continuous least squares polynomial in python

main.py
import numpy as np
from scipy.interpolate import make_interp_spline, BSpline

# Generate some sample data
x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 8, 20, 38, 62])

# Define the degree of the polynomial
poly_degree = 2

# Get the polynomial coefficients
poly_coeffs = np.polyfit(x, y, poly_degree)

# Generate the polynomial function
poly_func = np.poly1d(poly_coeffs)

# Plot the original data and the fitted polynomial
import matplotlib.pyplot as plt
x_new = np.linspace(x.min(), x.max(), 300)
y_new = poly_func(x_new)
plt.plot(x, y, 'o', x_new, y_new)
plt.show()
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main.py
import numpy as np
from scipy.interpolate import make_interp_spline, BSpline

# Generate some sample data
x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 8, 20, 38, 62])

# Define the degree of the polynomial
poly_degree = 2

# Get the polynomial coefficients
poly_coeffs = np.polyfit(x, y, poly_degree)

# Generate the polynomial function
poly_func = np.poly1d(poly_coeffs)

# Interpolate the data
spline_func = make_interp_spline(x, y, k=poly_degree)

# Generate a higher resolution x range
x_new = np.linspace(x.min(), x.max(), 300)

# Evaluate the fitted curve at the new x range
y_new = spline_func(x_new)

# Plot the original data and the fitted polynomial
plt.plot(x, y, 'o', x_new, y_new)
plt.show()
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