Polynomial Fit#

GraphingLib components used: Figure
, FitFromPolynomial
, Scatter
import numpy as np
import graphinglib as gl
# Create noisy data0
x = np.linspace(0, 10, 100)
y = x**2 - 3 * x + 3 + np.random.normal(0, 7, 100)
scatter = gl.Scatter(x, y, "Data")
fit = gl.FitFromPolynomial(scatter, degree=2, label="Fit")
# Use the fit to predict value of y at x = 5.
print(f"Value of fit at x = 5 is y = {fit.function(5)}")
predicted_point = fit.create_point_at_x(5, color="red")
fig = gl.Figure(size=(8, 6), x_label="x values", y_label="y values")
fig.add_elements(scatter, fit, predicted_point)
fig.show()