added test

2 files changed, 44 insertions, 19 deletions

diff --git a/Wrappers/Python/test/test_run_test.py b/Wrappers/Python/test/test_run_test.py
index 4f66da1..a0db9cb 100755
--- a/Wrappers/Python/test/test_run_test.py
+++ b/Wrappers/Python/test/test_run_test.py
@@ -10,10 +10,9 @@ from ccpi.optimisation.algorithms import FISTA
 #from ccpi.optimisation.algs import FBPD
 from ccpi.optimisation.functions import Norm2Sq
 from ccpi.optimisation.functions import ZeroFunction
+# from ccpi.optimisation.funcs import Norm1
 from ccpi.optimisation.functions import L1Norm
-# This was removed
-#from ccpi.optimisation.funcs import Norm2
-#from ccpi.optimisation.funcs import Norm1
+from ccpi.optimisation.funcs import Norm2
 
 from ccpi.optimisation.operators import LinearOperatorMatrix
 from ccpi.optimisation.operators import Identity
@@ -83,7 +82,6 @@ class TestAlgorithms(unittest.TestCase):
                 opt = {'memopt': True}
                 # Create object instances with the test data A and b.
                 f = Norm2Sq(A, b, c=0.5, memopt=True)
-                f.L = LinearOperator.PowerMethod(A, 10)
                 g0 = ZeroFunction()
 
                 # Initial guess
@@ -126,7 +124,6 @@ class TestAlgorithms(unittest.TestCase):
             self.assertTrue(cvx_not_installable)
 
     def test_FISTA_Norm1_cvx(self):
-        print ("test_FISTA_Norm1_cvx")
         if not cvx_not_installable:
             try:
                 opt = {'memopt': True}
@@ -141,8 +138,11 @@ class TestAlgorithms(unittest.TestCase):
 
                 # A = Identity()
                 # Change n to equal to m.
-
-                b = DataContainer(bmat)
+                vgb = VectorGeometry(m)
+                vgx = VectorGeometry(n)
+                b = vgb.allocate()
+                b.fill(bmat)
+                #b = DataContainer(bmat)
 
                 # Regularization parameter
                 lam = 10
@@ -152,10 +152,11 @@ class TestAlgorithms(unittest.TestCase):
                 g0 = ZeroFunction()
 
                 # Initial guess
-                x_init = DataContainer(np.zeros((n, 1)))
+                #x_init = DataContainer(np.zeros((n, 1)))
+                x_init = vgx.allocate()
 
                 # Create 1-norm object instance
-                g1 = lam * L1Norm()
+                g1 = Norm1(lam)
 
                 g1(x_init)
                 g1.prox(x_init, 0.02)
@@ -229,7 +230,7 @@ class TestAlgorithms(unittest.TestCase):
 
 
             # Create 1-norm object instance
-            g1 = lam * L1Norm()
+            g1 = Norm1(lam)
 
             # Compare to CVXPY
 
@@ -296,7 +297,7 @@ class TestAlgorithms(unittest.TestCase):
 
             # 1-norm regulariser
             lam1_denoise = 1.0
-            g1_denoise = lam1_denoise * L1Norm()
+            g1_denoise = Norm1(lam1_denoise)
 
             # Initial guess
             x_init_denoise = ImageData(np.zeros((N, N)))
diff --git a/Wrappers/Python/wip/fix_test.py b/Wrappers/Python/wip/fix_test.py
index 9eb0a4e..b1006c0 100755
--- a/Wrappers/Python/wip/fix_test.py
+++ b/Wrappers/Python/wip/fix_test.py
@@ -103,13 +103,12 @@ a = VectorData(x_init.as_array(), deep_copy=True)
 
 assert id(x_init.as_array()) != id(a.as_array())
 
-#%%
-# f.L = LinearOperator.PowerMethod(A, 25, x_init)[0] 
-# print ('f.L', f.L)
+
+#f.L = LinearOperator.PowerMethod(A, 25, x_init)[0] 
+#print ('f.L', f.L)
 rate = (1 / f.L) / 6
-f.L *= 12
-print (f.L)
-# rate = f.L / 1000
+#f.L *= 12
+
 # Initial guess
 #x_init = DataContainer(np.zeros((n, 1)))
 print ('x_init', x_init.as_array())
@@ -154,7 +153,7 @@ fa.update_objective_interval = int( fa.max_iteration / 10 )
 fa.run(fa.max_iteration, callback = None, verbose=True)
 
 gd = GradientDescent(x_init=x_init, objective_function=f, rate = rate )
-gd.max_iteration = 10000
+gd.max_iteration = 5000
 gd.update_objective_interval = int( gd.max_iteration / 10 ) 
 gd.run(gd.max_iteration, callback = None, verbose=True)
 
@@ -181,4 +180,29 @@ print ('CGLS           ', A.direct(cgls.get_output()).as_array())
 
 cond = numpy.linalg.cond(A.A)
 
-print ("cond" , cond)
\ No newline at end of file
+print ("cond" , cond)
+
+#%%
+try:
+    import cvxpy as cp
+    # Construct the problem.
+    x = cp.Variable(n)
+    objective = cp.Minimize(cp.sum_squares(A.A*x - bmat))
+    prob = cp.Problem(objective)
+    # The optimal objective is returned by prob.solve().
+    result = prob.solve(solver = cp.MOSEK)
+
+    print ('CGLS           ', cgls.get_output().as_array())
+    print ('CVX           ', x.value)
+
+    print ('FISTA           ', fa.get_output().as_array())
+    print ('GD           ', gd.get_output().as_array())
+except ImportError as ir:
+    pass
+
+    #%%
+
+
+
+
+