[SciPy-dev] New coding style (docstrings) : question
dmitrey
openopt at ukr.net
Mon Aug 6 13:10:31 EDT 2007
Hi all,
I try to rewrite scipy.optimize docstrings (as well as openopt ones) in
new docstrings standard.
(it was assigned to my GSoC milestones)
So please take a look at the example below - is all correct?
Especially I'm interested in func handlers - are they need any type
describer?
(see those 1 line above and some lines below of line
x0 : ndarray -- the initial guess
)
Regards, D.
def fmin(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None,
full_output=0, disp=1, retall=0, callback=None):
"""Minimize a function using the downhill simplex algorithm.
:Parameters:
func -- the Python function or method to be minimized.
x0 : ndarray -- the initial guess.
args -- extra arguments for func.
callback -- an optional user-supplied function to call after each
iteration. It is called as callback(xk), where xk is the
current parameter vector.
:Returns: (xopt, {fopt, iter, funcalls, warnflag})
xopt : ndarray -- minimizer of function
fopt : number -- value of function at minimum: fopt = func(xopt)
iter : number -- number of iterations
funcalls : number-- number of function calls
warnflag : number -- Integer warning flag:
1 : 'Maximum number of function evaluations.'
2 : 'Maximum number of iterations.'
allvecs : Python list -- a list of solutions at each iteration
:OtherParameters:
xtol : number -- acceptable relative error in xopt for convergence.
ftol : number -- acceptable relative error in func(xopt) for
convergence.
maxiter : number -- the maximum number of iterations to perform.
maxfun : number -- the maximum number of function evaluations.
full_output : number -- non-zero if fval and warnflag outputs are
desired.
disp : number -- non-zero to print convergence messages.
retall : number -- non-zero to return list of solutions at each
iteration
:SeeAlso:
fmin, fmin_powell, fmin_cg,
fmin_bfgs, fmin_ncg -- multivariate local optimizers
leastsq -- nonlinear least squares minimizer
fmin_l_bfgs_b, fmin_tnc,
fmin_cobyla -- constrained multivariate optimizers
anneal, brute -- global optimizers
fminbound, brent, golden, bracket -- local scalar minimizers
fsolve -- n-dimenstional root-finding
brentq, brenth, ridder, bisect, newton -- one-dimensional root-finding
fixed_point -- scalar fixed-point finder
Notes
-----
Uses a Nelder-Mead simplex algorithm to find the minimum of function
of one or more variables.
"""
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