Overflow error using LMS algorithm from Neupy with example

Question

I tried the example for LMS algorithm:

import numpy as np
from neupy import algorithms

input_data = np.array([[1, 0], [2, 2], [3, 3], [0, 0]])
target_data = np.array([[1], [0], [0], [1]])

lmsnet = algorithms.LMS((2, 1), step=0.5)

lmsnet.train(input_data, target_data, epochs=200)
lmsnet.predict(np.array([[4, 4], [0, 0]])) 

But I get "OverflowError: cannot convert float infinity to integer" error in this line (file:summary_info.py):

scale = math.ceil(self.delay_limit / average_delay)

I can't relate the input parameters from the example to the error, I know that a division by zero get there but I can't figure out how to fix this. I don't want to modify library files to fix the problem.

Answer 1

Your example works perfectly fine for me

You can overcome this issue, if you train your network in a loop, like this

import numpy as np
from neupy import algorithms

input_data = np.array([[1, 0], [2, 2], [3, 3], [0, 0]])
target_data = np.array([[1], [0], [0], [1]])

# Used smaller step since 0.5 is too big
lmsnet = algorithms.LMS((2, 1), step=0.1)

for _ in range(200):
    lmsnet.train(input_data, target_data, epochs=1)

lmsnet.predict(np.array([[4, 4], [0, 0]]))