Python DifferentialEvolutionSolver._scale_parameters方法代码示例

本文整理汇总了Python中scipy.optimize._differentialevolution.DifferentialEvolutionSolver._scale_parameters方法的典型用法代码示例。如果您正苦于以下问题：Python DifferentialEvolutionSolver._scale_parameters方法的具体用法？Python DifferentialEvolutionSolver._scale_parameters怎么用？Python DifferentialEvolutionSolver._scale_parameters使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类scipy.optimize._differentialevolution.DifferentialEvolutionSolver的用法示例。

在下文中一共展示了DifferentialEvolutionSolver._scale_parameters方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: TestDifferentialEvolutionSolver

# 需要导入模块: from scipy.optimize._differentialevolution import DifferentialEvolutionSolver [as 别名]
# 或者: from scipy.optimize._differentialevolution.DifferentialEvolutionSolver import _scale_parameters [as 别名]

#.........这里部分代码省略.........

        assert_equal(solver.dither, list(mutation))

    def test_invalid_mutation_values_arent_accepted(self):
        func = rosen
        mutation = (0.5, 3)
        assert_raises(ValueError,
                          DifferentialEvolutionSolver,
                          func,
                          self.bounds,
                          mutation=mutation)

        mutation = (-1, 1)
        assert_raises(ValueError,
                          DifferentialEvolutionSolver,
                          func,
                          self.bounds,
                          mutation=mutation)

        mutation = (0.1, np.nan)
        assert_raises(ValueError,
                          DifferentialEvolutionSolver,
                          func,
                          self.bounds,
                          mutation=mutation)

        mutation = 0.5
        solver = DifferentialEvolutionSolver(func,
                                             self.bounds,
                                             mutation=mutation)
        assert_equal(0.5, solver.scale)
        assert_equal(None, solver.dither)

    def test__scale_parameters(self):
        trial = np.array([0.3])
        assert_equal(30, self.dummy_solver._scale_parameters(trial))

        # it should also work with the limits reversed
        self.dummy_solver.limits = np.array([[100], [0.]])
        assert_equal(30, self.dummy_solver._scale_parameters(trial))

    def test__unscale_parameters(self):
        trial = np.array([30])
        assert_equal(0.3, self.dummy_solver._unscale_parameters(trial))

        # it should also work with the limits reversed
        self.dummy_solver.limits = np.array([[100], [0.]])
        assert_equal(0.3, self.dummy_solver._unscale_parameters(trial))

    def test__ensure_constraint(self):
        trial = np.array([1.1, -100, 0.9, 2., 300., -0.00001])
        self.dummy_solver._ensure_constraint(trial)

        assert_equal(trial[2], 0.9)
        assert_(np.logical_and(trial >= 0, trial <= 1).all())

    def test_differential_evolution(self):
        # test that the Jmin of DifferentialEvolutionSolver
        # is the same as the function evaluation
        solver = DifferentialEvolutionSolver(self.quadratic, [(-2, 2)])
        result = solver.solve()
        assert_almost_equal(result.fun, self.quadratic(result.x))

    def test_best_solution_retrieval(self):
        # test that the getter property method for the best solution works.
        solver = DifferentialEvolutionSolver(self.quadratic, [(-2, 2)])

开发者ID:toddrjen，项目名称:scipy，代码行数:70，代码来源:test__differential_evolution.py

示例2: TestDifferentialEvolutionSolver

# 需要导入模块: from scipy.optimize._differentialevolution import DifferentialEvolutionSolver [as 别名]
# 或者: from scipy.optimize._differentialevolution.DifferentialEvolutionSolver import _scale_parameters [as 别名]

#.........这里部分代码省略.........

        self.assertEqual(solver.dither, list(mutation))

    def test_invalid_mutation_values_arent_accepted(self):
        func = rosen
        mutation = (0.5, 3)
        self.assertRaises(ValueError,
                          DifferentialEvolutionSolver,
                          func,
                          self.bounds,
                          mutation=mutation)

        mutation = (-1, 1)
        self.assertRaises(ValueError,
                          DifferentialEvolutionSolver,
                          func,
                          self.bounds,
                          mutation=mutation)

        mutation = (0.1, np.nan)
        self.assertRaises(ValueError,
                          DifferentialEvolutionSolver,
                          func,
                          self.bounds,
                          mutation=mutation)

        mutation = (0.5)
        solver = DifferentialEvolutionSolver(func,
                                             self.bounds,
                                             mutation=mutation)
        assert_equal(0.5, solver.scale)
        assert_equal(None, solver.dither)

    def test__scale_parameters(self):
        trial = np.array([0.3])
        assert_equal(30, self.dummy_solver._scale_parameters(trial))

        # it should also work with the limits reversed
        self.dummy_solver.limits = np.array([[100], [0.]])
        assert_equal(30, self.dummy_solver._scale_parameters(trial))

    def test__unscale_parameters(self):
        trial = np.array([30])
        assert_equal(0.3, self.dummy_solver._unscale_parameters(trial))

        # it should also work with the limits reversed
        self.dummy_solver.limits = np.array([[100], [0.]])
        assert_equal(0.3, self.dummy_solver._unscale_parameters(trial))

    def test__ensure_constraint(self):
        trial = np.array([1.1, -100, 2., 300., -0.00001])
        self.dummy_solver._ensure_constraint(trial)
        assert_equal(np.all(trial <= 1), True)

    def test_differential_evolution(self):
        # test that the Jmin of DifferentialEvolutionSolver
        # is the same as the function evaluation
        solver = DifferentialEvolutionSolver(self.quadratic, [(-2, 2)])
        result = solver.solve()
        assert_almost_equal(result.fun, self.quadratic(result.x))

    def test_best_solution_retrieval(self):
        # test that the getter property method for the best solution works.
        solver = DifferentialEvolutionSolver(self.quadratic, [(-2, 2)])
        result = solver.solve()
        assert_equal(result.x, solver.x)

开发者ID:AI-Org，项目名称:scipy，代码行数:70，代码来源:test__differential_evolution.py

示例3: TestDifferentialEvolutionSolver

# 需要导入模块: from scipy.optimize._differentialevolution import DifferentialEvolutionSolver [as 别名]
# 或者: from scipy.optimize._differentialevolution.DifferentialEvolutionSolver import _scale_parameters [as 别名]

#.........这里部分代码省略.........

        assert_equal(solver.dither, list(mutation))

    def test_invalid_mutation_values_arent_accepted(self):
        func = rosen
        mutation = (0.5, 3)
        assert_raises(ValueError,
                          DifferentialEvolutionSolver,
                          func,
                          self.bounds,
                          mutation=mutation)

        mutation = (-1, 1)
        assert_raises(ValueError,
                          DifferentialEvolutionSolver,
                          func,
                          self.bounds,
                          mutation=mutation)

        mutation = (0.1, np.nan)
        assert_raises(ValueError,
                          DifferentialEvolutionSolver,
                          func,
                          self.bounds,
                          mutation=mutation)

        mutation = 0.5
        solver = DifferentialEvolutionSolver(func,
                                             self.bounds,
                                             mutation=mutation)
        assert_equal(0.5, solver.scale)
        assert_equal(None, solver.dither)

    def test__scale_parameters(self):
        trial = np.array([0.3])
        assert_equal(30, self.dummy_solver._scale_parameters(trial))

        # it should also work with the limits reversed
        self.dummy_solver.limits = np.array([[100], [0.]])
        assert_equal(30, self.dummy_solver._scale_parameters(trial))

    def test__unscale_parameters(self):
        trial = np.array([30])
        assert_equal(0.3, self.dummy_solver._unscale_parameters(trial))

        # it should also work with the limits reversed
        self.dummy_solver.limits = np.array([[100], [0.]])
        assert_equal(0.3, self.dummy_solver._unscale_parameters(trial))

    def test__ensure_constraint(self):
        trial = np.array([1.1, -100, 2., 300., -0.00001])
        self.dummy_solver._ensure_constraint(trial)
        assert_equal(np.all(trial <= 1), True)

    def test_differential_evolution(self):
        # test that the Jmin of DifferentialEvolutionSolver
        # is the same as the function evaluation
        solver = DifferentialEvolutionSolver(self.quadratic, [(-2, 2)])
        result = solver.solve()
        assert_almost_equal(result.fun, self.quadratic(result.x))

    def test_best_solution_retrieval(self):
        # test that the getter property method for the best solution works.
        solver = DifferentialEvolutionSolver(self.quadratic, [(-2, 2)])
        result = solver.solve()
        assert_almost_equal(result.x, solver.x)

开发者ID:alpaco42，项目名称:ML_Spring_2018，代码行数:70，代码来源:test__differential_evolution.py

示例4: TestDifferentialEvolutionSolver

# 需要导入模块: from scipy.optimize._differentialevolution import DifferentialEvolutionSolver [as 别名]
# 或者: from scipy.optimize._differentialevolution.DifferentialEvolutionSolver import _scale_parameters [as 别名]

#.........这里部分代码省略.........

        assert_equal(solver.dither, list(mutation))

    def test_invalid_mutation_values_arent_accepted(self):
        func = rosen
        mutation = (0.5, 3)
        assert_raises(ValueError,
                          DifferentialEvolutionSolver,
                          func,
                          self.bounds,
                          mutation=mutation)

        mutation = (-1, 1)
        assert_raises(ValueError,
                          DifferentialEvolutionSolver,
                          func,
                          self.bounds,
                          mutation=mutation)

        mutation = (0.1, np.nan)
        assert_raises(ValueError,
                          DifferentialEvolutionSolver,
                          func,
                          self.bounds,
                          mutation=mutation)

        mutation = 0.5
        solver = DifferentialEvolutionSolver(func,
                                             self.bounds,
                                             mutation=mutation)
        assert_equal(0.5, solver.scale)
        assert_equal(None, solver.dither)

    def test__scale_parameters(self):
        trial = np.array([0.3])
        assert_equal(30, self.dummy_solver._scale_parameters(trial))

        # it should also work with the limits reversed
        self.dummy_solver.limits = np.array([[100], [0.]])
        assert_equal(30, self.dummy_solver._scale_parameters(trial))

    def test__unscale_parameters(self):
        trial = np.array([30])
        assert_equal(0.3, self.dummy_solver._unscale_parameters(trial))

        # it should also work with the limits reversed
        self.dummy_solver.limits = np.array([[100], [0.]])
        assert_equal(0.3, self.dummy_solver._unscale_parameters(trial))

    def test__ensure_constraint(self):
        trial = np.array([1.1, -100, 0.9, 2., 300., -0.00001])
        self.dummy_solver._ensure_constraint(trial)

        assert_equal(trial[2], 0.9)
        assert_(np.logical_and(trial >= 0, trial <= 1).all())

    def test_differential_evolution(self):
        # test that the Jmin of DifferentialEvolutionSolver
        # is the same as the function evaluation
        solver = DifferentialEvolutionSolver(self.quadratic, [(-2, 2)])
        result = solver.solve()
        assert_almost_equal(result.fun, self.quadratic(result.x))

    def test_best_solution_retrieval(self):
        # test that the getter property method for the best solution works.
        solver = DifferentialEvolutionSolver(self.quadratic, [(-2, 2)])

开发者ID:anntzer，项目名称:scipy，代码行数:70，代码来源:test__differential_evolution.py

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