Question

Write a function that takes as argument two Numpy arrays and uses the built-in Numpy regression functions to compute and return the coefficients a1 and a0, and the fitting function f _linear, in that order.

Answer 1

To compute the coefficients and fitting function using NumPy's regression functions, you can use the polyfit function.

Step-by-step explanation:

To compute the coefficients and fitting function using NumPy's regression functions, you can use the polyfit function. Here's an example:

import numpy as np

def compute_regression(x, y):
a = np.polyfit(x, y, deg=1)
a1 = a[0]
a0 = a[1]
f_linear = np.poly1d(a)
return a1, a0, f_linear

x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 6, 8, 10])
a1, a0, f_linear = compute_regression(x, y)
print('a1:', a1)
print('a0:', a0)
print('f_linear:', f_linear)

In this example, the NumPy polyfit function is used to compute the coefficients 'a1' and 'a0', and the resulting polynomial function 'f_linear' is created using 'poly1d'. The 'deg=1' argument in 'polyfit' specifies a linear regression.