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… 😯