5.1.6.6. numdifftools.nd_algopy.directionaldiff¶
- directionaldiff(f, x0, vec, **options)[source]¶
Return directional derivative of a function of n variables
- Parameters
- fun: callable
analytical function to differentiate.
- x0: array
vector location at which to differentiate fun. If x0 is an nxm array, then fun is assumed to be a function of n*m variables.
- vec: array
vector defining the line along which to take the derivative. It should be the same size as x0, but need not be a vector of unit length.
- **options:
optional arguments to pass on to Derivative.
- Returns
- dder: scalar
estimate of the first derivative of fun in the specified direction.
See also
Examples
At the global minimizer (1,1) of the Rosenbrock function, compute the directional derivative in the direction [1 2]
>>> import numpy as np >>> import numdifftools.nd_algopy as nda >>> vec = np.r_[1, 2] >>> rosen = lambda x: (1-x[0])**2 + 105*(x[1]-x[0]**2)**2 >>> dd = nda.directionaldiff(rosen, [1, 1], vec) >>> np.allclose(dd, 0) True