Data Science SCIPY for Gradient evaluation with optimization:-
SCIPY provide an optimize package to provide optimization to linear-gradient evaluation using multiple algorithms
Optimization and Fit in SciPy – scipy.optimize
Optimization provides a useful algorithm for minimization of curve fitting, multidimensional or scalar, and root fitting.
%matplotlib inline import matplotlib.pyplot as plt from scipy import optimize import numpy as npfre = 5 #Sample rate fre_samp = 50t = np.linspace(0, 2, 2 * fre_samp, endpoint = False ) a = np.sin(fre * 2 * np.pi * t)def function(a): return a*2 + 20 * np.sin(a) plt.plot(a, function(a)) plt.show() #use BFGS algorithm for optimization optimize.fmin_bfgs(function, 0)
Nelder –Mead Algorithm:
- Nelder-Mead algorithm selects through method parameter.
- It provides the most straightforward way of minimization for fair behaved function.
- Nelder – Mead algorithm is not used for gradient evaluations because it may take a longer time to find the solution.
import numpy as np from scipy import optimize #define function f(x) def f(x): return .4*(1 - x[0])**2 optimize.minimize(f, [2, -1], method="Nelder-Mead")![]()
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