1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
|
Files in this directory demonstrate basic use of mystic. The most common
case demonstrated is fitting a standard test function from mystic.models,
however the use of constraints, the use of ensemble solvers, and the use
of parallel computing is also demonstrated.
== Notes on mystic examples ==
Dependencies:
- Several of the examples require matplotlib to be installed.
- For the examples that use matplotlib, see trac ticket #36 for more details.
Other dependencies:
- Examples with prefix "example" are part of the tutorial (TRY THESE FIRST).
- All examples with prefix "example" should run without new dependencies.
- All examples with prefix "test_" should run without new dependencies.
- All examples with prefix "gplot_" requre gnuplot-py for visualization.
Exceptions to the rule:
- The following examples also require scipy to be installed:
. test_lorentzian.py
. test_mogi_anneal.py,
Special examples:
- All examples with prefix "rosetta_" require park to be installed. (tests on version park-1.2). Run with "--park" to execute with park. See "--help" for more options.
-------------------------------------------------------------------------------
Notes on the "ffit" tests/examples:
- test_ffit: The fitting problem whose exact solution is 8th order Chebyshev polynomial of the first kind. This example uses a Ctrl-C signal handler. Try ctrl-c as the differential_evolution strategy is running.
- test_ffit2: The fitting problem whose exact solution is 16th order Chebyshev polynomial of the first kind. Also uses the signal_handler.
- test_ffitB: Same as test_ffit.py, but uses DifferentialEvolutionSolver2 instead of DifferentialEvolutionSolver.
- test_ffitC: Same as test_ffit.py, but uses scipy_optimize.fmin.
- test_ffitD: Same as test_ffit.py, but uses scipy_optimize.diffev.
Notes on the "mogi" tests/examples:
- test_mogi.py: One mogi source with noise, comparison between DE and Conjugate Gradient, Simplex, and least squares (Levenberg Marquardt). CG / lsq don't work very well. lsq should work when bounds on the parameters are given, but minpack (wrapped by scipy) version doesn't seem to support bounds.
- test_mogi_anneal.py: tests with scipy simulated annealing, but hasn't been tuned, so again, doesn't work at all.
- test_mogi2.py: two mogi sources
- test_mogi3.py: reimplements test_mogi
# end of file
|
