simple_lif.py

import numpy as np

def matrixpush(m, row):
    if not np.all(np.isfinite(values[:,0])):
        i = np.count_nonzero(np.logical_not(np.isnan(values[:,0])))
        m[i,] = row
    else:
        m = np.vstack([m,row])
        m = m[1:,]
    return(m)

def getobs( x, max = 5, err=0 ):
    if (err==0):
        obsr = -1*pow((x-max),2)
    else:
        obsr = -1*pow((x-max),2) + np.random.normal(0,err,1)
    return obsr;

max = 5                           # Set maximum

stream = 3000                     # Length of stream
inttime = 100                     # Integration time
amplitude = 1.4                   # Amlitude LIF
learnrate = .004                  # Learnrate
omega = (2*np.pi)/inttime         # Omega
x0 = 1.0                          # Startvalue

p_return = 0.80                   # Chance that returns
variance = 1                      # variance in observations

values = np.zeros((inttime,3))
values.fill(np.nan)

x = 0.0
t = 0.0
y = 0.0

for t in range(0,stream):
    x = x0 + amplitude*np.cos(omega * t)
    y = amplitude*np.cos(omega * t)*getobs(x,5,variance)
    values = matrixpush(values, np.array([t,x,y]))
    if np.all(np.isfinite(values[:,0])):
        x0 = x0 + learnrate * sum( values[:,2] ) / inttime
print(x0)