amachine.am_fast._am_fast API documentation

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# amachine.am_fast._am_fast

HMM utilities (nanobind)

class ComplexityMeasures:
ComplexityMeasures(*args, **kwargs)
E

(self) -> float

T

(self) -> float

S

(self) -> float

E_L

(self) -> numpy.ndarray[dtype=float64, shape=(1), order='C']

T_L

(self) -> numpy.ndarray[dtype=float64, shape=(1), order='C']

S_L

(self) -> numpy.ndarray[dtype=float64, shape=(1), order='C']

H_L

(self) -> numpy.ndarray[dtype=float64, shape=(1), order='C']

h_mu_L

(self) -> numpy.ndarray[dtype=float64, shape=(1), order='C']

H_sync

(self) -> numpy.ndarray[dtype=float64, shape=(1), order='C']

converged

(self) -> bool

block_entropy_convergence_cpp = <nanobind.nb_func object>
generate_cpp = <nanobind.nb_func object>
strongly_connected_components_cpp = <nanobind.nb_func object>
class MinifyResult:
MinifyResult(*args, **kwargs)
is_empty_language

(self) -> bool

True if the minimised DFA accepts no strings.

n_classes

(self) -> int

Number of equivalence classes (= states in the minimised DFA).

new_initial

(self) -> int

Class id of the initial state.

eq_class

(self) -> list[int]

eq_class[old_idx] = new class id, or -1 for trap-equivalent states.

class_trans

(self) -> list[list[tuple[int, int]]]

class_trans[class] = list of DFTransition objects.

class_is_final

(self) -> list[bool]

class_is_final[class] = True if the class is an accepting state.

minify_dfa_cpp = <nanobind.nb_func object>