640 lines
23 KiB
Python
640 lines
23 KiB
Python
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"""Routines for numerical differentiation."""
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from __future__ import division
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import numpy as np
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from numpy.linalg import norm
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from scipy.sparse.linalg import LinearOperator
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from ..sparse import issparse, csc_matrix, csr_matrix, coo_matrix, find
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from ._group_columns import group_dense, group_sparse
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EPS = np.finfo(np.float64).eps
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def _adjust_scheme_to_bounds(x0, h, num_steps, scheme, lb, ub):
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"""Adjust final difference scheme to the presence of bounds.
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Parameters
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----------
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x0 : ndarray, shape (n,)
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Point at which we wish to estimate derivative.
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h : ndarray, shape (n,)
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Desired finite difference steps.
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num_steps : int
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Number of `h` steps in one direction required to implement finite
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difference scheme. For example, 2 means that we need to evaluate
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f(x0 + 2 * h) or f(x0 - 2 * h)
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scheme : {'1-sided', '2-sided'}
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Whether steps in one or both directions are required. In other
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words '1-sided' applies to forward and backward schemes, '2-sided'
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applies to center schemes.
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lb : ndarray, shape (n,)
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Lower bounds on independent variables.
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ub : ndarray, shape (n,)
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Upper bounds on independent variables.
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Returns
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-------
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h_adjusted : ndarray, shape (n,)
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Adjusted step sizes. Step size decreases only if a sign flip or
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switching to one-sided scheme doesn't allow to take a full step.
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use_one_sided : ndarray of bool, shape (n,)
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Whether to switch to one-sided scheme. Informative only for
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``scheme='2-sided'``.
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"""
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if scheme == '1-sided':
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use_one_sided = np.ones_like(h, dtype=bool)
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elif scheme == '2-sided':
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h = np.abs(h)
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use_one_sided = np.zeros_like(h, dtype=bool)
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else:
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raise ValueError("`scheme` must be '1-sided' or '2-sided'.")
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if np.all((lb == -np.inf) & (ub == np.inf)):
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return h, use_one_sided
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h_total = h * num_steps
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h_adjusted = h.copy()
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lower_dist = x0 - lb
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upper_dist = ub - x0
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if scheme == '1-sided':
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x = x0 + h_total
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violated = (x < lb) | (x > ub)
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fitting = np.abs(h_total) <= np.maximum(lower_dist, upper_dist)
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h_adjusted[violated & fitting] *= -1
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forward = (upper_dist >= lower_dist) & ~fitting
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h_adjusted[forward] = upper_dist[forward] / num_steps
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backward = (upper_dist < lower_dist) & ~fitting
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h_adjusted[backward] = -lower_dist[backward] / num_steps
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elif scheme == '2-sided':
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central = (lower_dist >= h_total) & (upper_dist >= h_total)
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forward = (upper_dist >= lower_dist) & ~central
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h_adjusted[forward] = np.minimum(
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h[forward], 0.5 * upper_dist[forward] / num_steps)
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use_one_sided[forward] = True
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backward = (upper_dist < lower_dist) & ~central
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h_adjusted[backward] = -np.minimum(
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h[backward], 0.5 * lower_dist[backward] / num_steps)
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use_one_sided[backward] = True
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min_dist = np.minimum(upper_dist, lower_dist) / num_steps
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adjusted_central = (~central & (np.abs(h_adjusted) <= min_dist))
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h_adjusted[adjusted_central] = min_dist[adjusted_central]
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use_one_sided[adjusted_central] = False
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return h_adjusted, use_one_sided
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relative_step = {"2-point": EPS**0.5,
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"3-point": EPS**(1/3),
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"cs": EPS**0.5}
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def _compute_absolute_step(rel_step, x0, method):
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if rel_step is None:
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rel_step = relative_step[method]
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sign_x0 = (x0 >= 0).astype(float) * 2 - 1
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return rel_step * sign_x0 * np.maximum(1.0, np.abs(x0))
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def _prepare_bounds(bounds, x0):
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lb, ub = [np.asarray(b, dtype=float) for b in bounds]
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if lb.ndim == 0:
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lb = np.resize(lb, x0.shape)
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if ub.ndim == 0:
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ub = np.resize(ub, x0.shape)
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return lb, ub
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def group_columns(A, order=0):
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"""Group columns of a 2-d matrix for sparse finite differencing [1]_.
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Two columns are in the same group if in each row at least one of them
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has zero. A greedy sequential algorithm is used to construct groups.
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Parameters
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----------
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A : array_like or sparse matrix, shape (m, n)
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Matrix of which to group columns.
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order : int, iterable of int with shape (n,) or None
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Permutation array which defines the order of columns enumeration.
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If int or None, a random permutation is used with `order` used as
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a random seed. Default is 0, that is use a random permutation but
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guarantee repeatability.
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Returns
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-------
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groups : ndarray of int, shape (n,)
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Contains values from 0 to n_groups-1, where n_groups is the number
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of found groups. Each value ``groups[i]`` is an index of a group to
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which i-th column assigned. The procedure was helpful only if
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n_groups is significantly less than n.
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References
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----------
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.. [1] A. Curtis, M. J. D. Powell, and J. Reid, "On the estimation of
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sparse Jacobian matrices", Journal of the Institute of Mathematics
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and its Applications, 13 (1974), pp. 117-120.
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"""
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if issparse(A):
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A = csc_matrix(A)
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else:
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A = np.atleast_2d(A)
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A = (A != 0).astype(np.int32)
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if A.ndim != 2:
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raise ValueError("`A` must be 2-dimensional.")
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m, n = A.shape
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if order is None or np.isscalar(order):
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rng = np.random.RandomState(order)
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order = rng.permutation(n)
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else:
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order = np.asarray(order)
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if order.shape != (n,):
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raise ValueError("`order` has incorrect shape.")
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A = A[:, order]
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if issparse(A):
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groups = group_sparse(m, n, A.indices, A.indptr)
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else:
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groups = group_dense(m, n, A)
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groups[order] = groups.copy()
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return groups
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def approx_derivative(fun, x0, method='3-point', rel_step=None, f0=None,
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bounds=(-np.inf, np.inf), sparsity=None,
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as_linear_operator=False, args=(), kwargs={}):
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"""Compute finite difference approximation of the derivatives of a
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vector-valued function.
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If a function maps from R^n to R^m, its derivatives form m-by-n matrix
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called the Jacobian, where an element (i, j) is a partial derivative of
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f[i] with respect to x[j].
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Parameters
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----------
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fun : callable
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Function of which to estimate the derivatives. The argument x
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passed to this function is ndarray of shape (n,) (never a scalar
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even if n=1). It must return 1-d array_like of shape (m,) or a scalar.
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x0 : array_like of shape (n,) or float
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Point at which to estimate the derivatives. Float will be converted
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to a 1-d array.
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method : {'3-point', '2-point', 'cs'}, optional
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Finite difference method to use:
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- '2-point' - use the first order accuracy forward or backward
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difference.
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- '3-point' - use central difference in interior points and the
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second order accuracy forward or backward difference
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near the boundary.
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- 'cs' - use a complex-step finite difference scheme. This assumes
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that the user function is real-valued and can be
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analytically continued to the complex plane. Otherwise,
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produces bogus results.
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rel_step : None or array_like, optional
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Relative step size to use. The absolute step size is computed as
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``h = rel_step * sign(x0) * max(1, abs(x0))``, possibly adjusted to
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fit into the bounds. For ``method='3-point'`` the sign of `h` is
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ignored. If None (default) then step is selected automatically,
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see Notes.
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f0 : None or array_like, optional
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If not None it is assumed to be equal to ``fun(x0)``, in this case
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the ``fun(x0)`` is not called. Default is None.
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bounds : tuple of array_like, optional
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Lower and upper bounds on independent variables. Defaults to no bounds.
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Each bound must match the size of `x0` or be a scalar, in the latter
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case the bound will be the same for all variables. Use it to limit the
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range of function evaluation. Bounds checking is not implemented
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when `as_linear_operator` is True.
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sparsity : {None, array_like, sparse matrix, 2-tuple}, optional
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Defines a sparsity structure of the Jacobian matrix. If the Jacobian
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matrix is known to have only few non-zero elements in each row, then
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it's possible to estimate its several columns by a single function
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evaluation [3]_. To perform such economic computations two ingredients
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are required:
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* structure : array_like or sparse matrix of shape (m, n). A zero
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element means that a corresponding element of the Jacobian
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identically equals to zero.
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* groups : array_like of shape (n,). A column grouping for a given
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sparsity structure, use `group_columns` to obtain it.
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A single array or a sparse matrix is interpreted as a sparsity
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structure, and groups are computed inside the function. A tuple is
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interpreted as (structure, groups). If None (default), a standard
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dense differencing will be used.
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Note, that sparse differencing makes sense only for large Jacobian
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matrices where each row contains few non-zero elements.
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as_linear_operator : bool, optional
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When True the function returns an `scipy.sparse.linalg.LinearOperator`.
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Otherwise it returns a dense array or a sparse matrix depending on
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`sparsity`. The linear operator provides an efficient way of computing
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``J.dot(p)`` for any vector ``p`` of shape (n,), but does not allow
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direct access to individual elements of the matrix. By default
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`as_linear_operator` is False.
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args, kwargs : tuple and dict, optional
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Additional arguments passed to `fun`. Both empty by default.
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The calling signature is ``fun(x, *args, **kwargs)``.
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Returns
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-------
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J : {ndarray, sparse matrix, LinearOperator}
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Finite difference approximation of the Jacobian matrix.
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If `as_linear_operator` is True returns a LinearOperator
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with shape (m, n). Otherwise it returns a dense array or sparse
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matrix depending on how `sparsity` is defined. If `sparsity`
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is None then a ndarray with shape (m, n) is returned. If
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`sparsity` is not None returns a csr_matrix with shape (m, n).
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For sparse matrices and linear operators it is always returned as
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a 2-dimensional structure, for ndarrays, if m=1 it is returned
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as a 1-dimensional gradient array with shape (n,).
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See Also
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--------
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check_derivative : Check correctness of a function computing derivatives.
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Notes
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-----
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If `rel_step` is not provided, it assigned to ``EPS**(1/s)``, where EPS is
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machine epsilon for float64 numbers, s=2 for '2-point' method and s=3 for
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'3-point' method. Such relative step approximately minimizes a sum of
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truncation and round-off errors, see [1]_.
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A finite difference scheme for '3-point' method is selected automatically.
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The well-known central difference scheme is used for points sufficiently
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far from the boundary, and 3-point forward or backward scheme is used for
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points near the boundary. Both schemes have the second-order accuracy in
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terms of Taylor expansion. Refer to [2]_ for the formulas of 3-point
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forward and backward difference schemes.
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For dense differencing when m=1 Jacobian is returned with a shape (n,),
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on the other hand when n=1 Jacobian is returned with a shape (m, 1).
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Our motivation is the following: a) It handles a case of gradient
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computation (m=1) in a conventional way. b) It clearly separates these two
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different cases. b) In all cases np.atleast_2d can be called to get 2-d
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Jacobian with correct dimensions.
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References
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----------
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.. [1] W. H. Press et. al. "Numerical Recipes. The Art of Scientific
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Computing. 3rd edition", sec. 5.7.
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.. [2] A. Curtis, M. J. D. Powell, and J. Reid, "On the estimation of
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sparse Jacobian matrices", Journal of the Institute of Mathematics
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and its Applications, 13 (1974), pp. 117-120.
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.. [3] B. Fornberg, "Generation of Finite Difference Formulas on
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Arbitrarily Spaced Grids", Mathematics of Computation 51, 1988.
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Examples
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--------
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>>> import numpy as np
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>>> from scipy.optimize import approx_derivative
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>>>
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>>> def f(x, c1, c2):
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... return np.array([x[0] * np.sin(c1 * x[1]),
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... x[0] * np.cos(c2 * x[1])])
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...
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>>> x0 = np.array([1.0, 0.5 * np.pi])
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>>> approx_derivative(f, x0, args=(1, 2))
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array([[ 1., 0.],
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[-1., 0.]])
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Bounds can be used to limit the region of function evaluation.
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In the example below we compute left and right derivative at point 1.0.
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>>> def g(x):
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... return x**2 if x >= 1 else x
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...
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>>> x0 = 1.0
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>>> approx_derivative(g, x0, bounds=(-np.inf, 1.0))
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array([ 1.])
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>>> approx_derivative(g, x0, bounds=(1.0, np.inf))
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array([ 2.])
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"""
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if method not in ['2-point', '3-point', 'cs']:
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raise ValueError("Unknown method '%s'. " % method)
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x0 = np.atleast_1d(x0)
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if x0.ndim > 1:
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raise ValueError("`x0` must have at most 1 dimension.")
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lb, ub = _prepare_bounds(bounds, x0)
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if lb.shape != x0.shape or ub.shape != x0.shape:
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raise ValueError("Inconsistent shapes between bounds and `x0`.")
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if as_linear_operator and not (np.all(np.isinf(lb))
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and np.all(np.isinf(ub))):
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raise ValueError("Bounds not supported when "
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"`as_linear_operator` is True.")
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def fun_wrapped(x):
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f = np.atleast_1d(fun(x, *args, **kwargs))
|
||
|
|
if f.ndim > 1:
|
||
|
|
raise RuntimeError("`fun` return value has "
|
||
|
|
"more than 1 dimension.")
|
||
|
|
return f
|
||
|
|
|
||
|
|
if f0 is None:
|
||
|
|
f0 = fun_wrapped(x0)
|
||
|
|
else:
|
||
|
|
f0 = np.atleast_1d(f0)
|
||
|
|
if f0.ndim > 1:
|
||
|
|
raise ValueError("`f0` passed has more than 1 dimension.")
|
||
|
|
|
||
|
|
if np.any((x0 < lb) | (x0 > ub)):
|
||
|
|
raise ValueError("`x0` violates bound constraints.")
|
||
|
|
|
||
|
|
if as_linear_operator:
|
||
|
|
if rel_step is None:
|
||
|
|
rel_step = relative_step[method]
|
||
|
|
|
||
|
|
return _linear_operator_difference(fun_wrapped, x0,
|
||
|
|
f0, rel_step, method)
|
||
|
|
else:
|
||
|
|
h = _compute_absolute_step(rel_step, x0, method)
|
||
|
|
|
||
|
|
if method == '2-point':
|
||
|
|
h, use_one_sided = _adjust_scheme_to_bounds(
|
||
|
|
x0, h, 1, '1-sided', lb, ub)
|
||
|
|
elif method == '3-point':
|
||
|
|
h, use_one_sided = _adjust_scheme_to_bounds(
|
||
|
|
x0, h, 1, '2-sided', lb, ub)
|
||
|
|
elif method == 'cs':
|
||
|
|
use_one_sided = False
|
||
|
|
|
||
|
|
if sparsity is None:
|
||
|
|
return _dense_difference(fun_wrapped, x0, f0, h,
|
||
|
|
use_one_sided, method)
|
||
|
|
else:
|
||
|
|
if not issparse(sparsity) and len(sparsity) == 2:
|
||
|
|
structure, groups = sparsity
|
||
|
|
else:
|
||
|
|
structure = sparsity
|
||
|
|
groups = group_columns(sparsity)
|
||
|
|
|
||
|
|
if issparse(structure):
|
||
|
|
structure = csc_matrix(structure)
|
||
|
|
else:
|
||
|
|
structure = np.atleast_2d(structure)
|
||
|
|
|
||
|
|
groups = np.atleast_1d(groups)
|
||
|
|
return _sparse_difference(fun_wrapped, x0, f0, h,
|
||
|
|
use_one_sided, structure,
|
||
|
|
groups, method)
|
||
|
|
|
||
|
|
|
||
|
|
def _linear_operator_difference(fun, x0, f0, h, method):
|
||
|
|
m = f0.size
|
||
|
|
n = x0.size
|
||
|
|
|
||
|
|
if method == '2-point':
|
||
|
|
def matvec(p):
|
||
|
|
if np.array_equal(p, np.zeros_like(p)):
|
||
|
|
return np.zeros(m)
|
||
|
|
dx = h / norm(p)
|
||
|
|
x = x0 + dx*p
|
||
|
|
df = fun(x) - f0
|
||
|
|
return df / dx
|
||
|
|
|
||
|
|
elif method == '3-point':
|
||
|
|
def matvec(p):
|
||
|
|
if np.array_equal(p, np.zeros_like(p)):
|
||
|
|
return np.zeros(m)
|
||
|
|
dx = 2*h / norm(p)
|
||
|
|
x1 = x0 - (dx/2)*p
|
||
|
|
x2 = x0 + (dx/2)*p
|
||
|
|
f1 = fun(x1)
|
||
|
|
f2 = fun(x2)
|
||
|
|
df = f2 - f1
|
||
|
|
return df / dx
|
||
|
|
|
||
|
|
elif method == 'cs':
|
||
|
|
def matvec(p):
|
||
|
|
if np.array_equal(p, np.zeros_like(p)):
|
||
|
|
return np.zeros(m)
|
||
|
|
dx = h / norm(p)
|
||
|
|
x = x0 + dx*p*1.j
|
||
|
|
f1 = fun(x)
|
||
|
|
df = f1.imag
|
||
|
|
return df / dx
|
||
|
|
|
||
|
|
else:
|
||
|
|
raise RuntimeError("Never be here.")
|
||
|
|
|
||
|
|
return LinearOperator((m, n), matvec)
|
||
|
|
|
||
|
|
|
||
|
|
def _dense_difference(fun, x0, f0, h, use_one_sided, method):
|
||
|
|
m = f0.size
|
||
|
|
n = x0.size
|
||
|
|
J_transposed = np.empty((n, m))
|
||
|
|
h_vecs = np.diag(h)
|
||
|
|
|
||
|
|
for i in range(h.size):
|
||
|
|
if method == '2-point':
|
||
|
|
x = x0 + h_vecs[i]
|
||
|
|
dx = x[i] - x0[i] # Recompute dx as exactly representable number.
|
||
|
|
df = fun(x) - f0
|
||
|
|
elif method == '3-point' and use_one_sided[i]:
|
||
|
|
x1 = x0 + h_vecs[i]
|
||
|
|
x2 = x0 + 2 * h_vecs[i]
|
||
|
|
dx = x2[i] - x0[i]
|
||
|
|
f1 = fun(x1)
|
||
|
|
f2 = fun(x2)
|
||
|
|
df = -3.0 * f0 + 4 * f1 - f2
|
||
|
|
elif method == '3-point' and not use_one_sided[i]:
|
||
|
|
x1 = x0 - h_vecs[i]
|
||
|
|
x2 = x0 + h_vecs[i]
|
||
|
|
dx = x2[i] - x1[i]
|
||
|
|
f1 = fun(x1)
|
||
|
|
f2 = fun(x2)
|
||
|
|
df = f2 - f1
|
||
|
|
elif method == 'cs':
|
||
|
|
f1 = fun(x0 + h_vecs[i]*1.j)
|
||
|
|
df = f1.imag
|
||
|
|
dx = h_vecs[i, i]
|
||
|
|
else:
|
||
|
|
raise RuntimeError("Never be here.")
|
||
|
|
|
||
|
|
J_transposed[i] = df / dx
|
||
|
|
|
||
|
|
if m == 1:
|
||
|
|
J_transposed = np.ravel(J_transposed)
|
||
|
|
|
||
|
|
return J_transposed.T
|
||
|
|
|
||
|
|
|
||
|
|
def _sparse_difference(fun, x0, f0, h, use_one_sided,
|
||
|
|
structure, groups, method):
|
||
|
|
m = f0.size
|
||
|
|
n = x0.size
|
||
|
|
row_indices = []
|
||
|
|
col_indices = []
|
||
|
|
fractions = []
|
||
|
|
|
||
|
|
n_groups = np.max(groups) + 1
|
||
|
|
for group in range(n_groups):
|
||
|
|
# Perturb variables which are in the same group simultaneously.
|
||
|
|
e = np.equal(group, groups)
|
||
|
|
h_vec = h * e
|
||
|
|
if method == '2-point':
|
||
|
|
x = x0 + h_vec
|
||
|
|
dx = x - x0
|
||
|
|
df = fun(x) - f0
|
||
|
|
# The result is written to columns which correspond to perturbed
|
||
|
|
# variables.
|
||
|
|
cols, = np.nonzero(e)
|
||
|
|
# Find all non-zero elements in selected columns of Jacobian.
|
||
|
|
i, j, _ = find(structure[:, cols])
|
||
|
|
# Restore column indices in the full array.
|
||
|
|
j = cols[j]
|
||
|
|
elif method == '3-point':
|
||
|
|
# Here we do conceptually the same but separate one-sided
|
||
|
|
# and two-sided schemes.
|
||
|
|
x1 = x0.copy()
|
||
|
|
x2 = x0.copy()
|
||
|
|
|
||
|
|
mask_1 = use_one_sided & e
|
||
|
|
x1[mask_1] += h_vec[mask_1]
|
||
|
|
x2[mask_1] += 2 * h_vec[mask_1]
|
||
|
|
|
||
|
|
mask_2 = ~use_one_sided & e
|
||
|
|
x1[mask_2] -= h_vec[mask_2]
|
||
|
|
x2[mask_2] += h_vec[mask_2]
|
||
|
|
|
||
|
|
dx = np.zeros(n)
|
||
|
|
dx[mask_1] = x2[mask_1] - x0[mask_1]
|
||
|
|
dx[mask_2] = x2[mask_2] - x1[mask_2]
|
||
|
|
|
||
|
|
f1 = fun(x1)
|
||
|
|
f2 = fun(x2)
|
||
|
|
|
||
|
|
cols, = np.nonzero(e)
|
||
|
|
i, j, _ = find(structure[:, cols])
|
||
|
|
j = cols[j]
|
||
|
|
|
||
|
|
mask = use_one_sided[j]
|
||
|
|
df = np.empty(m)
|
||
|
|
|
||
|
|
rows = i[mask]
|
||
|
|
df[rows] = -3 * f0[rows] + 4 * f1[rows] - f2[rows]
|
||
|
|
|
||
|
|
rows = i[~mask]
|
||
|
|
df[rows] = f2[rows] - f1[rows]
|
||
|
|
elif method == 'cs':
|
||
|
|
f1 = fun(x0 + h_vec*1.j)
|
||
|
|
df = f1.imag
|
||
|
|
dx = h_vec
|
||
|
|
cols, = np.nonzero(e)
|
||
|
|
i, j, _ = find(structure[:, cols])
|
||
|
|
j = cols[j]
|
||
|
|
else:
|
||
|
|
raise ValueError("Never be here.")
|
||
|
|
|
||
|
|
# All that's left is to compute the fraction. We store i, j and
|
||
|
|
# fractions as separate arrays and later construct coo_matrix.
|
||
|
|
row_indices.append(i)
|
||
|
|
col_indices.append(j)
|
||
|
|
fractions.append(df[i] / dx[j])
|
||
|
|
|
||
|
|
row_indices = np.hstack(row_indices)
|
||
|
|
col_indices = np.hstack(col_indices)
|
||
|
|
fractions = np.hstack(fractions)
|
||
|
|
J = coo_matrix((fractions, (row_indices, col_indices)), shape=(m, n))
|
||
|
|
return csr_matrix(J)
|
||
|
|
|
||
|
|
|
||
|
|
def check_derivative(fun, jac, x0, bounds=(-np.inf, np.inf), args=(),
|
||
|
|
kwargs={}):
|
||
|
|
"""Check correctness of a function computing derivatives (Jacobian or
|
||
|
|
gradient) by comparison with a finite difference approximation.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
fun : callable
|
||
|
|
Function of which to estimate the derivatives. The argument x
|
||
|
|
passed to this function is ndarray of shape (n,) (never a scalar
|
||
|
|
even if n=1). It must return 1-d array_like of shape (m,) or a scalar.
|
||
|
|
jac : callable
|
||
|
|
Function which computes Jacobian matrix of `fun`. It must work with
|
||
|
|
argument x the same way as `fun`. The return value must be array_like
|
||
|
|
or sparse matrix with an appropriate shape.
|
||
|
|
x0 : array_like of shape (n,) or float
|
||
|
|
Point at which to estimate the derivatives. Float will be converted
|
||
|
|
to 1-d array.
|
||
|
|
bounds : 2-tuple of array_like, optional
|
||
|
|
Lower and upper bounds on independent variables. Defaults to no bounds.
|
||
|
|
Each bound must match the size of `x0` or be a scalar, in the latter
|
||
|
|
case the bound will be the same for all variables. Use it to limit the
|
||
|
|
range of function evaluation.
|
||
|
|
args, kwargs : tuple and dict, optional
|
||
|
|
Additional arguments passed to `fun` and `jac`. Both empty by default.
|
||
|
|
The calling signature is ``fun(x, *args, **kwargs)`` and the same
|
||
|
|
for `jac`.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
accuracy : float
|
||
|
|
The maximum among all relative errors for elements with absolute values
|
||
|
|
higher than 1 and absolute errors for elements with absolute values
|
||
|
|
less or equal than 1. If `accuracy` is on the order of 1e-6 or lower,
|
||
|
|
then it is likely that your `jac` implementation is correct.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
approx_derivative : Compute finite difference approximation of derivative.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> import numpy as np
|
||
|
|
>>> from scipy.optimize import check_derivative
|
||
|
|
>>>
|
||
|
|
>>>
|
||
|
|
>>> def f(x, c1, c2):
|
||
|
|
... return np.array([x[0] * np.sin(c1 * x[1]),
|
||
|
|
... x[0] * np.cos(c2 * x[1])])
|
||
|
|
...
|
||
|
|
>>> def jac(x, c1, c2):
|
||
|
|
... return np.array([
|
||
|
|
... [np.sin(c1 * x[1]), c1 * x[0] * np.cos(c1 * x[1])],
|
||
|
|
... [np.cos(c2 * x[1]), -c2 * x[0] * np.sin(c2 * x[1])]
|
||
|
|
... ])
|
||
|
|
...
|
||
|
|
>>>
|
||
|
|
>>> x0 = np.array([1.0, 0.5 * np.pi])
|
||
|
|
>>> check_derivative(f, jac, x0, args=(1, 2))
|
||
|
|
2.4492935982947064e-16
|
||
|
|
"""
|
||
|
|
J_to_test = jac(x0, *args, **kwargs)
|
||
|
|
if issparse(J_to_test):
|
||
|
|
J_diff = approx_derivative(fun, x0, bounds=bounds, sparsity=J_to_test,
|
||
|
|
args=args, kwargs=kwargs)
|
||
|
|
J_to_test = csr_matrix(J_to_test)
|
||
|
|
abs_err = J_to_test - J_diff
|
||
|
|
i, j, abs_err_data = find(abs_err)
|
||
|
|
J_diff_data = np.asarray(J_diff[i, j]).ravel()
|
||
|
|
return np.max(np.abs(abs_err_data) /
|
||
|
|
np.maximum(1, np.abs(J_diff_data)))
|
||
|
|
else:
|
||
|
|
J_diff = approx_derivative(fun, x0, bounds=bounds,
|
||
|
|
args=args, kwargs=kwargs)
|
||
|
|
abs_err = np.abs(J_to_test - J_diff)
|
||
|
|
return np.max(abs_err / np.maximum(1, np.abs(J_diff)))
|