Continuo da qui, nell’esame di altre risorse.
Theano
Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray
). Using Theano it is possible to attain speeds rivaling hand-crafted C implementations for problems involving large amounts of data. It can also surpass C on a CPU by many orders of magnitude by taking advantage of recent GPUs.
Installato via Conda ma mi da errori (di versione?). Da mettere tra le cose da esaminare in futuro (davvero, prossimamente… forse 😊).
SciPy
SciPy is a collection of mathematical algorithms and convenience functions built on the Numpy extension of Python. It adds significant power to the interactive Python session by providing the user with high-level commands and classes for manipulating and visualizing data. With SciPy an interactive Python session becomes a data-processing and system-prototyping environment rivaling systems such as MATLAB, IDL, Octave, R-Lab, and SciLab.
#esempio minimo do SciPy
import numpy as np
from scipy import linalg, optimize
np.info(optimize.fmin)
Produce questo file:
fmin(func, x0, args=(), xtol=0.0001, ftol=0.0001, maxiter=None, maxfun=None,
full_output=0, disp=1, retall=0, callback=None, initial_simplex=None)
Minimize a function using the downhill simplex algorithm.
This algorithm only uses function values, not derivatives or second
derivatives.
Parameters
----------
func : callable func(x,*args)
The objective function to be minimized.
x0 : ndarray
Initial guess.
args : tuple, optional
Extra arguments passed to func, i.e. ``f(x,*args)``.
xtol : float, optional
Absolute error in xopt between iterations that is acceptable for
convergence.
ftol : number, optional
Absolute error in func(xopt) between iterations that is acceptable for
convergence.
maxiter : int, optional
Maximum number of iterations to perform.
maxfun : number, optional
Maximum number of function evaluations to make.
full_output : bool, optional
Set to True if fopt and warnflag outputs are desired.
disp : bool, optional
Set to True to print convergence messages.
retall : bool, optional
Set to True to return list of solutions at each iteration.
callback : callable, optional
Called after each iteration, as callback(xk), where xk is the
current parameter vector.
initial_simplex : array_like of shape (N + 1, N), optional
Initial simplex. If given, overrides `x0`.
``initial_simplex[j,:]`` should contain the coordinates of
the j-th vertex of the ``N+1`` vertices in the simplex, where
``N`` is the dimension.
Returns
-------
xopt : ndarray
Parameter that minimizes function.
fopt : float
Value of function at minimum: ``fopt = func(xopt)``.
iter : int
Number of iterations performed.
funcalls : int
Number of function calls made.
warnflag : int
1 : Maximum number of function evaluations made.
2 : Maximum number of iterations reached.
allvecs : list
Solution at each iteration.
See also
--------
minimize: Interface to minimization algorithms for multivariate
functions. See the 'Nelder-Mead' `method` in particular.
Notes
-----
Uses a Nelder-Mead simplex algorithm to find the minimum of function of
one or more variables.
This algorithm has a long history of successful use in applications.
But it will usually be slower than an algorithm that uses first or
second derivative information. In practice it can have poor
performance in high-dimensional problems and is not robust to
minimizing complicated functions. Additionally, there currently is no
complete theory describing when the algorithm will successfully
converge to the minimum, or how fast it will if it does. Both the ftol and
xtol criteria must be met for convergence.
References
----------
.. [1] Nelder, J.A. and Mead, R. (1965), "A simplex method for function
minimization", The Computer Journal, 7, pp. 308-313
.. [2] Wright, M.H. (1996), "Direct Search Methods: Once Scorned, Now
Respectable", in Numerical Analysis 1995, Proceedings of the
1995 Dundee Biennial Conference in Numerical Analysis, D.F.
Griffiths and G.A. Watson (Eds.), Addison Wesley Longman,
Harlow, UK, pp. 191-208.
Anche questo da esaminare in dettaglio prossimamente 😊
Le risorse disponibili per SciPy sono infinite tantissime. Roba da Ok, panico! fin da prima di cominciare 😯
Ma mi serve un attimo di riflessione, riorganizzare le idee. E ci sono anche altre cose che mi stanno tentando… 😯