J-jqdZddlmZddlmZddlmZ[[[dZdZ dZ ddl Z ddl Zd d lmZ ddlZd d lmZmZmZmZmZmZmZmZmZmZd Zd d lmZd dlm Z d dlm!Z!m"Z"d dlm#Z#m$Z$m%Z%m&Z&m'Z'm(Z(d dlm)Z)d dl*m+Z+m,Z,e)Z-d dl.m/Z/m0Z0m1Z1m2Z2d dlm3Z3d dl4m5Z5m6Z6m7Z7m8Z8d dlm9Z9m:Z:e6e_6 e jzdddde j}ddj}dDZ?eBd!e j}ddj}dDZ?y#e;$rejxdYwxYw#e@$r~ e jzddde j}ddj}dDZ?n;#eA$r3eBd e j}ddj}dDZ?YnwxYwYwxYw)"ad Package `cma` implements the CMA-ES (Covariance Matrix Adaptation Evolution Strategy). CMA-ES is a stochastic optimizer for robust non-linear non-convex derivative- and function-value-free numerical optimization. This release was tested with Python versions 3.8 to 3.13. The implementation is intended to be compatible with Python >= 2.7. CMA-ES searches for a minimizer (a solution x in :math:`R^n`) of an objective function f (cost function), such that f(x) is minimal. Regarding f, only a passably reliable ranking of the candidate solutions in each iteration is necessary. Neither the function values themselves, nor the gradient of f need to be available or do matter, like in the downhill simplex Nelder-Mead algorithm. Some termination criteria however depend on actual Delta f-values. The `cma` module provides two independent implementations of the CMA-ES algorithm in the classes `cma.CMAEvolutionStrategy` and `cma.purecma.CMAES`. In each implementation two interfaces are provided: - functions `fmin2` and `purecma.fmin`: run a complete minimization of the passed objective function with CMA-ES. `fmin2` also provides optional restarts and noise handling. - class `CMAEvolutionStrategy` (and the alias `CMA`) and `purecma.CMAES`: allow for minimization such that the control of the iteration loop remains with the user. `fmin2` returns an instance of `CMAEvolutionStrategy`. Additionally, `fmin_con2` provides constrained optimization. For a quick start see below or confer to the notebook(s) https://github.com/CMA-ES/pycma/blob/development/notebooks/notebook-usecases-basics.ipynb `CMAEvolutionStrategy` relies, in contrast to `cma.purecma`, heavily on `numpy` and optionally on `matplotlib.pyplot` (for `plot` etc., optional but highly recommended). The source code is available at https://github.com/CMA-ES/pycma. Testing ======= From the system shell:: python -m cma.test -h python -m cma.test python -c "import cma.test; cma.test.main()" # the same or from any (i)python shell:: import cma.test cma.test.main() should run without complaints in about between 20 and 100 seconds. Example ======= From a python shell:: import cma help(cma) # "this" help message, use cma? in ipython help(cma.fmin) help(cma.CMAEvolutionStrategy) help(cma.CMAOptions) cma.CMAOptions('tol') # display 'tolerance' termination options cma.CMAOptions('verb') # display verbosity options x, es = cma.fmin2(cma.ff.tablet, 15 * [1], 1) es = cma.CMAEvolutionStrategy(15 * [1], 1).optimize(cma.ff.tablet) help(es.result) x, es.result[0] # best evaluated solution es.result[5] # mean solution, presumably better with noise :See also: `fmin` (), `CMAOptions`, `CMAEvolutionStrategy` :Author: Nikolaus Hansen, 2008- :Author: Petr Baudis, 2014 :Author: Youhei Akimoto, 2017- :License: BSD 3-Clause, see LICENSE file. )absolute_import)division)print_functionz2Nikolaus Hansen and Petr Baudis and Youhei Akimotoz BSD 3-clausez4.4.4N)purecma) constraints_handlerevolution_strategyfitness_functionsfitness_transformations interfacesoptimization_toolssamplersigma_adaptationtransformations utilitiesz;type "import cma.test" to access the `test` module of `cma`)s)ff) GlueArgumentsScaleCoordinates)fminfmin2fmin_con fmin_con2 fmin_lq_surr fmin_lq_surr2)CMAEvolutionStrategy) CMAOptionscma_default_options_)dispplotplot_zip CMADataLogger) NoiseHandler) BoundPenaltyBoundTransform BoundNoneBoundDomainTransform)ConstrainedFitnessALAugmentedLagrangianz{Only `cma.purecma` has been imported. Install `numpy` ("pip install numpy") if you want to import the entire `cma` package. version_info)majorminormicrocma)modulec#2K|]}t|ywNint.0rs Z/Users/jameslopez/projects/TradingBot25/.venv/lib/python3.12/site-packages/cma/__init__.py r7sK&Jc!f&J .c#2K|]}t|ywr1r2r4s r6r7r7sO*NQ#a&*Nr8c#2K|]}t|ywr1r2r4s r6r7r7sR-QSV-Qr8c#2K|]}t|ywr1r2r4s r6r7r7sJ%ISV%Ir8)C__doc__ __future__r_abr_dir_pr ___author__ __license__ __version__ collections _collectionswarnings _warningsmore_algorithmsrnumpy_nprr r r r r rrrrtestrrrrrrrrrrroptions_parametersrrCMAloggerrr r!r"r#boundary_handlerr$r%r&r'r(r) ImportErrorwarn namedtuplesplitr* TypeError Exceptiontupler6r\sR@.&,cC   #%8  ID%Haa8D C;;0__N)7& SG*<** 7GKk&7&7&%D%DS%IJJ I INN  8SS=.|.. ;=O+*;*;C*@*C*I*I#*NOQ  SR[->->s-CA-F-L-LS-QRR S SsHD.AD<D98D9<F?>FF?5F96F?8F99F?>F?