MinigamePythonBeginner
scipy.optimize.least_squares
Python. Called in 2 tutorials; each line below leads to its place in the tutorial.
ConceptPythonAdvanced
Profile likelihood: the lopsided error bar that curve_fit's ± hides
- fit = least_squares(lambda p: residuals(N, rate(t, *p)), x0=[40, 10, 15], bounds=(lower, np.inf))
- r = least_squares(lambda q: residuals(N, rate(t, q[0], T, q[1])), x0=[A_hat, B_hat], bounds=(0, np.inf))
- r = least_squares(lambda q: residuals(N, rate(t, q[0], T, B)), x0=[A_hat], bounds=(0, np.inf))
- r = least_squares(lambda p: residuals(Nk, rate(t, *p)), x0=best, bounds=(lower, np.inf))