Hi,
This error is because of the difference between minfx-1.0.0 and the
repository version of minfx (https://gna.org/projects/minfx/). The
1.0.0 release uses the old Numeric package whereas the repository uses
numpy (both at http://numpy.scipy.org/). The 1.2 line uses Numeric as
well. Hence if you use the repository version of minfx with the 1.2
relax line, then you have the clash of Numeric vs. numpy and this
error. The 1.2 relax versions should use the internal code and not
minfx though, so this error message should not be of concern.
Cheers,
Edward
On Mon, Apr 7, 2008 at 11:46 PM, Sébastien Morin
<[EMAIL PROTECTED]> wrote:
>
> Hi again,
>
> I just ran a test with the minfx library instead of the "minimise"
> directory, even though the test-suite fails for the two last model-free
> tests.
>
> As expected, the script failed when it reached the minimise() function...
>
>
> ===========================
>
> relax> minimise(*args=('newton',), run='final', func_tol=1e-25,
> max_iterations=10000000, constraints=1, scaling=1, print_flag=1)
> Simulation 1
> Traceback (most recent call last):
> File "/home/semor/packages/relax-1.2/relax", line 454, in ?
> Relax()
> File "/home/semor/packages/relax-1.2/relax", line 166, in __init__
> self.interpreter.run()
> File "/home/semor/packages/relax-1.2/prompt/interpreter.py", line 216, in
> run
> run_script(intro=self.relax.intro_string, local=self.local,
> script_file=self.relax.script_file, quit=1)
> File "/home/semor/packages/relax-1.2/prompt/interpreter.py", line 391, in
> run_script
> console.interact(intro, local, script_file, quit)
> File "/home/semor/packages/relax-1.2/prompt/interpreter.py", line 343, in
> interact_script
> execfile(script_file, local)
> File "./full_analysis_final.py", line 587, in ?
> Main(self.relax)
> File "./full_analysis_final.py", line 313, in __init__
> minimise(MIN_ALGOR, run='final')
> File "/home/semor/packages/relax-1.2/prompt/minimisation.py", line 365,
> in minimise
> self.relax.generic.minimise.minimise(run=run, min_algor=min_algor,
> min_options=min_options, func_tol=func_tol, grad_tol=grad_tol,
> max_iterations=max_iterations, constraints=constraints, scaling=scaling,
> print_flag=print_flag)
> File "/home/semor/packages/relax-1.2/generic_fns/minimise.py", line 135,
> in minimise
> minimise(run=run, min_algor=min_algor, min_options=min_options,
> func_tol=func_tol, grad_tol=grad_tol, max_iterations=max_iterations,
> constraints=constraints, scaling=scaling, print_flag=print_flag-1,
> sim_index=i)
> File "/home/semor/packages/relax-1.2/specific_fns/model_free.py", line
> 2372, in minimise
> results = generic_minimise(func=self.mf.func, dfunc=self.mf.dfunc,
> d2func=self.mf.d2func, args=(), x0=self.param_vector, min_algor=min_algor,
> min_options=min_options, func_tol=func_tol, grad_tol=grad_tol,
> maxiter=max_iterations, A=A, b=b, full_output=1, print_flag=print_flag)
> File "/home/semor/packages/relax-1.3/minfx/generic.py", line 392, in
> generic_minimise
> results = method_of_multipliers(func=func, dfunc=dfunc, d2func=d2func,
> args=args, x0=x0, min_options=min_options, A=A, b=b, l=l, u=u, c=c, dc=dc,
> d2c=d2c, func_tol=func_tol, grad_tol=grad_tol, maxiter=maxiter,
> full_output=full_output, print_flag=print_flag)
> File "/home/semor/packages/relax-1.3/minfx/method_of_multipliers.py",
> line 143, in method_of_multipliers
> results = min.minimise()
> File "/home/semor/packages/relax-1.3/minfx/method_of_multipliers.py",
> line 397, in minimise
> results = self.generic_minimise(func=self.func_LA, dfunc=self.func_dLA,
> d2func=self.func_d2LA, args=self.args, x0=self.xk, min_algor=self.min_algor,
> min_options=self.min_options, func_tol=None, grad_tol=self.tk,
> maxiter=maxiter, full_output=1, print_flag=sub_print_flag,
> print_prefix="\t")
> File "/home/semor/packages/relax-1.3/minfx/generic.py", line 323, in
> generic_minimise
> results = newton(func=func, dfunc=dfunc, d2func=d2func, args=args,
> x0=x0, min_options=min_options, func_tol=func_tol, grad_tol=grad_tol,
> maxiter=maxiter, full_output=full_output, print_flag=print_flag,
> print_prefix=print_prefix)
> File "/home/semor/packages/relax-1.3/minfx/newton.py", line 44, in newton
> results = min.minimise()
> File "/home/semor/packages/relax-1.3/minfx/base_classes.py", line 233, in
> minimise
> self.new_param_func()
> File "/home/semor/packages/relax-1.3/minfx/newton.py", line 163, in
> new_param_func
> self.pk = self.get_pk()
> File "/home/semor/packages/relax-1.3/minfx/base_classes.py", line 642, in
> gmw
> return gmw(self.dfk, self.d2fk, self.I, self.n, self.mach_acc,
> self.print_prefix, self.print_flag, return_matrix)
> File "/home/semor/packages/relax-1.3/minfx/hessian_mods/gmw81.py", line
> 150, in gmw
> y = solve(L, dfk)
> File "/usr/lib/python2.4/site-packages/numpy/linalg/linalg.py", line 179,
> in solve
> t, result_t = _commonType(a, b)
> File "/usr/lib/python2.4/site-packages/numpy/linalg/linalg.py", line 71,
> in _commonType
> if issubclass(a.dtype.type, inexact):
> AttributeError: dtype
> ===========================
>
>
> I'm not familiar with the minfx package, but this error as probably nothing
> to do with the constraints problem...
>
> More to come... Stay tuned...
>
>
> Séb
>
>
>
>
>
> Sébastien Morin wrote:
>
> Hi Ed,
>
> I posted a bug report. As you will see, I found also the problem in normal
> optimization at the end of each round for the 4 diffusion models (sphere,
> prolate, oblate and ellipsoid)...
>
> Also, I used the normal minimise library. After reading your mail, I tried
> to use the minfx library as I do in the 1.3 line, but simply making a link
> wouldn't completely work (the test-suite failing for the two last model-free
> tests :
>
> ==========================
> Constrained Newton opt, GMW Hessian mod, backtracking line search
> {S2=0.970, te=2048, Rex=0.149} [ Failed ]
> Constrained Newton opt, GMW Hessian mod, More and Thuente line search
> {S2=0.970, te=2048, Rex=0.149} [ Failed ]
> ==========================
>
> Moreover, the --debug option wouldn't give more details concerning the
> incorrect supply of the constraints...
>
> I'll try to dig further...
>
> Cheers
>
>
> Séb
>
>
>
>
>
> Edward d'Auvergne wrote:
> Hi Seb,
>
> That's an interesting one. That message is a debugging statement that
> should only be triggered if someone goes into the relax source code
> and tries to reprogram the constraints algorithm or tries to implement
> a completely new algorithm. It shouldn't appear in the 1.2 stable
> releases and I haven't seen it since I first coded the Method of
> Multipliers constraint algorithm. Which version of relax are you
> using? Are you using the minfx library which was spun off from relax
> (https://gna.org/projects/minfx/)?
>
> I think, though, that you would be highly capable of being able to
> debug the problem yourself, probably using solely print statements.
> The message comes from the minimise/method_of_multipliers.py file. It
> should only occur if either of the constraint matrices A and b
> returned by the model-free linear_constraints() method are set to
> None. This method is the same for the normal optimisation and Monte
> Carlo simulations, so I can't see what the problem is myself (well,
> more information about the problem would be required). The model-free
> minimise() method also is run almost identically by both the normal
> optimisation and the simulations. So debugging this one will be very
> simple (for me I'd need to be able to reproduce the problem exactly
> before I could even start, something I'm currently incapable of
> doing). Something has caused one of these functions to go a bit
> crazy, and if this occurs in the 1.2 line, and we can come up with a
> fix, I'll then release a new version of relax. Could you make a bug
> report for this?
>
> Cheers,
>
> Edward
>
>
>
>
> On Mon, Apr 7, 2008 at 9:40 PM, Sébastien Morin
> <[EMAIL PROTECTED]> wrote:
>
>
> Hi,
>
> I am using the full_analysis.py script and got an intriguing warning in
> the "final" step. It seems there is a problem with the setting of the
> constraints for the minimise() function during monte carlo simulations...
>
>
> ==================
> relax> minimise(*args=('newton',), run='final', func_tol=1e-25,
> max_iterations=10000000, constraints=1, scaling=1, print_flag=1)
> Simulation 1
> The constraints have been incorrectly supplied.
> Simulation 2
> The constraints have been incorrectly supplied.
> ...
> ==================
>
>
> This problem arises for every single simulation.
>
> Is this a serious problem ? Will the simulated errors be erroneous or is
> this just a warning ?
>
> Does anyone have a clue about the reason for that ?
>
> Thanks !
>
>
> Sébastien :)
>
>
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>
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>
>
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