add prints

1 files changed, 15 insertions, 23 deletions

diff --git a/Wrappers/Python/wip/fix_test.py b/Wrappers/Python/wip/fix_test.py
index 5e40d70..316606e 100755
--- a/Wrappers/Python/wip/fix_test.py
+++ b/Wrappers/Python/wip/fix_test.py
@@ -61,8 +61,8 @@ class Norm1(Function):
 
 opt = {'memopt': True}
 # Problem data.
-m = 30
-n = 30
+m = 4
+n = 10
 np.random.seed(1)
 Amat = np.asarray( np.random.randn(m, n), dtype=numpy.float32)
 #Amat = np.asarray(np.eye(m), dtype=np.float32) * 2
@@ -78,20 +78,21 @@ print ("A", A.A)
 # Change n to equal to m.
 vgb = VectorGeometry(m)
 vgx = VectorGeometry(n)
-b = vgb.allocate(VectorGeometry.RANDOM, dtype=numpy.float32)
-b.fill(bmat)
+b = vgb.allocate(2, dtype=numpy.float32)
+# b.fill(bmat)
 #b = DataContainer(bmat)
 
 # Regularization parameter
 lam = 10
 opt = {'memopt': True}
 # Create object instances with the test data A and b.
-f = Norm2Sq(A, b, c=0.5, memopt=True)
+f = Norm2Sq(A, b, c=1., memopt=True)
 #f = FunctionOperatorComposition(A, L2NormSquared(b=bmat))
 g0 = ZeroFunction()
 
 #f.L = 30.003
 x_init = vgx.allocate(VectorGeometry.RANDOM, dtype=numpy.float32)
+x_initcgls = x_init.copy()
 
 a = VectorData(x_init.as_array(), deep_copy=True)
 
@@ -136,33 +137,24 @@ print ("x1", x1.as_array())
 # Combine with least squares and solve using generic FISTA implementation
 #x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g1, opt=opt)
 def callback(it,  objective, solution):
-    print (it, objective, solution.as_array())
+    print (objective, f(solution))
 
 fa = FISTA(x_init=x_init, f=f, g=g1)
-fa.max_iteration = 10000
+fa.max_iteration = 100
 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 = 100000
-gd.update_objective_interval = 10000
+gd.max_iteration = 100
+gd.update_objective_interval = int( gd.max_iteration / 10 ) 
 gd.run(gd.max_iteration, callback = None, verbose=True)
 
-cgls = CGLS(x_init= x_init, operator=A, data=b)
-cgls.max_iteration = 200
-cgls.update_objective_interval = 1
-def stop_criterion(alg):
-    try:
-        x = alg.get_last_objective()
-        print (x)
-        a = True if x < numpy.finfo(numpy.float32).eps else False
-    except IndexError as ie:
-        a = False
-    def f ():
-        return a or alg.max_iteration_stop_cryterion()
-    return f
+cgls = CGLS(x_init= x_initcgls, operator=A, data=b)
+cgls.max_iteration = 1000
+cgls.update_objective_interval = 2
+
 #cgls.should_stop = stop_criterion(cgls)
-cgls.run(cgls.max_iteration, callback = None, verbose=True)
+cgls.run(10, callback = callback, verbose=True)
 
 # Print for comparison
 print("FISTA least squares plus 1-norm solution and objective value:")