add fixed parameters test

test/test_x_fixed.py

@@ -0,0 +1,61 @@
+"""
+This is for testing the fixing of parameters feature.
+"""
+
+import unittest
+import numpy as np
+import pypesto
+from .test_objective import get_objective_rosen_separated
+
+
+class XFixedTest(unittest.TestCase):
+
+    def test_problem(self):
+        problem = create_problem()
+
+        self.assertEqual(len(problem.lb), 3)
+        self.assertEqual(problem.dim, 3)
+        self.assertEqual(problem.dim_full, 5)
+        self.assertTrue(np.array_equal(problem.x_free_indices, [0, 2, 4]))
+
+    def test_optimize(self):
+        problem = create_problem()
+        optimizer = pypesto.ScipyOptimizer()
+        n_starts = 5
+        result = pypesto.minimize(problem, optimizer, n_starts)
+
+        optimizer_result = result.optimize_result.list[0]
+        self.assertEqual(len(optimizer_result.x), 5)
+        self.assertEqual(len(optimizer_result.grad), 5)
+
+        # maybe not what we want, but that's how it is right now
+        self.assertEqual(len(problem.ub), 3)
+
+        # nans written into unknown components
+        self.assertTrue(np.isnan(optimizer_result.grad[1]))
+
+        # fixed values written into parameter vector
+        self.assertEqual(optimizer_result.x[1], 1)
+
+        lb_full = problem.get_full_vector(problem.lb)
+        self.assertEqual(len(lb_full), 5)
+
+
+def create_problem():
+    objective = get_objective_rosen_separated()
+    lb = [-3, -3, -3, -3, -3]
+    ub = [3, 3, 3, 3, 3]
+    x_fixed_indices = [1, 3]
+    x_fixed_vals = [1, 1]
+    problem = pypesto.Problem(objective=objective,
+                              lb=lb, ub=ub,
+                              x_fixed_indices=x_fixed_indices,
+                              x_fixed_vals=x_fixed_vals)
+
+    return problem
+
+
+if __name__ == '__main__':
+    suite = unittest.TestSuite()
+    suite.addTest(XFixedTest())
+    unittest.main()