Merge pull request #158 from emitra17/156-reflect

Particle swarm improvements

docs/config_keys.rst

@@ -539,9 +539,9 @@ The following options are only available with ``fit_type = de``, and serve to ma
     * ``social = 1.7``
 
 **particle_weight**
-  Inertia weight of particle. A value less than 1 can be thought of as friction that contiuously decelerates the particle. 
+  Inertia weight of particle. A value less than 1 can be thought of as friction that contniuously decelerates the particle.
 
-  Default: 1
+  Default: 0.7
 
   Example:
     * ``particle_weight = 0.9``

pybnf/algorithms.py

@@ -922,8 +922,10 @@ def start_run(self):
         for i in range(len(new_params_list)):
             p = new_params_list[i]
             p.name = 'iter0p%i' % i
-            # Todo: Smart way to initialize velocity?
-            new_velocity = {v.name: np.random.uniform(-1, 1) for v in self.variables}
+
+            # As suggested by Engelbrecht 2012, set all initial velocities to 0
+            new_velocity = dict({v.name: 0. for v in self.variables})
+
             self.swarm.append([p, new_velocity])
             self.pset_map[p] = len(self.swarm)-1  # Index of the newly added PSet.
 
@@ -987,10 +989,13 @@ def got_result(self, res):
         # If so, update based on reflection protocol and set velocity to 0
         new_vars = []
         for v in self.swarm[p][0]:
-            new_val = v.value + self.swarm[p][1][v.name]
+            new_vars.append(v.add(self.swarm[p][1][v.name]))
+            if v.log_space:
+                new_val = 10.**(np.log10(v.value) + self.swarm[p][1][v.name])
+            else:
+                new_val = v.value + self.swarm[p][1][v.name]
             if new_val < v.lower_bound or v.upper_bound < new_val:
                 self.swarm[p][1][v.name] = 0.0
-            new_vars.append(v.add(self.swarm[p][1][v.name]))
 
         new_pset = PSet(new_vars)
         self.swarm[p][0] = new_pset

pybnf/config.py

@@ -128,7 +128,7 @@ def default_config():
             'mutation_rate': 0.5, 'mutation_factor': 0.5, 'islands': 1, 'migrate_every': 20, 'num_to_migrate': 3,
             'stop_tolerance': 0.002, 'de_strategy': 'rand1',
 
-            'particle_weight': 1.0, 'adaptive_n_max': 30, 'adaptive_n_stop': np.inf, 'adaptive_abs_tol': 0.0,
+            'particle_weight': 0.7, 'adaptive_n_max': 30, 'adaptive_n_stop': np.inf, 'adaptive_abs_tol': 0.0,
             'adaptive_rel_tol': 0.0, 'cognitive': 1.5, 'social': 1.5, 'v_stop': 0.,
 
             'local_min_limit': 5,

pybnf/pset.py

@@ -974,8 +974,11 @@ def _reflect(self, new):
 
         while True:
             if num_reflections >= 1000:
-                logger.error("Error in parameter reflection.  Too many reflections: Init = %s, add = %s, parameter = %s" % (cur, add, self.name))
-                raise PybnfError("Too many reflections for parameter %s. Current value = %s, adding value %s" % (self.name, cur, add))
+                logger.error("Error in parameter reflection.  Too many reflections: Init = %s, add = %s, parameter ="
+                             " %s. Set parameter to a random value" % (cur, add, self.name))
+                # Return a random value. Not ideal, but better than crashing the whole run.
+                rand = np.random.uniform(lb, ub)
+                return 10.**rand if self.log_space else rand
 
             num_reflections += 1
             if cur + add > ub: