import numpy as np
import pylab as pl

avgO=0.0    # online average
steps=5000  # iteration steps
avgs=np.zeros(steps)

# First try - a linearly decreasing learning rate (1/n)
for i in range(1,steps+1):
    a=np.random.randn()+2	    # random - N(2,1)
    if i>3000:
        a+=3                        # random - N(5,1)
    avgO+=(a-avgO)/i            # online averaging
    avgs[i-1]=avgO
    
## Display the results
pl.plot(avgs);

# Second try - use a small constant learning rate
for i in range(1,steps+1):
    a=np.random.randn()+2	    # random - N(2,1)
    if i>3000:
        a+=3                        # random - N(5,1)
    avgO+=(a-avgO)*0.02           # online averaging
    avgs[i-1]=avgO
    
## Display the results
pl.plot(avgs);