to get working..

SimPEG/inverse/Inversion.py

@@ -1,4 +1,5 @@
 import numpy as np
+from SimPEG.utils import sdiag
 
 class Inversion(object):
     """docstring for Inversion"""
@@ -11,20 +12,18 @@ class Inversion(object):
         self.opt = opt
 
     @property
-    def W(self):
+    def Wd(self):
         """
             Standard deviation weighting matrix.
         """
-        return self._W
-    @W.setter
-    def W(self, value):
-        self._W = value
+        return sdiag(1/self.prob.std)
 
     def run(self, m0):
+        m = m0
         self._iter = 0
         while True:
             self._beta = self.getBeta()
-            self.opt.minimize(self.evalFunction,m)
+            m = self.opt.minimize(self.evalFunction,m)
             if self.stoppingCriteria(): break
             self._iter += 1
 

SimPEG/inverse/Optimize.py

@@ -111,13 +111,16 @@ class Minimize(object):
         self._STOP[4] = self._iter >= self.maxIter
         return all(self._STOP[0:3]) | any(self._STOP[3:])
 
+    def projection(self, p):
+        return p
+
     def linesearch(self, p):
         # Armijo linesearch
         descent = np.inner(self.g, p)
         t = 1
         iterLS = 0
         while iterLS < self.maxIterLS:
-            xt = self.xc + t*p
+            xt = self.projection(self.xc + t*p)
             ft = self.evalFunction(xt, return_g=False, return_H=False)
             if ft < self.f + t*self.LSreduction*descent:
                 break

SimPEG/inverse/__init__.py

@@ -1 +1,2 @@
 from Optimize import *
+from Inversion import *