SparseModel

class SparseModel[ValueType]

A probabilistic model in a sparse matrix representation

Template arguments:

• ValueType (type)

Available specializations:

• SparseModel[float]

• SparseModel[stormpy.numbers.Rational]

• SparseModel[stormpy.numbers.Interval]

• SparseModel[stormpy.numbers.RationalFunction]

• SparseModel[stormpy.numbers.RationalInterval]

Construction

The default specialization is SparseModel[float].

Members

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

property SparseModel.backward_transition_matrix

Backward transition matrix

property SparseModel.choice_labeling

get choice labelling

property SparseModel.choice_origins

SparseModel.collect_all_parameters(

self: stormpy.models.SparseModel[ValueType],

) → set[stormpy.numbers.Variable]

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

Collect all parameters

SparseModel.collect_probability_parameters(

self: stormpy.models.SparseModel[ValueType],

) → set[stormpy.numbers.Variable]

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

Collect probability parameters

SparseModel.collect_rate_parameters(

self: stormpy.models.SparseModel[ValueType],

) → set[stormpy.numbers.Variable]

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

Collect rate parameters

SparseModel.collect_reward_parameters(

self: stormpy.models.SparseModel[ValueType],

) → set[stormpy.numbers.Variable]

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

Collect reward parameters

SparseModel.get_labels_of_state(

self: stormpy.models.SparseModel[ValueType],

state: SupportsInt | SupportsIndex,

) → set[str]

Get labels of state

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

Reward model

SparseModel.get_states_with_parameter(

self: stormpy.models.SparseModel[ValueType],

parameter: stormpy.numbers.Variable,

) → stormpy.datastructures.BitVector

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

Find states with a particular parameter

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

Does the model have an associated choice labelling?

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

has choice origins?

property SparseModel.has_parameters

Flag whether model has parameters

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

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

has state valuation?

property SparseModel.initial_states

Initial states

property SparseModel.initial_states_as_bitvector

property SparseModel.is_discrete_time_model

Flag whether the model is a discrete time model

property SparseModel.is_exact

Flag whether model is exact

property SparseModel.is_nondeterministic_model

Flag whether the model contains nondeterminism

property SparseModel.is_partially_observable

Flag whether the model has observation labels

SparseModel.is_sink_state(

self: stormpy.models.SparseModel[ValueType],

state: SupportsInt | SupportsIndex,

) → bool

property SparseModel.is_sparse_model

Flag whether the model is stored as a sparse model

property SparseModel.is_symbolic_model

Flag whether the model is stored using decision diagrams

property SparseModel.labeling

Labels

property SparseModel.model_type

Model type

property SparseModel.nr_choices

Number of choices

property SparseModel.nr_states

Number of states

property SparseModel.nr_transitions

Number of transitions

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

property SparseModel.reward_models

Reward models

SparseModel.set_initial_states(

self: stormpy.models.SparseModel[ValueType],

states: stormpy.datastructures.BitVector,

) → None

Set initial states

property SparseModel.state_valuations

state valuations

property SparseModel.states

Get states

property SparseModel.supports_parameters

Flag whether model supports parameters

property SparseModel.supports_uncertainty

Flag whether model supports uncertainty via intervals

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

Write dot to a string

property SparseModel.transition_matrix

Transition matrix