I'm happy to announce the first official release of ALGOPY in version 0.2.1.
Rationale: ~~~~~~~~ The purpose of ALGOPY is the evaluation of higher-order derivatives in the forward and reverse mode of Algorithmic Differentiation (AD) using univariate Taylor polynomial arithmetic. Particular focus are functions that contain numerical linear algebra functions (e.g. inv, dot, eigh, qr, cholesky,...) as they often appear in statistically motivated functions. Download: ~~~~~~~~~ http://pypi.python.org/pypi/algopy/0.2.1 or bleeding edge versions from from http://github.com/b45ch1/algopy Documentation: ~~~~~~~~~~~~ available at http://packages.python.org/algopy/ OS Support: ~~~~~~~~~~ Linux, Windows (tested with pythonxy), should also work on Mac Software Dependencies: ~~~~~~~~~~~~~~~~~~~~ for the core: numpy, scipy for testing: nose Exampe Session: ~~~~~~~~~~~~~ Consider the contrived example where it is the goal to compute the directional derivative df/dx_1 : >>> import numpy; from numpy import log, exp, sin, cos >>> import algopy; from algopy import UTPM, dot, inv, zeros >>> >>> def f(x): ... A = zeros((2,2),dtype=x) ... A[0,0] = numpy.log(x[0]*x[1]) ... A[0,1] = numpy.log(x[1]) + exp(x[0]) ... A[1,0] = sin(x[1])**2 + cos(x[0])**3.1 ... A[1,1] = x[0]**cos(x[1]) ... return log( dot(x.T, dot( inv(A), x))) ... >>> >>> x = UTPM(zeros((2,1,2),dtype=float)) >>> x.data[0,0] = [1,2] >>> x.data[1,0] = [1,0] >>> y = f(x) >>> >>> print 'normal function evaluation f(x) = ',y.data[0,0] normal function evaluation f(x) = 0.641250189986 >>> print 'directional derivative df/dx1 = ',y.data[1,0] directional derivative df/dx1 = 1.62982340133 _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
