Changeset 135

Timestamp:

04/04/09 09:35:06 (7 years ago)

Author:

mmckerns

Message:

add 'disp' convergence display to DE solvers (ticket #26)

File:

• mystic/mystic/differential_evolution.py (modified) (8 diffs)

mystic/mystic/differential_evolution.py

@@ -164 +164 @@
         iteration.  It is called as callback(xk), where xk is
         the current parameter vector.  [default = None]
+    disp -- non-zero to print convergence messages.
 
         """
@@ -172 +173 @@
         ScalingFactor=0.7    #multiplier for mutation impact
         callback=None        #user-supplied function, called after each step
+        disp=0               #non-zero to print convergence messages
         if kwds.has_key('strategy'): strategy = kwds['strategy']
         if kwds.has_key('CrossProbability'): \
@@ -177 +179 @@
         if kwds.has_key('ScalingFactor'): ScalingFactor = kwds['ScalingFactor']
         if kwds.has_key('callback'): callback = kwds['callback']
+        if kwds.has_key('disp'): disp = kwds['disp']
         #-------------------------------------------------------------
 
@@ -233 +236 @@
 
         signal.signal(signal.SIGINT,signal.default_int_handler)
+
+        # code below here pushes output to scipy.optimize.fmin interface
+        fval = self.bestEnergy
+        warnflag = 0
+
+        if fcalls[0] >= self._maxfun:
+            warnflag = 1
+            if disp:
+                print "Warning: Maximum number of function evaluations has "\
+                      "been exceeded."
+        elif generation >= self._maxiter:
+            warnflag = 2
+            if disp:
+                print "Warning: Maximum number of iterations has been exceeded"
+        else:
+            if disp:
+                print "Optimization terminated successfully."
+                print "         Current function value: %f" % fval
+                print "         Iterations: %d" % generation
+                print "         Function evaluations: %d" % fcalls[0]
 
         return 
@@ -282 +305 @@
         iteration.  It is called as callback(xk), where xk is
         the current parameter vector.  [default = None]
+    disp -- non-zero to print convergence messages.
 
         """
@@ -290 +314 @@
         ScalingFactor=0.7    #multiplier for mutation impact
         callback=None        #user-supplied function, called after each step
+        disp=0               #non-zero to print convergence messages
         if kwds.has_key('strategy'): strategy = kwds['strategy']
         if kwds.has_key('CrossProbability'): \
@@ -295 +320 @@
         if kwds.has_key('ScalingFactor'): ScalingFactor = kwds['ScalingFactor']
         if kwds.has_key('callback'): callback = kwds['callback']
+        if kwds.has_key('disp'): disp = kwds['disp']
         #-------------------------------------------------------------
 
@@ -355 +381 @@
 
         signal.signal(signal.SIGINT,signal.default_int_handler)
+
+        # code below here pushes output to scipy.optimize.fmin interface
+        fval = self.bestEnergy
+        warnflag = 0
+
+        if fcalls[0] >= self._maxfun:
+            warnflag = 1
+            if disp:
+                print "Warning: Maximum number of function evaluations has "\
+                      "been exceeded."
+        elif generation >= self._maxiter:
+            warnflag = 2
+            if disp:
+                print "Warning: Maximum number of iterations has been exceeded"
+        else:
+            if disp:
+                print "Optimization terminated successfully."
+                print "         Current function value: %f" % fval
+                print "         Iterations: %d" % generation
+                print "         Function evaluations: %d" % fcalls[0]
 
         return