Exemple #1
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 def check_simple_marquardt(self):
   startPoint = numpy.zeros(2, numpy.float)
   optimi = optimizer.StandardOptimizer(function = Quadratic(), step = step.MarquardtStep(), criterion = criterion.criterion(gtol=0.00001, iterations_max=200), x0 = startPoint, line_search = line_search.BacktrackingSearch())
   opt = optimi.optimize()
   assert_almost_equal(optimi.optimize(), numpy.array([1, 3], dtype = numpy.float), decimal = 5)
Exemple #2
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 def check_swpr_dygradient(self):
   startPoint = numpy.empty(2, numpy.float)
   startPoint[0] = -1.01
   startPoint[-1] = 1.01
   optimi = optimizer.StandardOptimizer(function = Rosenbrock(2), step = step.DYConjugateGradientStep(), criterion = criterion.criterion(iterations_max = 1000, ftol = 0.00000001, gtol = 0.0001), x0 = startPoint, line_search = line_search.StrongWolfePowellRule())
   assert_array_almost_equal(optimi.optimize(), numpy.ones(2, numpy.float), decimal = 4)
Exemple #3
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 def check_swp_dypgradient_relative(self):
   startPoint = numpy.zeros(2, numpy.float)
   optimi = optimizer.StandardOptimizer(function = Quadratic(), step = step.DYConjugateGradientStep(), criterion = criterion.criterion(ftol=0.000001, iterations_max=1000, gtol = 0.0001), x0 = startPoint, line_search = line_search.StrongWolfePowellRule())
   assert_almost_equal(optimi.optimize(), numpy.array([1, 3], dtype = numpy.float))