From: Jean-Philippe ARGAUD <jean-philippe.argaud@edf.fr>
Date: Wed, 5 Apr 2017 10:31:06 +0000 (+0200)
Subject: Improvement of bounds treatment for DFO
X-Git-Tag: V8_3_0rc1~10
X-Git-Url: http://git.salome-platform.org/gitweb/?a=commitdiff_plain;h=641b9166714bb8d9e0cd2b24f883f2abd2ced0d1;p=modules%2Fadao.git

Improvement of bounds treatment for DFO
---

diff --git a/src/daComposant/daAlgorithms/DerivativeFreeOptimization.py b/src/daComposant/daAlgorithms/DerivativeFreeOptimization.py
index 221d4ef..e9d9a5f 100644
--- a/src/daComposant/daAlgorithms/DerivativeFreeOptimization.py
+++ b/src/daComposant/daAlgorithms/DerivativeFreeOptimization.py
@@ -213,9 +213,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 return CostFunction(_Xx, self._parameters["QualityCriterion"])
             opt.set_min_objective(_f)
             if self._parameters["Bounds"] is not None:
-                lub = numpy.array(self._parameters["Bounds"]).reshape((Xini.size,2))
-                lb = lub[:,0]
-                ub = lub[:,1]
+                lub = numpy.array(self._parameters["Bounds"],dtype=float).reshape((Xini.size,2))
+                lb = lub[:,0] ; lb[numpy.isnan(lb)] = -float('inf')
+                ub = lub[:,1] ; ub[numpy.isnan(ub)] = +float('inf')
                 if self._parameters["optdisp"]:
                     print("%s: upper bounds %s"%(opt.get_algorithm_name(),ub))
                     print("%s: lower bounds %s"%(opt.get_algorithm_name(),lb))
@@ -249,9 +249,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 return CostFunction(_Xx, self._parameters["QualityCriterion"])
             opt.set_min_objective(_f)
             if self._parameters["Bounds"] is not None:
-                lub = numpy.array(self._parameters["Bounds"]).reshape((Xini.size,2))
-                lb = lub[:,0]
-                ub = lub[:,1]
+                lub = numpy.array(self._parameters["Bounds"],dtype=float).reshape((Xini.size,2))
+                lb = lub[:,0] ; lb[numpy.isnan(lb)] = -float('inf')
+                ub = lub[:,1] ; ub[numpy.isnan(ub)] = +float('inf')
                 if self._parameters["optdisp"]:
                     print("%s: upper bounds %s"%(opt.get_algorithm_name(),ub))
                     print("%s: lower bounds %s"%(opt.get_algorithm_name(),lb))
@@ -273,9 +273,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 return CostFunction(_Xx, self._parameters["QualityCriterion"])
             opt.set_min_objective(_f)
             if self._parameters["Bounds"] is not None:
-                lub = numpy.array(self._parameters["Bounds"]).reshape((Xini.size,2))
-                lb = lub[:,0]
-                ub = lub[:,1]
+                lub = numpy.array(self._parameters["Bounds"],dtype=float).reshape((Xini.size,2))
+                lb = lub[:,0] ; lb[numpy.isnan(lb)] = -float('inf')
+                ub = lub[:,1] ; ub[numpy.isnan(ub)] = +float('inf')
                 if self._parameters["optdisp"]:
                     print("%s: upper bounds %s"%(opt.get_algorithm_name(),ub))
                     print("%s: lower bounds %s"%(opt.get_algorithm_name(),lb))
@@ -297,9 +297,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 return CostFunction(_Xx, self._parameters["QualityCriterion"])
             opt.set_min_objective(_f)
             if self._parameters["Bounds"] is not None:
-                lub = numpy.array(self._parameters["Bounds"]).reshape((Xini.size,2))
-                lb = lub[:,0]
-                ub = lub[:,1]
+                lub = numpy.array(self._parameters["Bounds"],dtype=float).reshape((Xini.size,2))
+                lb = lub[:,0] ; lb[numpy.isnan(lb)] = -float('inf')
+                ub = lub[:,1] ; ub[numpy.isnan(ub)] = +float('inf')
                 if self._parameters["optdisp"]:
                     print("%s: upper bounds %s"%(opt.get_algorithm_name(),ub))
                     print("%s: lower bounds %s"%(opt.get_algorithm_name(),lb))
@@ -321,9 +321,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 return CostFunction(_Xx, self._parameters["QualityCriterion"])
             opt.set_min_objective(_f)
             if self._parameters["Bounds"] is not None:
-                lub = numpy.array(self._parameters["Bounds"]).reshape((Xini.size,2))
-                lb = lub[:,0]
-                ub = lub[:,1]
+                lub = numpy.array(self._parameters["Bounds"],dtype=float).reshape((Xini.size,2))
+                lb = lub[:,0] ; lb[numpy.isnan(lb)] = -float('inf')
+                ub = lub[:,1] ; ub[numpy.isnan(ub)] = +float('inf')
                 if self._parameters["optdisp"]:
                     print("%s: upper bounds %s"%(opt.get_algorithm_name(),ub))
                     print("%s: lower bounds %s"%(opt.get_algorithm_name(),lb))