How to use _optimize method in avocado

Best Python code snippet using avocado_python

test_optimizers.py

Source:test_optimizers.py

...20    def setUp(self):21        super().setUp()22        np.random.seed(50)23        pass24    def _optimize(self, optimizer):25        x0 = [1.3, 0.7, 0.8, 1.9, 1.2]26        res = optimizer.optimize(len(x0), rosen, initial_point=x0)27        np.testing.assert_array_almost_equal(res[0], [1.0] * len(x0), decimal=2)28        return res29    def test_adam(self):30        optimizer = ADAM(maxiter=10000, tol=1e-06)31        res = self._optimize(optimizer)32        self.assertLessEqual(res[2], 10000)33    def test_cg(self):34        optimizer = CG(maxiter=1000, tol=1e-06)35        res = self._optimize(optimizer)36        self.assertLessEqual(res[2], 10000)37    def test_cobyla(self):38        optimizer = COBYLA(maxiter=100000, tol=1e-06)39        res = self._optimize(optimizer)40        self.assertLessEqual(res[2], 100000)41    def test_l_bfgs_b(self):42        optimizer = L_BFGS_B(maxfun=1000)43        res = self._optimize(optimizer)44        self.assertLessEqual(res[2], 10000)45    def test_nelder_mead(self):46        optimizer = NELDER_MEAD(maxfev=10000, tol=1e-06)47        res = self._optimize(optimizer)48        self.assertLessEqual(res[2], 10000)49    def test_powell(self):50        optimizer = POWELL(maxfev=10000, tol=1e-06)51        res = self._optimize(optimizer)52        self.assertLessEqual(res[2], 10000)53    def test_slsqp(self):54        optimizer = SLSQP(maxiter=1000, tol=1e-06)55        res = self._optimize(optimizer)56        self.assertLessEqual(res[2], 10000)57    @unittest.skip("Skipping SPSA as it does not do well on non-convex rozen")58    def test_spsa(self):59        optimizer = SPSA(max_trials=10000)60        res = self._optimize(optimizer)61        self.assertLessEqual(res[2], 100000)62    def test_tnc(self):63        optimizer = TNC(maxiter=1000, tol=1e-06)64        res = self._optimize(optimizer)65        self.assertLessEqual(res[2], 10000)66if __name__ == '__main__':...

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