SparseDtmc

class SparseDtmc[ValueType](other_model: stormpy.models.SparseDtmc[ValueType])

class SparseDtmc[ValueType](components)

DTMC in sparse representation

Template arguments:

• ValueType (type)

Available specializations:

• SparseDtmc[float]

• SparseDtmc[stormpy.numbers.Rational]

• SparseDtmc[stormpy.numbers.Interval]

• SparseDtmc[stormpy.numbers.RationalFunction]

• SparseDtmc[stormpy.numbers.RationalInterval]

Construction

The specialization can be inferred from the first positional constructor argument (alternatively, the components or other_model keyword argument).

Members

SparseDtmc.add_reward_model(self: stormpy.models.SparseModel[ValueType], arg0: str, arg1) → None

property SparseDtmc.backward_transition_matrix

Backward transition matrix

property SparseDtmc.choice_labeling

get choice labelling

property SparseDtmc.choice_origins

SparseDtmc.collect_all_parameters(

self: stormpy.models.SparseModel[stormpy.numbers.RationalFunction],

) → set[stormpy.numbers.Variable]

Available only for: SparseDtmc[stormpy.numbers.RationalFunction].

Collect all parameters

SparseDtmc.collect_probability_parameters(

self: stormpy.models.SparseModel[stormpy.numbers.RationalFunction],

) → set[stormpy.numbers.Variable]

Available only for: SparseDtmc[stormpy.numbers.RationalFunction].

Collect probability parameters

SparseDtmc.collect_rate_parameters(

self: stormpy.models.SparseModel[stormpy.numbers.RationalFunction],

) → set[stormpy.numbers.Variable]

Available only for: SparseDtmc[stormpy.numbers.RationalFunction].

Collect rate parameters

SparseDtmc.collect_reward_parameters(

self: stormpy.models.SparseModel[stormpy.numbers.RationalFunction],

) → set[stormpy.numbers.Variable]

Available only for: SparseDtmc[stormpy.numbers.RationalFunction].

Collect reward parameters

SparseDtmc.get_labels_of_state(

self: stormpy.models.SparseModel[ValueType],

state: SupportsInt | SupportsIndex,

) → set[str]

Get labels of state

SparseDtmc.get_reward_model(self: stormpy.models.SparseModel[ValueType], name: str)

Reward model

SparseDtmc.get_states_with_parameter(

self: stormpy.models.SparseModel[stormpy.numbers.RationalFunction],

parameter: stormpy.numbers.Variable,

) → stormpy.datastructures.BitVector

Available only for: SparseDtmc[stormpy.numbers.RationalFunction].

Find states with a particular parameter

SparseDtmc.has_choice_labeling(self: stormpy.models.SparseModel[ValueType]) → bool

Does the model have an associated choice labelling?

SparseDtmc.has_choice_origins(self: stormpy.models.SparseModel[ValueType]) → bool

has choice origins?

property SparseDtmc.has_parameters

Flag whether model has parameters

SparseDtmc.has_reward_model(self: stormpy.models.SparseModel[ValueType], name: str) → bool

SparseDtmc.has_state_valuations(self: stormpy.models.SparseModel[ValueType]) → bool

has state valuation?

property SparseDtmc.initial_states

Initial states

property SparseDtmc.initial_states_as_bitvector

property SparseDtmc.is_discrete_time_model

Flag whether the model is a discrete time model

property SparseDtmc.is_exact

Flag whether model is exact

property SparseDtmc.is_nondeterministic_model

Flag whether the model contains nondeterminism

property SparseDtmc.is_partially_observable

Flag whether the model has observation labels

SparseDtmc.is_sink_state(

self: stormpy.models.SparseModel[ValueType],

state: SupportsInt | SupportsIndex,

) → bool

property SparseDtmc.is_sparse_model

Flag whether the model is stored as a sparse model

property SparseDtmc.is_symbolic_model

Flag whether the model is stored using decision diagrams

property SparseDtmc.labeling

Labels

property SparseDtmc.model_type

Model type

property SparseDtmc.nr_choices

Number of choices

property SparseDtmc.nr_states

Number of states

property SparseDtmc.nr_transitions

Number of transitions

SparseDtmc.reduce_to_state_based_rewards(self: stormpy.models.SparseModel[ValueType]) → None

property SparseDtmc.reward_models

Reward models

SparseDtmc.set_initial_states(

self: stormpy.models.SparseModel[ValueType],

states: stormpy.datastructures.BitVector,

) → None

Set initial states

property SparseDtmc.state_valuations

state valuations

property SparseDtmc.states

Get states

property SparseDtmc.supports_parameters

Flag whether model supports parameters

property SparseDtmc.supports_uncertainty

Flag whether model supports uncertainty via intervals

SparseDtmc.to_dot(self: stormpy.models.SparseModel[ValueType]) → str

Write dot to a string

property SparseDtmc.transition_matrix

Transition matrix