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Module « scipy.optimize »

Fonction approx_fprime - module scipy.optimize

Signature de la fonction approx_fprime

def approx_fprime(xk, f, epsilon=np.float64(1.4901161193847656e-08), *args) 

Description

help(scipy.optimize.approx_fprime)

Finite difference approximation of the derivatives of a
scalar or vector-valued function.

If a function maps from :math:`R^n` to :math:`R^m`, its derivatives form
an m-by-n matrix
called the Jacobian, where an element :math:`(i, j)` is a partial
derivative of f[i] with respect to ``xk[j]``.

Parameters
----------
xk : array_like
    The coordinate vector at which to determine the gradient of `f`.
f : callable
    Function of which to estimate the derivatives of. Has the signature
    ``f(xk, *args)`` where `xk` is the argument in the form of a 1-D array
    and `args` is a tuple of any additional fixed parameters needed to
    completely specify the function. The argument `xk` passed to this
    function is an ndarray of shape (n,) (never a scalar even if n=1).
    It must return a 1-D array_like of shape (m,) or a scalar.

    Suppose the callable has signature ``f0(x, *my_args, **my_kwargs)``, where
    ``my_args`` and ``my_kwargs`` are required positional and keyword arguments.
    Rather than passing ``f0`` as the callable, wrap it to accept
    only ``x``; e.g., pass ``fun=lambda x: f0(x, *my_args, **my_kwargs)`` as the
    callable, where ``my_args`` (tuple) and ``my_kwargs`` (dict) have been
    gathered before invoking this function.

    .. versionchanged:: 1.9.0
        `f` is now able to return a 1-D array-like, with the :math:`(m, n)`
        Jacobian being estimated.

epsilon : {float, array_like}, optional
    Increment to `xk` to use for determining the function gradient.
    If a scalar, uses the same finite difference delta for all partial
    derivatives. If an array, should contain one value per element of
    `xk`. Defaults to ``sqrt(np.finfo(float).eps)``, which is approximately
    1.49e-08.
\*args : args, optional
    Any other arguments that are to be passed to `f`.

Returns
-------
jac : ndarray
    The partial derivatives of `f` to `xk`.

See Also
--------
check_grad : Check correctness of gradient function against approx_fprime.

Notes
-----
The function gradient is determined by the forward finite difference
formula::

             f(xk[i] + epsilon[i]) - f(xk[i])
    f'[i] = ---------------------------------
                        epsilon[i]

Examples
--------
>>> import numpy as np
>>> from scipy import optimize
>>> def func(x, c0, c1):
...     "Coordinate vector `x` should be an array of size two."
...     return c0 * x[0]**2 + c1*x[1]**2

>>> x = np.ones(2)
>>> c0, c1 = (1, 200)
>>> eps = np.sqrt(np.finfo(float).eps)
>>> optimize.approx_fprime(x, func, [eps, np.sqrt(200) * eps], c0, c1)
array([   2.        ,  400.00004208])

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