Changeset 782 for mystic

Timestamp:

02/17/15 10:30:50 (15 months ago)

Author:

mmckerns

Message:

moved _SetEvaluationLimits inside CheckTermination?;
moved _termination and callback inside Step

Location:

mystic/mystic

Files:

• abstract_solver.py (modified) (3 diffs)
• differential_evolution.py (modified) (4 diffs)
• scipy_optimize.py (modified) (4 diffs)

mystic/mystic/abstract_solver.py

@@ -542 +542 @@
         if termination is None:
             termination = self._termination
+        # ensure evaluation limits have been imposed
+        self._SetEvaluationLimits()
         # check for termination messages
         msg = termination(self, info=True)
@@ -704 +706 @@
         # set up signal handler
         import signal
-        self._EARLYEXIT = False
+        self._EARLYEXIT = False  #XXX: why not use EARLYEXIT singleton?
         self._generateHandler(sigint_callback) 
-        if self._handle_sigint: signal.signal(signal.SIGINT, self.signal_handler)
+        if self._handle_sigint: signal.signal(signal.SIGINT,self.signal_handler)
 
        ## decorate cost function with bounds, penalties, monitors, etc
@@ -716 +718 @@
         # the initital optimization iteration
         if not len(self._stepmon): # do generation = 0
-            self.Step()
-            if callback is not None:
-                callback(self.bestSolution)
-         
-            # initialize termination conditions, if needed
-            self._termination(self) #XXX: call at generation 0 or always?
-        # impose the evaluation limits
-        self._SetEvaluationLimits()
+            self.Step(callback=callback)
 
         # the main optimization loop
         while not self.CheckTermination() and not self._EARLYEXIT:
-            self.Step(**settings)
-            if callback is not None:
-                callback(self.bestSolution)
+            self.Step(**settings)  # includes settings['callback']
         else: self._exitMain()
 

mystic/mystic/differential_evolution.py

@@ -222 +222 @@
         cost = self._bootstrap_objective(cost, ExtraArgs)
         # process and activate input settings
-        kwds['strategy'] = strategy  # override default strategy with None
+        kwds['strategy'] = strategy  # override default strategy
         settings = self._process_inputs(kwds)
         for key in settings:
             exec "%s = settings['%s']" % (key,key)
 
+        init = False  # flag to do 0th iteration 'post-initialization'
+
         if not len(self._stepmon): # do generation = 0
+            init = True
             self.population[0] = asfarray(self.population[0])
             # decouple bestSolution from population and bestEnergy from popEnergy
@@ -267 +270 @@
         # if savefrequency matches, then save state
         self._AbstractSolver__save_state()
+
+        # do callback
+        if callback is not None: callback(self.bestSolution)
+        # initialize termination conditions, if needed
+        if init: self._termination(self) #XXX: at generation 0 or always?
         return #XXX: call CheckTermination ?
 
@@ -379 +387 @@
         cost = self._bootstrap_objective(cost, ExtraArgs)
         # process and activate input settings
-        kwds['strategy'] = strategy  # override default strategy with None
+        kwds['strategy'] = strategy  # override default strategy
         settings = self._process_inputs(kwds)
         for key in settings:
             exec "%s = settings['%s']" % (key,key)
 
+        init = False  # flag to do 0th iteration 'post-initialization'
+
         if not len(self._stepmon): # do generation = 0
+            init = True
             self.population[0] = asfarray(self.population[0])
             # decouple bestSolution from population and bestEnergy from popEnergy
@@ -429 +440 @@
         # if savefrequency matches, then save state
         self._AbstractSolver__save_state()
+
+        # do callback
+        if callback is not None: callback(self.bestSolution)
+        # initialize termination conditions, if needed
+        if init: self._termination(self) #XXX: at generation 0 or always?
         return #XXX: call CheckTermination ?
 

mystic/mystic/scipy_optimize.py

@@ -152 +152 @@
 
         rho = 1; chi = 2; psi = 0.5; sigma = 0.5;
+        init = False  # flag to do 0th iteration 'post-initialization'
 
         if not len(self._stepmon): # do generation = 0
+            init = True
             x0 = self.population[0]
             x0 = asfarray(x0).flatten()
@@ -252 +254 @@
         # if savefrequency matches, then save state
         self._AbstractSolver__save_state()
+
+        # do callback
+        if callback is not None: callback(self.bestSolution)
+        # initialize termination conditions, if needed
+        if init: self._termination(self) #XXX: at generation 0 or always?
         return #XXX: call CheckTermination ?
 
@@ -469 +476 @@
         fval = self.popEnergy[0] # bestEnergy
         x1, fx, bigind, delta = self.__internals
+        init = False  # flag to do 0th iteration 'post-initialization'
 
         if not len(self._stepmon): # do generation = 0
+            init = True
             x = asfarray(x).flatten()
             x = asfarray(self._constraints(x))
@@ -558 +567 @@
         self.population[0] = x   # bestSolution
         self.popEnergy[0] = fval # bestEnergy
+
+        # do callback
+        if callback is not None: callback(self.bestSolution)
+        # initialize termination conditions, if needed
+        if init: self._termination(self) #XXX: at generation 0 or always?
         return #XXX: call CheckTermination ?