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