How to use test_converged method in molecule

Best Python code snippet using molecule_python

test_criteria.py

Source:test_criteria.py

...56        # change tolerance57        t.tolerance[x] = 1e-558        self.assertAlmostEqual(t.tolerance[x], 1e-5)59        self.assertAlmostEqual(t.tolerance.default, 1e-8)60    def test_converged(self):61        """Test converged method."""62        x = relentless.variable.DesignVariable(value=3.0)63        q = QuadraticObjective(x=x)64        t = relentless.optimize.GradientTest(tolerance=1e-8, variables=x)65        self.assertFalse(t.converged(result=q.compute(x)))66        x.value = 0.99999999967        self.assertTrue(t.converged(result=q.compute(x)))68        # test at high69        x.value = -2.070        x.high = 2.071        self.assertFalse(t.converged(result=q.compute(x)))72        x.value = 0.073        x.high = 0.074        self.assertTrue(t.converged(result=q.compute(x)))75        # test at low76        x.high = None77        x.value = 0.078        x.low = 0.079        self.assertFalse(t.converged(result=q.compute(x)))80        x.value = 2.081        x.low = 2.082        self.assertTrue(t.converged(result=q.compute(x)))83class test_ValueTest(unittest.TestCase):84    """Unit tests for relentless.optimize.ValueTest"""85    def test_init(self):86        """Test creation with data."""87        x = relentless.variable.DesignVariable(value=3.0)88        # test default values89        t = relentless.optimize.ValueTest(value=2.5)90        self.assertAlmostEqual(t.absolute, 1e-8)91        self.assertAlmostEqual(t.relative, 1e-5)92        self.assertAlmostEqual(t.value, 2.5)93        # non-default values94        t = relentless.optimize.ValueTest(absolute=1e-7, relative=1e-4, value=3.0)95        self.assertAlmostEqual(t.absolute, 1e-7)96        self.assertAlmostEqual(t.relative, 1e-4)97        self.assertAlmostEqual(t.value, 3.0)98        # change parameters99        t.absolute = 1e-9100        t.relative = 1e-5101        t.value = 1.5102        self.assertAlmostEqual(t.absolute, 1e-9)103        self.assertAlmostEqual(t.relative, 1e-5)104        self.assertAlmostEqual(t.value, 1.5)105    def test_converged(self):106        """Test converged method."""107        x = relentless.variable.DesignVariable(value=3.0)108        q = QuadraticObjective(x=x)109        t = relentless.optimize.ValueTest(absolute=0.2, relative=0.2, value=1.0)110        self.assertFalse(t.converged(result=q.compute(x)))111        x.value = 1.999999999112        self.assertTrue(t.converged(result=q.compute(x)))113        t = relentless.optimize.ValueTest(value=9.0)114        self.assertFalse(t.converged(result=q.compute(x)))115        x.value = 3.9999999999116        self.assertTrue(t.converged(result=q.compute(x)))117class AnyTest(unittest.TestCase):118    """Unit tests for relentless.optimize.AnyTest"""119    def test_init(self):120        """Test creation with data."""121        x = relentless.variable.DesignVariable(value=3.0)122        t1 = relentless.optimize.GradientTest(tolerance=1e-8, variables=x)123        t2 = relentless.optimize.ValueTest(value=2.0)124        t3 = relentless.optimize.ValueTest(value=1.0)125        t = relentless.optimize.AnyTest(t1,t2,t3)126        self.assertCountEqual(t.tests, (t1,t2,t3))127    def test_converged(self):128        """Test converged method."""129        x = relentless.variable.DesignVariable(value=3.0)130        q = QuadraticObjective(x=x)131        t1 = relentless.optimize.GradientTest(tolerance=1e-8, variables=x)132        t2 = relentless.optimize.ValueTest(value=1.0)133        t3 = relentless.optimize.ValueTest(value=0.0)134        t = relentless.optimize.AnyTest(t1,t2,t3)135        self.assertFalse(t.converged(result=q.compute(x)))136        x.value = 2.00000001137        self.assertTrue(t.converged(result=q.compute(x)))138        x.value = 1.00000001139        self.assertTrue(t.converged(result=q.compute(x)))140class AllTest(unittest.TestCase):141    """Unit tests for relentless.optimize.AllTest"""142    def test_init(self):143        """Test creation with data."""144        x = relentless.variable.DesignVariable(value=3.0)145        t1 = relentless.optimize.GradientTest(tolerance=1e-8, variables=x)146        t2 = relentless.optimize.ValueTest(value=1.0)147        t3 = relentless.optimize.ValueTest(value=0.0)148        t = relentless.optimize.AllTest(t1,t2,t3)149        self.assertCountEqual(t.tests, (t1,t2,t3))150    def test_converged(self):151        """Test converged method."""152        x = relentless.variable.DesignVariable(value=3.0)153        q = QuadraticObjective(x=x)154        t1 = relentless.optimize.GradientTest(tolerance=1e-8, variables=x)155        t2 = relentless.optimize.ValueTest(value=1.0)156        t3 = relentless.optimize.ValueTest(value=0.0)157        t = relentless.optimize.AllTest(t1,t2,t3)158        self.assertFalse(t.converged(result=q.compute(x)))159        x.value = 1.999999999160        self.assertFalse(t.converged(result=q.compute(x)))161        t2.value = 0.0162        x.value = 0.999999999163        self.assertTrue(t.converged(result=q.compute(x)))164class OrTest(unittest.TestCase):165    """Unit tests for relentless.optimize.OrTest"""166    def test_init(self):167        """Test creation with data."""168        x = relentless.variable.DesignVariable(value=3.0)169        t1 = relentless.optimize.GradientTest(tolerance=1e-8, variables=x)170        t2 = relentless.optimize.ValueTest(value=1.0)171        t = relentless.optimize.OrTest(t1,t2)172        self.assertCountEqual(t.tests, (t1,t2))173    def test_converged(self):174        """Test converged method."""175        x = relentless.variable.DesignVariable(value=3.0)176        q = QuadraticObjective(x=x)177        t1 = relentless.optimize.GradientTest(tolerance=1e-8, variables=x)178        t2 = relentless.optimize.ValueTest(value=1.0)179        t = relentless.optimize.OrTest(t1,t2)180        self.assertFalse(t.converged(result=q.compute(x)))181        x.value = 1.9999999999182        self.assertTrue(t.converged(result=q.compute(x)))183        x.value = 0.9999999999184        self.assertTrue(t.converged(result=q.compute(x)))185class AndTest(unittest.TestCase):186    """Unit tests for relentless.optimize.AndTest"""187    def test_init(self):188        """Test creation with data."""189        x = relentless.variable.DesignVariable(value=3.0)190        t1 = relentless.optimize.GradientTest(tolerance=1e-8, variables=x)191        t2 = relentless.optimize.ValueTest(value=1.0)192        t = relentless.optimize.AndTest(t1,t2)193        self.assertCountEqual(t.tests, (t1,t2))194    def test_converged(self):195        """Test converged method."""196        x = relentless.variable.DesignVariable(value=3.0)197        q = QuadraticObjective(x=x)198        t1 = relentless.optimize.GradientTest(tolerance=1e-8, variables=x)199        t2 = relentless.optimize.ValueTest(value=1.0)200        t = relentless.optimize.AndTest(t1,t2)201        self.assertFalse(t.converged(result=q.compute(x)))202        x.value = 1.999999999203        self.assertFalse(t.converged(result=q.compute(x)))204        t2.value = 0.0205        x.value = 0.999999999206        self.assertTrue(t.converged(result=q.compute(x)))207if __name__ == '__main__':208    unittest.main()

dynamic_programming.py

Source:dynamic_programming.py

...73                continue74            x = [i,j]75            PI[i,j] = greedy_policy_improvement(x, V, gamma, cost_fn)76    return PI77def test_converged(arg1, arg2):78    """79    Computes the maximum norm between arg1 and arg2 and80    checks whether the result is smaller than the global variable delta.81    """82    return np.max(np.abs(arg1-arg2)) < c.delta83def Value_Iteration(V, gamma, cost_fn):84    """85    Value iteration algorithm. Stops after convergence of max_steps.86    """87    max_steps = 100000088    for i in range(max_steps):89        V_prev = V90        V = value_iteration_step(V, gamma, cost_fn)91        if test_converged(V, V_prev):92            return V93    else:94        print(f"failed to converge after {max_steps} steps.")95        96def Policy_Evaluation(V, PI, gamma, cost_fn, eval_steps = None):97    """98    Policy evaluation used for the policy iteration. It stops afer eval_step.99    If no value for eval_steps is given, Policy_Evaluation runs until100    convergence or until max_steps is reached.101    """102    if eval_steps != None:103        max_steps = eval_steps104    else:105        max_steps = 1000000106        107    if eval_steps != None:108        for i in range(max_steps):109            V_prev = V110            V = policy_evaluation_step(V, PI, gamma, cost_fn)111            if test_converged(V, V_prev):112                return V113        else: return V114        115    else:116        for i in range(max_steps):117            V_prev = V118            V = policy_evaluation_step(V, PI, gamma, cost_fn)119            if test_converged(V, V_prev):120                return V121        else:122            print(f"failed to converge after {max_steps} steps.")123    124def Policy_Iteration(V, PI, gamma, cost_fn, eval_steps = None):125    """126    This function computes the Policy_Iteration of no value for eval_steps is given.127    If eval_steps is given, it computes the optistic policy128    iteration with that many policy evaluation steps.129    """130    max_steps = 1000000131    for i in range(max_steps):132        V_prev = V133        V = Policy_Evaluation(V, PI, gamma, cost_fn, eval_steps)134        PI_prev = PI135        PI = policy_improvement_step(V, gamma, cost_fn, PI)136        if test_converged(V, V_prev):137            return PI, V138    else:...

matrix_test.py

Source:matrix_test.py

...38        result_matrix = [[2, 1, 0],39                         [1, 2, 3],40                         [1, 1, 2]]41        self.assertEqual(add_self_loops(test_matrix, 2), result_matrix)42    def test_converged(self):43        test_matrix = [[0.03, 1, 0],44                       [1, 0, 3],45                       [0.0008, 1, 1]]46        self.assertEqual(converged(test_matrix, test_matrix), True)47if __name__ == '__main__':...

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