Solution:Step 1: Fit a 2nd degree polynomial to pointsThe points (x, y) fit exactly to y = 1 + 2x + x^2, so polyfit finds coefficients close to [1, 2, 1].Step 2: Use polyval to compute fitted values at xEvaluating the polynomial at x gives the original y values: [1, 3, 7, 13].Final Answer:[ 1. 3.

[Solved] Given the code:import numpy as np from scipy import polyfit, polyval x = np.array([0, 1, 2, 3]) y = np.... | SciPy

SciPy - Curve Fitting and Regression

Given the code:

import numpy as np
from scipy import polyfit, polyval
x = np.array([0, 1, 2, 3])
y = np.array([1, 3, 7, 13])
coeffs = polyfit(x, y, 2)
fitted = polyval(coeffs, x)
print(fitted)

What is the output printed?

A[ 1. 4. 9. 16.]

B[ 1. 2. 4. 8.]

C[ 0. 1. 4. 9.]

D[ 1. 3. 7. 13.]

Step-by-Step Solution

Solution:

Step 1: Fit a 2nd degree polynomial to points
The points (x, y) fit exactly to y = 1 + 2x + x^2, so polyfit finds coefficients close to [1, 2, 1].
Step 2: Use polyval to compute fitted values at x
Evaluating the polynomial at x gives the original y values: [1, 3, 7, 13].
Final Answer:
[ 1. 3. 7. 13.] -> Option D
Quick Check:
polyval(coeffs, x) = original y [OK]

Quick Trick: polyval with polyfit coeffs returns fitted y values [OK]

Common Mistakes:

Confusing input arrays order
Expecting different output than original y
Misunderstanding polynomial degree effect

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More SciPy Quizzes

Given the code:

Step 1: Fit a 2nd degree polynomial to points

Step 2: Use polyval to compute fitted values at x

Final Answer:

Quick Check:

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