Lagrange Interpolation Method: Algorithm, Computation and Plot | Numerical Computing with Python

[JD]

The best explanation, it was so lucid that I don't need to learn from anywhere about this topic any further, thanks a lot. ♥

[asifahmed]

cool ! This is one of the best explainaton i have ever seen regarding numerical python subjects

[DeGameBox_SRBT]

thanks! thanks! thanks! you just saved my studwork!

[AJ-etvf]

Great video sir! Thank you! You explain very well and your voice and video are very soothing and calming. Very nice to listen to.

[ignatiolu]

Just what I was looking for, sir! Great video!

[wolnyczowiek]

Thank you so much that was exactly what i was looking for

[IndrainKorea]

Thank you for the tutorial! I like the way you explain it too, very clear!

[MathPhysicsFunwithGus]

Amazing video can't wait for more!

[manashdey]

Very nice explanation sir 😊.. thank you,

[CAMiiKatze]

great. Blew my mind how you can do it in so little lines ! :)

[angelortiz]

Cool video. Really help me understand this algorithm. I have one questions tho. Given that the code is reseting the yp value every time it calculates, and in truth, the yp values depends on the previously calculated, isn't there a way to use the sum of the previously calculated yplt terms?

[syahputra]

thanks you sir
great video and clear explanation..
btw I really hope you can explane a litle bite description of how to plot lines in linear regression or polinomial regresion.
Thanks you

[sandeepgodiyal]

Thanks for this wonderful course. Can you please help for a small problem for polynomial fitting (4-5 order) of data and to find out regression coefficients?

[LoganKaleva]

I know this video is old, but i'm copying the code line for line and it's saying my i isn't defined for yp +=

[amvro]

A similar more comprehensive perpective

import numpy as np

x = np.linspace(0, 100, 6)
y = np.array([26.0, 48.6, 61.6, 71.2, 74.8, 75.2])

def lagrange_interpolation(x, y, xp):
n = len(x) - 1 # degree of the polynomial
index_i = range(n + 1)
index_j = range(n + 1)

p_values = np.array([np.prod([(xp - x[j]) / (x[i] - x[j]) for j in index_j if j != i]) for i in index_i])
yp = p_values.dot(y[index_i])

return yp

lagrange_interpolation(x, y, 50)

[erfanjavaheri]

Sir i want for n points how can i do that?

[agustinhernandez]

Hi, your video is helping me to do a university homework but the teacher want to know what was the polynomial, So i dont have to input the X value. How can i do in order to get the polynomial?

[quyashbekallanazarov]

u can use product they fixed i guess mine is working 11:20

[purib]

can you write code for differentiation to Lagrange's formula

[sugandhasingh]

how to implement same method for any image?