or e.value() == 1 or e.value() == 0
# Second model checking
result = stormpy.model_checking(model, formulas[0])
resValue = result.at(model.initial_states[0])
assert math.isclose(resValue, 0.06923076923076932)
# Change probabilities again
for state in model.states:
for action in state.actions:
for transition in action.transitions:
if transition.value() == 0.3:
transition.set_value(0.8)
elif transition.value() == 0.7:
transition.set_value(0.2)
# Third model checking
result = stormpy.model_checking(model, formulas[0])
resValue = result.at(model.initial_states[0])
assert math.isclose(resValue, 0.3555555555555556)
def test_change_parametric_sparse_matrix_modelchecking(self):
import stormpy.logic
program = stormpy.parse_prism_program(get_example_path("pdtmc", "brp16_2.pm"))
formulas = stormpy.parse_properties_for_prism_program("P=? [ F s=5 ]", program)
model = stormpy.build_parametric_model(program, formulas)
initial_state = model.initial_states[0]
assert initial_state == 0
matrix = model.transition_matrix
# Check matrix
one_pol = stormpy.RationalRF(1)
one_pol = stormpy.FactorizedPolynomial(one_pol)
one = stormpy.FactorizedRationalFunction(one_pol, one_pol)
for e in matrix:
assert e.value() == one or len(e.value().gather_variables()) > 0
# First model checking
result = stormpy.model_checking(model, formulas[0])
ratFunc = result.at(initial_state)
assert len(ratFunc.gather_variables()) > 0
# Change probabilities
two_pol = stormpy.RationalRF(2)
two_pol = stormpy.FactorizedPolynomial(two_pol)
new_val = stormpy.FactorizedRationalFunction(one_pol, two_pol)
for e in matrix:
if len(e.value().gather_variables()) > 0:
e.set_value(new_val)
for e in matrix:
assert e.value() == new_val or e.value() == one
# Second model checking
result = stormpy.model_checking(model, formulas[0])
ratFunc = result.at(initial_state)
assert len(ratFunc.gather_variables()) == 0