ontolearn.learners.spell_kit.fitting

Attributes

mode

HC

Simul

Pi

Pr

var_counter

Classes

Variables

Functions

compute_successors(sigma, A)

fresh_var()

constraint_conceptname(size, A, hc, ind_tp_idx, ...)

constraint_succ(size, A, sigma, pi, pr, simul, DR)

complement_type(tp, sigma)

compute_types(A, sigma)

simulation_constraints(size, sigma, A, mapping)

real_coverage(→ int)

is_model(size, sigma, model, mapping, solver)

minimize_concept_assertions(→ set[int])

model2fitting_query(...)

create_variables(→ Variables)

tree_query_constraints(size, sigma, v)

create_coverage_formula(→ list[list[int]])

non_empty_symbols(...)

determine_relevant_symbols(...)

restrict_nb(...)

solve(→ Union[tuple[int, ...)

solve_incr(→ tuple[int, ...)

Module Contents

ontolearn.learners.spell_kit.fitting.mode
ontolearn.learners.spell_kit.fitting.HC
ontolearn.learners.spell_kit.fitting.Simul
ontolearn.learners.spell_kit.fitting.Pi
ontolearn.learners.spell_kit.fitting.Pr
class ontolearn.learners.spell_kit.fitting.Variables[source]

Bases: NamedTuple

simul: Simul
pi: Pi
pr: Pr
hc: HC
ontolearn.learners.spell_kit.fitting.compute_successors(sigma: Signature, A: Structure)[source]
ontolearn.learners.spell_kit.fitting.var_counter: int = 1
ontolearn.learners.spell_kit.fitting.fresh_var()[source]
ontolearn.learners.spell_kit.fitting.constraint_conceptname(size: int, A: Structure, hc: HC, ind_tp_idx, anti_types, type_var: list[dict[int, int]], simul: Simul)[source]
ontolearn.learners.spell_kit.fitting.constraint_succ(size: int, A: Structure, sigma: Signature, pi: Pi, pr: Pr, simul: Simul, DR: list[list[list[int]]])[source]
ontolearn.learners.spell_kit.fitting.complement_type(tp, sigma: Signature)[source]
ontolearn.learners.spell_kit.fitting.compute_types(A: Structure, sigma: Signature)[source]
ontolearn.learners.spell_kit.fitting.simulation_constraints(size: int, sigma: Signature, A: Structure, mapping: Variables)[source]
ontolearn.learners.spell_kit.fitting.real_coverage(model, P: list[int], N: list[int], mapping: Variables) → int[source]
ontolearn.learners.spell_kit.fitting.is_model(size: int, sigma: Signature, model: set[int], mapping: Variables, solver: pysat.solvers.Glucose4)[source]
ontolearn.learners.spell_kit.fitting.minimize_concept_assertions(size: int, sigma: Signature, solver: pysat.solvers.Glucose4, mapping: Variables, model: set[int]) → set[int][source]
ontolearn.learners.spell_kit.fitting.model2fitting_query(size: int, sigma: Signature, mapping: Variables, model: set[int]) → Structure[source]
ontolearn.learners.spell_kit.fitting.create_variables(size: int, sigma: Signature, A: Structure) → Variables[source]
ontolearn.learners.spell_kit.fitting.tree_query_constraints(size: int, sigma: Signature, v: Variables)[source]
ontolearn.learners.spell_kit.fitting.create_coverage_formula(P: list[int], N: list[int], coverage: int, mapping: Variables, all_pos: bool) → list[list[int]][source]
ontolearn.learners.spell_kit.fitting.non_empty_symbols(A: Structure) → Signature[source]
ontolearn.learners.spell_kit.fitting.determine_relevant_symbols(A: Structure, P: list[int], minP: int, dist: int) → Signature[source]
ontolearn.learners.spell_kit.fitting.restrict_nb(k: int, A: Structure, P: list[int], N: list[int]) → tuple[Structure, list[int], list[int]][source]
ontolearn.learners.spell_kit.fitting.solve(size: int, A: Structure, P: list[int], N: list[int], coverage_lb: int, all_pos: bool, timeout: float = -1) → tuple[int, Structure] | None[source]
ontolearn.learners.spell_kit.fitting.solve_incr(A: Structure, P: list[int], N: list[int], m: mode, timeout: float = -1, starting_size: int = 1, max_size: int = 19) → tuple[int, Structure][source]