slight change to how infill works

capaulson · Jun 19, 2015 · afbd96c · afbd96c
1 parent 1674c1d
commit afbd96c
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Showing 2 changed files with 10 additions and 9 deletions.
diff --git a/pyKriging/krige.py b/pyKriging/krige.py
@@ -252,7 +252,7 @@ def infill_objective_ei(self,candidates, args):
             fitness.append(-1 * self.expimp(entry))
         return fitness
 
-    def infill(self, points, method='error'):
+    def infill(self, points, method='error', addPoint=True):
         '''
         The function identifies where new points are needed in the model.
         :param points: The number of points to add to the model. Multiple points are added via imputation.
@@ -287,7 +287,8 @@ def infill(self, points, method='error'):
             final_pop.sort(reverse=True)
             newpoint = final_pop[0].candidate
             returnValues[i][:] = newpoint
-            self.addPoint(returnValues[i], self.predict(returnValues[i]), norm=False)
+            if addPoint:
+                self.addPoint(returnValues[i], self.predict(returnValues[i]), norm=True)
 
         self.X = np.copy(initX)
         self.y = np.copy(inity)

diff --git a/pyKriging/regressionkrige.py b/pyKriging/regressionkrige.py
@@ -35,10 +35,9 @@ def __init__(self, X, y, testfunction=None, name='', testPoints=None, **kwargs):
         self.sp = samplingplan(self.k)
         self.updateData()
         self.updateModel()
-
         self.thetamin = 1e-4
-        self.thetamax = 100
-        self.pmin = 1
+        self.thetamax =100
+        self.pmin = 1.81231
         self.pmax = 2
 
         # Setup functions for tracking history
@@ -172,14 +171,15 @@ def updateModel(self):
             # print Exception, err
             raise Exception("bad params")
 
-    def predict(self, X):
+    def predict(self, X, norm=True):
         '''
         This function returns the prediction of the model at the real world coordinates of X
         :param X: Design variable to evaluate
         :return: Returns the 'real world' predicted value
         '''
         X = copy.deepcopy(X)
-        X = self.normX(X)
+        if norm:
+            X = self.normX(X)
         return self.inversenormy(self.predict_normalized(X))
 
     def predict_var(self, X, norm=True):
@@ -373,7 +373,7 @@ def train(self, optimizer='pso'):
             # ea.observer = inspyred.ec.observers.stats_observer
             final_pop = ea.evolve(generator=self.generate_population,
                                   evaluator=self.fittingObjective,
-                                  pop_size=300,
+                                  pop_size=150,
                                   maximize=False,
                                   bounder=ec.Bounder(lowerBound, upperBound),
                                   max_evaluations=30000,
@@ -388,7 +388,7 @@ def train(self, optimizer='pso'):
             ea.terminator = self.no_improvement_termination
             final_pop = ea.evolve(generator=self.generate_population,
                                   evaluator=self.fittingObjective,
-                                  pop_size=300,
+                                  pop_size=50,
                                   maximize=False,
                                   bounder=ec.Bounder(lowerBound, upperBound),
                                   max_evaluations=30000,