def test_single_variable_quadratic(self): for initial_step in [0.001, 0.01, 10]: obj, param, optimum = problems.build_single_variable_quadratic() opt = AdaptiveGradientDescent(obj, param, initial_step) opt.max_iters = 60 opt.optimize() np.testing.assert_almost_equal(opt.param.to_vector(), optimum)
def test_single_variable_quadratic(self): for method in self.methods: obj, param, optimum = problems.build_single_variable_quadratic() opt = ScipyOptimizer(obj, param, method) opt.optimize() np.testing.assert_almost_equal(opt.param.to_vector(), optimum, decimal=4)
def test_single_variable_quadratic(self): obj, param, optimum = problems.build_single_variable_quadratic() opt = GradientDescent(obj, param, 0.05) opt.max_iters = 500 opt.optimize() np.testing.assert_almost_equal(opt.param.encode(), optimum)
def test_single_variable_quadratic(self): obj, param, optimum = problems.build_single_variable_quadratic() opt = Nag(obj, param, 0.1) opt.max_iters = 60 opt.optimize() np.testing.assert_almost_equal(opt.param.encode(), optimum, decimal=4)
def test_single_variable_quadratic(self): obj, param, optimum = problems.build_single_variable_quadratic() opt = Adagrad(obj, param, 1) opt.max_iters = 40 opt.optimize() np.testing.assert_almost_equal(opt.param.encode(), optimum)