RuntimeWarning: invalid value encountered in double_scalars

[FrankenDeba]

C:\Users\debax\AppData\Local\Programs\Python\Python36-32\python.exe C:/Users/debax/Desktop/node/linear.py
C:/Users/debax/Desktop/node/linear.py:39: RuntimeWarning: overflow encountered in double_scalars
b_gradient+=((2/N)(-x(y-(cur_mx+cur_b))))
after 1000 iterations:
nan
nan
C:/Users/debax/Desktop/node/linear.py:41: RuntimeWarning: invalid value encountered in double_scalars
new_b=cur_b-(learning_rateb_gradient)
C:/Users/debax/Desktop/node/linear.py:42: RuntimeWarning: invalid value encountered in double_scalars
new_m=cur_m-(learning_rate*b_gradient)

Process finished with exit code 0

[AleeIbrahim]

Hi Franken,
If you're using some real non scaled data like me then you'll be facing that problem.
In lines 23, 24
b_gradient += (-2/N) * (y - ((m_current * x) + b_current))
m_gradient += (-2/N) * x * (y - ((m_current * x) + b_current))
the gradient values are increasing each time with such great quantities because of the large real values stored in the variables x and y. Hence both b_gradient and m_gradient will not be sufficient in order to have such large values stored inside them even if you converted then into int64.

So, in order to solve such a problem,
you'll need to scale your data set values so they would range in between 1 and -1 or any other small values.

[DulalBibek]

Hi Franken,
If you're using some real non scaled data like me then you'll be facing that problem.
In lines 23, 24
b_gradient += (-2/N) * (y - ((m_current * x) + b_current))
m_gradient += (-2/N) * x * (y - ((m_current * x) + b_current))
the gradient values are increasing each time with such great quantities because of the large real values stored in the variables x and y. Hence both b_gradient and m_gradient will not be sufficient in order to have such large values stored inside them even if you converted then into int64.

So, in order to solve such a problem,
you'll need to scale your data set values so they would range in between 1 and -1 or any other small values.

Thank you It worked