SparseMA

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

class SparseMA[ValueType](components)

MA in sparse representation

Template arguments:

• ValueType (type)

Available specializations:

• SparseMA[float]

• SparseMA[stormpy.numbers.Rational]

• SparseMA[stormpy.numbers.Interval]

• SparseMA[stormpy.numbers.RationalFunction]

• SparseMA[stormpy.numbers.RationalInterval]

Construction

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

Members

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

SparseMA.apply_scheduler(

self: stormpy.models.SparseMA[ValueType],

scheduler,

drop_unreachable_states: bool = True,

) → stormpy.models.SparseModel[ValueType]

apply scheduler

property SparseMA.backward_transition_matrix

Backward transition matrix

property SparseMA.choice_labeling

get choice labelling

property SparseMA.choice_origins

SparseMA.close(self: stormpy.models.SparseMA[ValueType]) → None

Close the MA by applying maximal progress assumption

SparseMA.collect_all_parameters(

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

) → set[stormpy.numbers.Variable]

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

Collect all parameters

SparseMA.collect_probability_parameters(

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

) → set[stormpy.numbers.Variable]

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

Collect probability parameters

SparseMA.collect_rate_parameters(

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

) → set[stormpy.numbers.Variable]

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

Collect rate parameters

SparseMA.collect_reward_parameters(

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

) → set[stormpy.numbers.Variable]

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

Collect reward parameters

SparseMA.convert_to_ctmc(

self: stormpy.models.SparseMA[ValueType],

) → stormpy.models.SparseCtmc[ValueType]

Convert the MA into a CTMC.

property SparseMA.convertible_to_ctmc

Check whether the MA can be converted into a CTMC.

property SparseMA.exit_rates

SparseMA.get_labels_of_state(

self: stormpy.models.SparseModel[ValueType],

state: SupportsInt | SupportsIndex,

) → set[str]

Get labels of state

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

Reward model

SparseMA.get_states_with_parameter(

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

parameter: stormpy.numbers.Variable,

) → stormpy.datastructures.BitVector

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

Find states with a particular parameter

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

Does the model have an associated choice labelling?

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

has choice origins?

property SparseMA.has_parameters

Flag whether model has parameters

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

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

has state valuation?

property SparseMA.has_zeno_cycle

Check whether the MA has a Zeno cycle.

property SparseMA.initial_states

Initial states

property SparseMA.initial_states_as_bitvector

property SparseMA.is_closed

Check whether the MA is closed.

property SparseMA.is_discrete_time_model

Flag whether the model is a discrete time model

property SparseMA.is_exact

Flag whether model is exact

property SparseMA.is_nondeterministic_model

Flag whether the model contains nondeterminism

property SparseMA.is_partially_observable

Flag whether the model has observation labels

SparseMA.is_sink_state(

self: stormpy.models.SparseModel[ValueType],

state: SupportsInt | SupportsIndex,

) → bool

property SparseMA.is_sparse_model

Flag whether the model is stored as a sparse model

property SparseMA.is_symbolic_model

Flag whether the model is stored using decision diagrams

property SparseMA.labeling

Labels

property SparseMA.markovian_states

property SparseMA.model_type

Model type

property SparseMA.nondeterministic_choice_indices

property SparseMA.nr_choices

Number of choices

property SparseMA.nr_states

Number of states

property SparseMA.nr_transitions

Number of transitions

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

property SparseMA.reward_models

Reward models

SparseMA.set_initial_states(

self: stormpy.models.SparseModel[ValueType],

states: stormpy.datastructures.BitVector,

) → None

Set initial states

property SparseMA.state_valuations

state valuations

property SparseMA.states

Get states

property SparseMA.supports_parameters

Flag whether model supports parameters

property SparseMA.supports_uncertainty

Flag whether model supports uncertainty via intervals

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

Write dot to a string

property SparseMA.transition_matrix

Transition matrix