Optimisation

The numpy module has some useful functions to calculate polynomial fits

Least squares polynomial fit

This can be accomplished with the numpy.polyfit(x, y, deg) function where x and y are two arrays and deg is the degree of the polynomial.

Here is an example where we fit [1,6], [3,3], [5,9] and [7,5] to a polynomial of degree 1:

import numpy as np
import matplotlib.pyplot as plt

x = np.array([1, 3, 5, 7])
y = np.array([ 6, 3, 9, 5 ])

m, b = np.polyfit(x, y, 1)

print(m)
print(b)

plt.plot(x, y, 'o') # plot data points
plt.plot(x, m*x + b) # plot line of best fit

plt.show()

Try it out

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