added tests from cvoptimizer

alphamind/tests/cython/test_optimizers.py

@@ -9,6 +9,7 @@ import unittest
 import numpy as np
 from alphamind.cython.optimizers import LPOptimizer
 from alphamind.cython.optimizers import QPOptimizer
+from alphamind.cython.optimizers import CVOptimizer
 
 
 class TestOptimizers(unittest.TestCase):
@@ -69,6 +70,90 @@ class TestOptimizers(unittest.TestCase):
                                              [-0.02, 0.01, 0.03],
                                              8)
 
+    def test_cvoptimizer_without_cons(self):
+        objective = np.array([0.1, 0.2, 0.3])
+        cov = np.array([[0.05, 0.01, 0.02],
+                        [0.01, 0.06, 0.03],
+                        [0.02, 0.03, 0.07]])
+        lbound = np.array([-0.3, -0.3, -0.3])
+        ubound = np.array([0.5, 0.5, 0.5])
+
+        cons = np.array([[1., 1., 1.]])
+        clbound = np.array([0.])
+        cubound = np.array([0.])
+        target_vol = 0.1
+
+        optimizer = CVOptimizer(objective,
+                                cov,
+                                lbound,
+                                ubound,
+                                None,
+                                None,
+                                None,
+                                target_vol,
+                                target_vol)
+
+        # check against known good result
+        np.testing.assert_array_almost_equal(optimizer.x_value(),
+                                             [.0231776, 0.1274768, 0.30130881],
+                                             4)
+
+    def test_cvoptimizer_with_cons(self):
+        objective = np.array([0.1, 0.2, 0.3])
+        cov = np.array([[0.05, 0.01, 0.02],
+                        [0.01, 0.06, 0.03],
+                        [0.02, 0.03, 0.07]])
+        lbound = np.array([-0.3, -0.3, -0.3])
+        ubound = np.array([0.5, 0.5, 0.5])
+
+        cons = np.array([[1., 1., 1.]])
+        clbound = np.array([0.])
+        cubound = np.array([0.])
+        target_vol = 0.1
+
+        optimizer = CVOptimizer(objective,
+                                cov,
+                                lbound,
+                                ubound,
+                                cons,
+                                clbound,
+                                cubound,
+                                target_vol,
+                                target_vol)
+
+        # check against known good result
+        np.testing.assert_array_almost_equal(optimizer.x_value(),
+                                             [-0.3, -0.10919033, 0.40919033],
+                                             4)
+
+    def test_cvoptimizer_with_cons_and_ieq(self):
+        objective = np.array([0.1, 0.2, 0.3])
+        cov = np.array([[0.05, 0.01, 0.02],
+                        [0.01, 0.06, 0.03],
+                        [0.02, 0.03, 0.07]])
+        lbound = np.array([-0.3, -0.3, -0.3])
+        ubound = np.array([0.5, 0.5, 0.5])
+
+        cons = np.array([[1., 1., 1.]])
+        clbound = np.array([0.])
+        cubound = np.array([0.])
+        target_vol = 0.1
+
+        optimizer = CVOptimizer(objective,
+                                cov,
+                                lbound,
+                                ubound,
+                                cons,
+                                clbound,
+                                cubound,
+                                0.,
+                                target_vol)
+
+        # check against known good result
+        np.testing.assert_array_almost_equal(optimizer.x_value(),
+                                             [-0.3, -0.10919033, 0.40919033],
+                                             4)
+
 
 if __name__ == '__main__':
     unittest.main()