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import torch
from memtorch.bh.memristor.Memristor import Memristor
class MemtorchMemristor(Memristor):
def __init__(
self,
k_off = 1.0, # switching rate for off state
k_on = -1.0, # switching rate for on state
alpha_off = 5, # exponent controlling nonlinearity
alpha_on = 5, # exponent controlling nonlinearity
i_off = 0.5e-3, # threshhold current to trigger off state
i_on = 0.5e-3, # threshold current to trigger on state
r_on = 1e3, # maximum resistance
r_off = 10e3, # minimum resistance
p = 2, # window function exponent
**kwargs
):
#initializing base memristor class
super(MemtorchMemristor, self).__init__(r_off=r_off, r_on=r_on, **kwargs)
# hyper parameters
self.k_off = k_off
self.k_on = k_on
self.alpha_off = alpha_off
self.alpha_on = alpha_on
self.i_on = i_on
self.i_off = i_off
self.p = p
# makes sure w starts in valid state
if not hasattr(self, 'w'):
self.w = torch.tensor(0.5)
"""
Updates w and computes new resistance
"""
def step(self, v, dt):
i = v / self.r_curr
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