Open bcdaniels opened 6 years ago
Actually, it looks like you've already split it up (#123)
No clear reference here—we probably need to think a bit about the math. But my guess is that this will be related to the "Derrida curve" as seen in, e.g.,
Compute the sensitivity based on state differences after t
time steps. The current implementation of SensitivityMixin.sensitivity
and SensitivityMixin.average_sensitivity
implicitly use t = 1
.
Modify the neet.boolean.sensitivity.SensitivityMixin
methods sensitivity
and average_sensitivity
to accept a timesteps
parameter.
class SensitivityMixin(object);
def sensitivity(state, timesteps=1, transitions=None):
pass
def average_sensitivity(timesteps=1, transitions=None):
pass
from neet.boolean.examples import s_pombe
s_pombe.sensitivity([0, 0, 1, 0, 0, 1, 0, 0, 1], timesteps=4)
s_pombe.average_sensitivity(timesteps=4)
Balleza, Enrique, Elena R. Alvarez-Buylla, Alvaro Chaos, Stuart Kauffman, Ilya Shmulevich, and Maximino Aldana. “Critical Dynamics in Genetic Regulatory Networks: Examples from Four Kingdoms.” PLoS ONE 3, no. 6 (2008). doi:10.1371/journal.pone.0002456.
@bcdaniels Do you have a reference for this? Should we split this issue into two: