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Commit 8fcb0150 authored by Theo Steininger's avatar Theo Steininger
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Fixed minimizers and their tests.

parent 668c15f7
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    nifty/minimization/descent_minimizer.py
    View file @ 8fcb0150
    ......@@ -156,24 +156,20 @@ class DescentMinimizer(Loggable, object):
    pk=descend_direction,
    f_k_minus_1=f_k_minus_1)
    f_k_minus_1 = energy.value
    # check if new energy value is bigger than old energy value
    if (new_energy.value - energy.value) > 0:
    self.logger.info("Line search algorithm was unsuccessful. "
    "Stopping.")
    new_energy = energy.at( position=energy.position +
    np.random.rand()/10*(new_energy.position-energy.position) )
    energy = new_energy
    convergence = self.convergence_level + 2
    self.logger.info("Line search algorithm returned a new energy "
    "that was larger than the old one. Stopping.")
    break
    energy = new_energy
    # check convergence
    delta = abs(gradient).max() * (step_length/gradient_norm)
    self.logger.debug("Iteration : %08u step_length = %3.1E "
    "delta = %3.1E" %
    (iteration_number, step_length, delta))
    self.logger.debug("Iteration:%08u step_length=%3.1E "
    "delta=%3.1E energy=%3.1E" %
    (iteration_number, step_length, delta,
    energy.value))
    if delta == 0:
    convergence = self.convergence_level + 2
    self.logger.info("Found minimum according to line-search. "
    ......
    nifty/minimization/steepest_descent.py
    View file @ 8fcb0150
    ......@@ -40,8 +40,4 @@ class SteepestDescent(DescentMinimizer):
    """
    descend_direction = energy.gradient
    norm = descend_direction.norm()
    if norm != 1:
    return descend_direction / -norm
    else:
    return descend_direction * -1
    return descend_direction * -1
    nifty/minimization/vl_bfgs.py
    View file @ 8fcb0150
    ......@@ -25,7 +25,7 @@ from .line_searching import LineSearchStrongWolfe
    class VL_BFGS(DescentMinimizer):
    def __init__(self, line_searcher=LineSearchStrongWolfe(), callback=None,
    convergence_tolerance=1E-4, convergence_level=3,
    iteration_limit=None, max_history_length=10):
    iteration_limit=None, max_history_length=5):
    super(VL_BFGS, self).__init__(
    line_searcher=line_searcher,
    ......@@ -84,9 +84,6 @@ class VL_BFGS(DescentMinimizer):
    for i in xrange(1, len(delta)):
    descend_direction += delta[i] * b[i]
    norm = descend_direction.norm()
    if norm != 1:
    descend_direction /= norm
    return descend_direction
    ......
    test/test_minimization/quadratic_potential.py 0 → 100644
    View file @ 8fcb0150
    # -*- coding: utf-8 -*-
    from nifty import Energy
    class QuadraticPotential(Energy):
    def __init__(self, position, eigenvalues):
    super(QuadraticPotential, self).__init__(position)
    self.eigenvalues = eigenvalues
    def at(self, position):
    return self.__class__(position,
    eigenvalues=self.eigenvalues)
    @property
    def value(self):
    H = 0.5 * self.position.vdot(
    self.eigenvalues(self.position))
    return H.real
    @property
    def gradient(self):
    g = self.eigenvalues(self.position)
    return g
    @property
    def curvature(self):
    return self.eigenvalues
    test/test_minimization/test_conjugate_gradient.py
    View file @ 8fcb0150
    import unittest
    import numpy as np
    from numpy.testing import assert_equal, assert_almost_equal
    from nifty import *
    from nifty import Field, DiagonalOperator, RGSpace, HPSpace
    from nifty import ConjugateGradient
    from itertools import product
    from test.common import expand
    from test.common import generate_spaces
    np.random.seed(42)
    class ConjugateGradient_Tests(unittest.TestCase):
    spaces = generate_spaces()
    @expand(product(spaces, [10, 100, 1000], [1E-3, 1E-4, 1E-5], [2, 3, 4] ))
    def test_property(self, space, iteration_limit, convergence_tolerance,
    convergence_level):
    x0 = Field.from_random('normal', domain=space)
    A = DiagonalOperator(space, diagonal = 1.)
    b = Field(space, val=0.)
    minimizer = ConjugateGradient(iteration_limit=iteration_limit,
    convergence_tolerance=convergence_tolerance,
    convergence_level=convergence_level)
    (position, convergence) = minimizer(A=A, x0=x0, b=b)
    if position.domain[0] != space:
    raise TypeError
    if type(convergence) != int:
    raise TypeError
    @expand(product(spaces, [10, 100, 1000], [1E-3, 1E-4, 1E-5], [2, 3, 4] ))
    def test_property(self, space, iteration_limit, convergence_tolerance,
    convergence_level):
    x0 = Field.from_random('normal', domain=space)
    test_x = Field(space, val = 1.)
    A = DiagonalOperator(space, diagonal = 1.)
    b = Field(space, val=1.)
    minimizer = ConjugateGradient(iteration_limit=iteration_limit,
    convergence_tolerance=convergence_tolerance,
    convergence_level=convergence_level)
    (position, convergence) = minimizer(A=A, x0=x0, b=b)
    assert_almost_equal(position.val.get_full_data(),
    test_x.val.get_full_data(), decimal=3)
    assert_equal(convergence, convergence_level+1)
    spaces = [RGSpace([1024, 1024], distances=0.123), HPSpace(32)]
    class Test_ConjugateGradient(unittest.TestCase):
    def test_interface(self):
    iteration_limit = 100
    convergence_level = 4
    convergence_tolerance = 1E-6
    callback = lambda z: z
    minimizer = ConjugateGradient(
    iteration_limit=iteration_limit,
    convergence_tolerance=convergence_tolerance,
    convergence_level=convergence_level,
    callback=callback)
    assert_equal(minimizer.iteration_limit, iteration_limit)
    assert_equal(minimizer.convergence_level, convergence_level)
    assert_equal(minimizer.convergence_tolerance, convergence_tolerance)
    assert(minimizer.callback is callback)
    @expand([[space] for space in spaces])
    def test_minimization(self, space):
    np.random.seed(42)
    starting_point = Field.from_random('normal', domain=space)*10
    covariance_diagonal = Field.from_random('uniform', domain=space) + 0.5
    covariance = DiagonalOperator(space, diagonal=covariance_diagonal)
    required_result = Field(space, val=1.)
    minimizer = ConjugateGradient()
    (position, convergence) = minimizer(A=covariance, x0=starting_point,
    b=required_result)
    assert_almost_equal(position.val.get_full_data(),
    1./covariance_diagonal.val.get_full_data(),
    decimal=3)
    test/test_minimization/test_descent_minimizers.py 0 → 100644
    View file @ 8fcb0150
    import unittest
    import numpy as np
    from numpy.testing import assert_equal, assert_almost_equal
    from nifty import Field, DiagonalOperator, RGSpace, HPSpace
    from nifty import SteepestDescent, RelaxedNewton, VL_BFGS
    from itertools import product
    from test.common import expand
    from quadratic_potential import QuadraticPotential
    from nifty import logger
    minimizers = [SteepestDescent, RelaxedNewton, VL_BFGS]
    spaces = [RGSpace([1024, 1024], distances=0.123), HPSpace(32)]
    class Test_DescentMinimizers(unittest.TestCase):
    @expand([[minimizer] for minimizer in minimizers])
    def test_interface(self, minimizer):
    iteration_limit = 100
    convergence_level = 4
    convergence_tolerance = 1E-6
    callback = lambda z: z
    minimizer = minimizer(iteration_limit=iteration_limit,
    convergence_tolerance=convergence_tolerance,
    convergence_level=convergence_level,
    callback=callback)
    assert_equal(minimizer.iteration_limit, iteration_limit)
    assert_equal(minimizer.convergence_level, convergence_level)
    assert_equal(minimizer.convergence_tolerance, convergence_tolerance)
    assert(minimizer.callback is callback)
    @expand(product(minimizers, spaces))
    def test_minimization(self, minimizer_class, space):
    np.random.seed(42)
    starting_point = Field.from_random('normal', domain=space)*10
    covariance_diagonal = Field.from_random('uniform', domain=space) + 0.5
    covariance = DiagonalOperator(space, diagonal=covariance_diagonal)
    energy = QuadraticPotential(position=starting_point,
    eigenvalues=covariance)
    minimizer = minimizer_class(iteration_limit=30)
    (energy, convergence) = minimizer(energy)
    assert_almost_equal(energy.value, 0, decimal=5)
    assert_almost_equal(energy.position.val.get_full_data(), 0., decimal=5)
    test/test_minimization/test_relaxed_newton.py deleted 100644 → 0
    View file @ 668c15f7
    import unittest
    from numpy.testing import assert_equal, assert_almost_equal
    from nifty import *
    from itertools import product
    from test.common import expand
    from test.common import generate_spaces
    np.random.seed(42)
    class QuadraticPot(Energy):
    def __init__(self, position, N):
    super(QuadraticPot, self).__init__(position)
    self.N = N
    def at(self, position):
    return self.__class__(position, N = self.N)
    @property
    def value(self):
    H = 0.5 *self.position.dot(self.N.inverse_times(self.position))
    return H.real
    @property
    def gradient(self):
    g = self.N.inverse_times(self.position)
    return_g = g.copy_empty(dtype=np.float)
    return_g.val = g.val.real
    return return_g
    @property
    def curvature(self):
    return self.N
    class RelaxedNewton_Tests(unittest.TestCase):
    spaces = generate_spaces()
    @expand(product(spaces, [10, 100, 1000], [1E-3, 1E-4, 1E-5], [2, 3, 4] ))
    def test_property(self, space, iteration_limit, convergence_tolerance,
    convergence_level):
    x = Field.from_random('normal', domain=space)
    N = DiagonalOperator(space, diagonal = 1.)
    energy = QuadraticPot(position=x , N=N)
    minimizer = RelaxedNewton(iteration_limit=iteration_limit,
    convergence_tolerance=convergence_tolerance,
    convergence_level=convergence_level)
    (energy, convergence) = minimizer(energy)
    if energy.position.domain[0] != space:
    raise TypeError
    if type(convergence) != int:
    raise TypeError
    @expand(product(spaces, [10, 100, 1000], [1E-3, 1E-4, 1E-5], [2, 3, 4] ))
    def test_property(self, space, iteration_limit, convergence_tolerance,
    convergence_level):
    x = Field.from_random('normal', domain=space)
    test_x = Field(space, val = 0.)
    N = DiagonalOperator(space, diagonal = 1.)
    energy = QuadraticPot(position=x , N=N)
    minimizer = RelaxedNewton(iteration_limit=iteration_limit,
    convergence_tolerance=convergence_tolerance,
    convergence_level=convergence_level)
    (energy, convergence) = minimizer(energy)
    assert_almost_equal(energy.value, 0, decimal=3)
    assert_almost_equal(energy.position.val.get_full_data(),
    test_x.val.get_full_data(), decimal=3)
    assert_equal(convergence, convergence_level+2)
    test/test_minimization/test_steepest_descent.py deleted 100644 → 0
    View file @ 668c15f7
    import unittest
    from numpy.testing import assert_equal, assert_almost_equal
    from nifty import *
    from itertools import product
    from test.common import expand
    from test.common import generate_spaces
    np.random.seed(42)
    class QuadraticPot(Energy):
    def __init__(self, position, N):
    super(QuadraticPot, self).__init__(position)
    self.N = N
    def at(self, position):
    return self.__class__(position, N = self.N)
    @property
    def value(self):
    H = 0.5 *self.position.dot(self.N.inverse_times(self.position))
    return H.real
    @property
    def gradient(self):
    g = self.N.inverse_times(self.position)
    return_g = g.copy_empty(dtype=np.float)
    return_g.val = g.val.real
    return return_g
    @property
    def curvature(self):
    return self.N
    class SteepestDescent_Tests(unittest.TestCase):
    spaces = generate_spaces()
    @expand(product(spaces, [10, 100, 1000], [1E-3, 1E-4, 1E-5], [2, 3, 4] ))
    def test_property(self, space, iteration_limit, convergence_tolerance,
    convergence_level):
    x = Field.from_random('normal', domain=space)
    N = DiagonalOperator(space, diagonal = 1.)
    energy = QuadraticPot(position=x , N=N)
    minimizer = SteepestDescent(iteration_limit=iteration_limit,
    convergence_tolerance=convergence_tolerance,
    convergence_level=convergence_level)
    (energy, convergence) = minimizer(energy)
    if energy.position.domain[0] != space:
    raise TypeError
    if type(convergence) != int:
    raise TypeError
    @expand(product(spaces, [10, 100, 1000], [1E-3, 1E-4, 1E-5], [2, 3, 4] ))
    def test_property(self, space, iteration_limit, convergence_tolerance,
    convergence_level):
    x = Field.from_random('normal', domain=space)
    test_x = Field(space, val = 0.)
    N = DiagonalOperator(space, diagonal = 1.)
    energy = QuadraticPot(position=x , N=N)
    minimizer = SteepestDescent(iteration_limit=iteration_limit,
    convergence_tolerance=convergence_tolerance,
    convergence_level=convergence_level)
    (energy, convergence) = minimizer(energy)
    assert_almost_equal(energy.value, 0, decimal=3)
    assert_almost_equal(energy.position.val.get_full_data(),
    test_x.val.get_full_data(), decimal=3)
    assert_equal(convergence, convergence_level+2)
    test/test_minimization/test_vl_bfgs.py deleted 100644 → 0
    View file @ 668c15f7
    import unittest
    from numpy.testing import assert_equal, assert_almost_equal
    from nifty import *
    from itertools import product
    from test.common import expand
    from test.common import generate_spaces
    np.random.seed(42)
    class QuadraticPot(Energy):
    def __init__(self, position, N):
    super(QuadraticPot, self).__init__(position)
    self.N = N
    def at(self, position):
    return self.__class__(position, N = self.N)
    @property
    def value(self):
    H = 0.5 *self.position.dot(self.N.inverse_times(self.position))
    return H.real
    @property
    def gradient(self):
    g = self.N.inverse_times(self.position)
    return_g = g.copy_empty(dtype=np.float)
    return_g.val = g.val.real
    return return_g
    @property
    def curvature(self):
    return self.N
    class VL_BFGS_Tests(unittest.TestCase):
    spaces = generate_spaces()
    @expand(product(spaces, [10, 100, 1000], [1E-3, 1E-4, 1E-5], [2, 3, 4] ))
    def test_property(self, space, iteration_limit, convergence_tolerance,
    convergence_level):
    x = Field.from_random('normal', domain=space)
    N = DiagonalOperator(space, diagonal = 1.)
    energy = QuadraticPot(position=x , N=N)
    minimizer = VL_BFGS(iteration_limit=iteration_limit,
    convergence_tolerance=convergence_tolerance,
    convergence_level=convergence_level)
    (energy, convergence) = minimizer(energy)
    if energy.position.domain[0] != space:
    raise TypeError
    if type(convergence) != int:
    raise TypeError
    @expand(product(spaces, [10, 100, 1000], [1E-3, 1E-4, 1E-5], [2, 3, 4] ))
    def test_property(self, space, iteration_limit, convergence_tolerance,
    convergence_level):
    x = Field.from_random('normal', domain=space)
    test_x = Field(space, val = 0.)
    N = DiagonalOperator(space, diagonal = 1.)
    energy = QuadraticPot(position=x , N=N)
    minimizer = VL_BFGS(iteration_limit=iteration_limit,
    convergence_tolerance=convergence_tolerance,
    convergence_level=convergence_level)
    (energy, convergence) = minimizer(energy)
    assert_almost_equal(energy.value, 0, decimal=3)
    assert_almost_equal(energy.position.val.get_full_data(),
    test_x.val.get_full_data(), decimal=3)
    assert_equal(convergence, convergence_level+2)
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