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How to use test_unbounded method in hypothesis

Best Python code snippet using hypothesis

LinProgramming.py

Source:LinProgramming.py

...76            if self.test_optimal() == 1:77                print("you have reached the optimal Tabular")78                print(str(self.step) + " steps were taken")79                break80            if self.test_unbounded() == 1:81                print("This Problem Is Unbounded")82                print("step reached: " + str(self.step))83                break84            if self.test_infeasible() == 1:85                print("This problem is infeasible")86                print("step reached: " + str(self.step))87                break88        rhs_j = self.prog.tab0.shape[1] - 189        for i, rhs_i in zip(self.prog.B, range(self.prog.B.size)):90            self.solution[int(i)] = self.prog.tab0[rhs_i, rhs_j]91        self.objective = self.prog.tab0[self.prog.tab0.shape[0] - 1, self.prog.tab0.shape[1] - 1]92    def jordan_exchange(self):93        self.prog.get_pivot()94        self.prog.update_col()95        self.prog.update_row()96        self.prog.update_rest()97        self.prog.tab0 = copy.copy(self.prog.tab1)98        self.prog.tab1[0:-1, 0:-1] = 099        self.step += 1100    def test_unbounded(self):101        for i in range(self.prog.tab0.shape[1] - 1):102            if sum(v >= 0 for v in self.prog.tab0[:, i]) == self.prog.tab0.shape[0]:103                return 1104    def test_optimal(self):105        tab = self.prog.tab0106        obj_arr = tab[-1:, :][0, :]107        rhs_arr = tab[:, self.prog.n_V - 1]108        if sum(n > 0 for n in obj_arr[:-1]) == 0:109            if sum(n < 0 for n in rhs_arr) == 0:110                return 1111        else:112            return 0113    def test_infeasible(self):114        tab = self.prog.tab1...

test_exact.py

Source:test_exact.py

...8            [[0.5, 0.5], [1, 0.5], [0, 1]], dtype=np.float329        )10        self.y_train = np.array([1, 1, 0], dtype=np.int)11        self.x_eval = np.array([0, 0], dtype=np.float)12    def test_unbounded(self):13        solver = ExactSolver(14            self.X_train, self.y_train,15            QpSolverFactory().create('gcd'), 1, False16        )17        perturbation = solver(self.x_eval)18        self.assertTrue(19            np.allclose(20                perturbation,21                np.array([-0.25, 0.25], dtype=self.X_train.dtype)22            )23        )24        self.assertEqual(25            perturbation.dtype, self.X_train.dtype26        )...

test_box.py

Source:test_box.py

1import numpy as np2import psdr3def test_unbounded(m = 5):4	dom = psdr.BoxDomain(-np.inf*np.ones(m), np.inf*np.ones(m))5	assert dom.is_unbounded == True6	7	dom = psdr.BoxDomain(-np.ones(m), np.ones(m))8	assert dom.is_unbounded == False9def test_point(m = 5):10	dom = psdr.BoxDomain(np.ones(m), np.ones(m))11	assert dom.is_point == True12	13	dom = psdr.BoxDomain(-np.ones(m), np.ones(m))...

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