1 """
2 Helper functions
3
4 Fitting functions :
5 - Quadratic defines a quadratic cost
6
7 NB : the first dimension of the cost, gradient or hessian is the number of
8 points to fit, the second is the dimension of the point if there is one.
9 This leads to the fact that the gradient returns in fact the jacobian of
10 the function.
11
12 Finite Difference functions :
13 - ForwardFiniteDifferences
14 - CenteredFiniteDifferences
15 - also, versions with in-built caching of previous values
16 """
17
18 from quadratic import *
19
20 from finite_difference import *
21
22 helpers__all__ = ['Quadratic', 'FiniteDifferencesFunction',
23 'ForwardFiniteDifferences',
24 'CenteredFiniteDifferences', 'ForwardFiniteDifferencesCache']
25
26 __all__ = helpers__all__
27