Get the formula of a interpolation function created by scipy

Question

I have done some work in Python, but I'm new to scipy. I'm trying to use the methods from the interpolate library to come up with a function that will approximate a set of data.

I've looked up some examples to get started, and could get the sample code below working in Python(x,y):

import numpy as np
from scipy.interpolate import interp1d, Rbf
import pylab as P

# show the plot (empty for now)
P.clf()
P.show()

# generate random input data
original_data = np.linspace(0, 1, 10)

# random noise to be added to the data
noise = (np.random.random(10)*2 - 1) * 1e-1

# calculate f(x)=sin(2*PI*x)+noise
f_original_data = np.sin(2 * np.pi * original_data) + noise

# create interpolator
rbf_interp = Rbf(original_data, f_original_data, function='gaussian')

# Create new sample data (for input), calculate f(x) 
#using different interpolation methods
new_sample_data = np.linspace(0, 1, 50)
rbf_new_sample_data    = rbf_interp(new_sample_data)

# draw all results to compare
P.plot(original_data, f_original_data, 'o', ms=6, label='f_original_data')
P.plot(new_sample_data, rbf_new_sample_data, label='Rbf interp')
P.legend()

The plot is displayed as follows:

Now, is there any way to get a polynomial expression representing the interpolated function created by Rbf (i.e. the method created as rbf_interp)?

Or, if this is not possible with Rbf, any suggestions using a different interpolation method, another library, or even a different tool are also welcome.

Answer 1

The answer is no, there is no "nice" way to write down the formula, or at least not in a short way. Some types of interpolations, like RBF and Loess, do not directly search for a parametric mathematical function to fit to the data and instead they calculate the value of each new data point separately as a function of the other points.

These interpolations are guaranteed to always give a good fit for your data (such as in your case), and the reason for this is that to describe them you need a very large number of parameters (basically all your data points). Think of it this way: you could interpolate linearly by connecting consecutive data points with straight lines. You could fit any data this way and then describe the function in a mathematical form, but it would take a large number of parameters (at least as many as the number of points). Actually what you are doing right now is pretty much a smoothed version of that.

If you want the formula to be short, this means you want to describe the data with a mathematical function that does not have many parameters (specifically the number of parameters should be much lower than the number of data points). Such examples are logistic functions, polynomial functions and even the sine function (that you used to generate the data). Obviously, if you know which function generated the data that will be the function you want to fit.