I used sklearn in python to fit polynomial functions:

from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression

poly = PolynomialFeatures(degree=2, include_bias=False)
poly_reg_model = LinearRegression()
poly_features = poly.fit_transform(xvalues.reshape(-1, 1))

poly_reg_model.fit(poly_features, y_values)
final_predicted = poly_reg_model.predict(poly_features)
...

Instead of only using x^n parts, I want to incude a (1-x^2)^(1/2) part in the fit-function. Is this possible with sklearn?

I tried to define a Feature which includes more complex terms but I falied to achieve this.

CodePudding user response：

No idea whether it is possible within scikitlearn - after all polynomial fit is constrained to specific polynomial formulations from the mathematical stanndpoint. If you want to fit a formula with some unknown parameters, you can use

So now you can use f_opt(new_x, *popt) to predict new points (alternatively you can print the values and hard-code them). popt basically has the parameters that you specify in f_opt except x - for more details check the documentation I've linked!