PyRBIT
See also
Home page
Exponential Forgetting Model
ACT-R Model
Memory Utilities
Evaluating the informativeness of a schedule
Comparing block recall percentages, power analysis
API reference
pyrbit.abc
pyrbit.actr
pyrbit.design
pyrbit.ef
pyrbit.ef.covar_delta_method_log_alpha
pyrbit.ef.diagnostics
pyrbit.ef.ef_ddq0_dalpha_dalpha_sample
pyrbit.ef.ef_ddq0_dalpha_dbeta_sample
pyrbit.ef.ef_ddq0_dbeta_dbeta_sample
pyrbit.ef.ef_ddq1_dalpha_dalpha_sample
pyrbit.ef.ef_ddq1_dalpha_dbeta_sample
pyrbit.ef.ef_ddq1_dbeta_dbeta_sample
pyrbit.ef.ef_dq0_dalpha_sample
pyrbit.ef.ef_dq0_dbeta_sample
pyrbit.ef.ef_dq1_dalpha_sample
pyrbit.ef.ef_dq1_dbeta_sample
pyrbit.ef.ef_observed_information_matrix
pyrbit.ef.ef_p0_sample
pyrbit.ef.ef_p1_sample
pyrbit.ef.ef_q0_sample
pyrbit.ef.ef_q1_sample
pyrbit.ef.flatten
pyrbit.ef.get_k_delta_schedule
pyrbit.ef.identify_ef_from_recall_sequence
identify_ef_from_recall_sequence()
pyrbit.ef.loglogpplot
pyrbit.ef.plot_exponent_scatter
pyrbit.ef.ExponentialForgetting
pyrbit.information
pyrbit.mem_utils
pyrbit.mle_utils
pyrbit.plot_utils
PyRBIT
pyrbit
pyrbit.ef
pyrbit.ef.identify_ef_from_recall_sequence
View page source
pyrbit.ef.identify_ef_from_recall_sequence
identify_ef_from_recall_sequence
(
recall_sequence
,
deltas
,
k_vector
=
None
,
guess
=
(0.001,
0.5)
,
optim_kwargs
=
{'bounds':
[(1e-07,
0.5),
(0,
0.99)],
'method':
'L-BFGS-B'}
,
verbose
=
True
,
basin_hopping
=
False
,
basin_hopping_kwargs
=
None
)
[source]