SparseCtmc

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

class SparseCtmc[ValueType](components)

CTMC in sparse representation

Template arguments:

• ValueType (type)

Available specializations:

• SparseCtmc[float]

• SparseCtmc[stormpy.numbers.Rational]

• SparseCtmc[stormpy.numbers.Interval]

• SparseCtmc[stormpy.numbers.RationalFunction]

• SparseCtmc[stormpy.numbers.RationalInterval]

Construction

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

Members

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

property SparseCtmc.backward_transition_matrix

Backward transition matrix

property SparseCtmc.choice_labeling

get choice labelling

property SparseCtmc.choice_origins

SparseCtmc.collect_all_parameters(

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

) → set[stormpy.numbers.Variable]

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

Collect all parameters

SparseCtmc.collect_probability_parameters(

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

) → set[stormpy.numbers.Variable]

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

Collect probability parameters

SparseCtmc.collect_rate_parameters(

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

) → set[stormpy.numbers.Variable]

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

Collect rate parameters

SparseCtmc.collect_reward_parameters(

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

) → set[stormpy.numbers.Variable]

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

Collect reward parameters

SparseCtmc.compute_probability_matrix(self: stormpy.models.SparseCtmc[ValueType])

property SparseCtmc.exit_rates

SparseCtmc.get_labels_of_state(

self: stormpy.models.SparseModel[ValueType],

state: SupportsInt | SupportsIndex,

) → set[str]

Get labels of state

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

Reward model

SparseCtmc.get_states_with_parameter(

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

parameter: stormpy.numbers.Variable,

) → stormpy.datastructures.BitVector

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

Find states with a particular parameter

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

Does the model have an associated choice labelling?

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

has choice origins?

property SparseCtmc.has_parameters

Flag whether model has parameters

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

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

has state valuation?

property SparseCtmc.initial_states

Initial states

property SparseCtmc.initial_states_as_bitvector

property SparseCtmc.is_discrete_time_model

Flag whether the model is a discrete time model

property SparseCtmc.is_exact

Flag whether model is exact

property SparseCtmc.is_nondeterministic_model

Flag whether the model contains nondeterminism

property SparseCtmc.is_partially_observable

Flag whether the model has observation labels

SparseCtmc.is_sink_state(

self: stormpy.models.SparseModel[ValueType],

state: SupportsInt | SupportsIndex,

) → bool

property SparseCtmc.is_sparse_model

Flag whether the model is stored as a sparse model

property SparseCtmc.is_symbolic_model

Flag whether the model is stored using decision diagrams

property SparseCtmc.labeling

Labels

property SparseCtmc.model_type

Model type

property SparseCtmc.nr_choices

Number of choices

property SparseCtmc.nr_states

Number of states

property SparseCtmc.nr_transitions

Number of transitions

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

property SparseCtmc.reward_models

Reward models

SparseCtmc.set_initial_states(

self: stormpy.models.SparseModel[ValueType],

states: stormpy.datastructures.BitVector,

) → None

Set initial states

property SparseCtmc.state_valuations

state valuations

property SparseCtmc.states

Get states

property SparseCtmc.supports_parameters

Flag whether model supports parameters

property SparseCtmc.supports_uncertainty

Flag whether model supports uncertainty via intervals

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

Write dot to a string

property SparseCtmc.transition_matrix

Transition matrix