on jupyter notebook

Use each of the three solvers scipy.optimize.minimize, scipy.optimize. least_squares and scipy.optimize.curve_fit to fit a multi-parameter function to made up data. You may choose either Fourth polynomial (easy) f(x;) = (4x+ + azx' + az x + x + 20 . Gaussian function (harder to program) f(x; ) = Qoe-((-a)?la? . Cosine function (harder to fit) f(x; ) = do cos(4x + 2) Construct slightly noisy data much like the lecutre notes For each fit, plot the data, the true curve, the fit. Use each of the three solvers scipy.optimize.minimize, scipy.optimize. least_squares and scipy.optimize.curve_fit to fit a multi-parameter function to made up data. You may choose either Fourth polynomial (easy) f(x;) = (4x+ + azx' + az x + x + 20 . Gaussian function (harder to program) f(x; ) = Qoe-((-a)?la? . Cosine function (harder to fit) f(x; ) = do cos(4x + 2) Construct slightly noisy data much like the lecutre notes For each fit, plot the data, the true curve, the fit