|
26 | 26 | class Arbitrary(BaselineFunction): |
27 | 27 | """Methods for evaluating a baseline from an arbitrary function. |
28 | 28 |
|
29 | | - Supports baseline calculations with arbitrary functions. These functions, |
30 | | - if implemented, must have the following signatures and return values: |
31 | | - valuef(pars, x) ==> numpy.array of length x if x is a sequence |
32 | | - ==> number if x is a number |
33 | | - jacobianf(pars, x, free) ==> list, each element a numpy.array of length x if |
34 | | - x is a sequence or None if value of free for |
35 | | - that parameter is False. |
36 | | - ==> list, each element a number if x is a number |
37 | | - or None if value of free for that parameter is |
38 | | - False |
39 | | - estimatef(x, y) ==> numpy.array of length npars |
| 29 | + Supports baseline calculations with arbitrary functions. These |
| 30 | + functions, if implemented, must have the following signatures and |
| 31 | + return values: valuef(pars, x) ==> numpy.array of length x |
| 32 | + if x is a sequence ==> number if x is a |
| 33 | + number jacobianf(pars, x, free) ==> list, each element a numpy.array |
| 34 | + of length x if x is a sequence or None |
| 35 | + if value of free for that parameter is |
| 36 | + False. ==> list, each element a number if x |
| 37 | + is a number or None if value of free for |
| 38 | + that parameter is False estimatef(x, y) |
| 39 | + ==> numpy.array of length npars |
40 | 40 | """ |
41 | 41 |
|
42 | 42 | def __init__(self, npars, valuef, jacobianf=None, estimatef=None, Cache=None): |
|
0 commit comments