test_minimizers.py 2.42 KB
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# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
#
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# Copyright(C) 2013-2018 Max-Planck-Society
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#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.

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import unittest
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import numpy as np
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from numpy.testing import assert_allclose, assert_equal
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import nifty4 as ift
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from itertools import product
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from test.common import expand
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from nose.plugins.skip import SkipTest
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spaces = [ift.RGSpace([1024], distances=0.123), ift.HPSpace(32)]
minimizers = [ift.SteepestDescent, ift.RelaxedNewton, ift.VL_BFGS,
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              ift.ConjugateGradient, ift.NonlinearCG,
              ift.NewtonCG, ift.L_BFGS_B]
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class Test_Minimizers(unittest.TestCase):
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    @expand(product(minimizers, spaces))
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    def test_quadratic_minimization(self, minimizer_class, space):
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        np.random.seed(42)
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        starting_point = ift.Field.from_random('normal', domain=space)*10
        covariance_diagonal = ift.Field.from_random(
                                  'uniform', domain=space) + 0.5
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        covariance = ift.DiagonalOperator(covariance_diagonal)
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        required_result = ift.Field.ones(space, dtype=np.float64)
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        IC = ift.GradientNormController(tol_abs_gradnorm=1e-5,
                                        iteration_limit=1000)
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        try:
            minimizer = minimizer_class(controller=IC)
            energy = ift.QuadraticEnergy(A=covariance, b=required_result,
                                         position=starting_point)
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            (energy, convergence) = minimizer(energy)
        except NotImplementedError:
            raise SkipTest

        assert_equal(convergence, IC.CONVERGED)
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        assert_allclose(energy.position.to_global_data(),
                        1./covariance_diagonal.to_global_data(),
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                        rtol=1e-3, atol=1e-3)
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# MR FIXME: add Rosenbrock test