optimization driver changes

aerialhedgehog · Jun 4, 2015 · b6a14ed · b6a14ed
1 parent ac2a705
commit b6a14ed
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diff --git a/tests/optimize/driver_findiff.py b/tests/optimize/driver_findiff.py
@@ -1,105 +1,105 @@
-# example_test_script.py
-# 
-# Created:  Your Name, Jun 2014
-# Modified:     
-
-
-# ----------------------------------------------------------------------        
-#   Imports
-# ----------------------------------------------------------------------    
-
-import VyPy
-import numpy as np
-
-import VyPy.optimize as opt
-
-from VyPy.data import obunch
-
-
-# ----------------------------------------------------------------------        
-#   Main
-# ----------------------------------------------------------------------    
-
-def main():
-    """ test the SLSQP Optimizer, with a test problem function """
-
-    # ------------------------------------------------------------------
-    #   Get the problem
-    # ------------------------------------------------------------------
-
-    #from problem_function import setup_problem
-    from problem_evaluator import setup_problem
-    problem = setup_problem()
-
-
-    # Gradients
-    evaluator = problem.objectives['f'].evaluator
-
-    variables = obunch()
-    variables.x1 = 0.
-    variables.x2 = 0.
-    variables.x3 = np.array([0.,0.,0.])
-
-    grad_1 = evaluator.gradient(variables)
-
-    print grad_1
-
-    evaluator.gradient = opt.gradients.FiniteDifference(evaluator.function)
-
-    grad_2 = evaluator.gradient(variables)
-
-
-    print grad_2
-
-    return
-
-    # ------------------------------------------------------------------
-    #   Setup Driver
-    # ------------------------------------------------------------------    
-
-    driver = opt.drivers.SLSQP()
-    driver.max_iterations = 1000
-    driver.verbose = True
-
-    # ------------------------------------------------------------------
-    #   Run the Problem
-    # ------------------------------------------------------------------        
-
-    results = driver.run(problem)
-
-    print 'Results:'
-    print results
-
-
-    # ------------------------------------------------------------------
-    #   Check Results
-    # ------------------------------------------------------------------
-
-    # the expected results
-    truth = problem.truth
-
-    # the checking function
-    def check(a,b):
-        return np.abs(a-b)
-
-    delta = truth.do_recursive(check,results)
-
-    print 'Error to Expected:'
-    print delta
-
-    assert np.all( delta.pack_array() < 1e-6 )
-    assert len( delta.pack_array() ) == 10
-
-    # done!
-    return
-
-#: def main()
-
-
-# ----------------------------------------------------------------------        
-#   Call Main
-# ----------------------------------------------------------------------    
-
-if __name__ == '__main__':
-
-    main()
+# example_test_script.py
+# 
+# Created:  Your Name, Jun 2014
+# Modified:     
+
+
+# ----------------------------------------------------------------------        
+#   Imports
+# ----------------------------------------------------------------------    
+
+import VyPy
+import numpy as np
+
+import VyPy.optimize as opt
+
+from VyPy.data import obunch
+
+
+# ----------------------------------------------------------------------        
+#   Main
+# ----------------------------------------------------------------------    
+
+def main():
+    """ test the SLSQP Optimizer, with a test problem function """
+
+    # ------------------------------------------------------------------
+    #   Get the problem
+    # ------------------------------------------------------------------
+
+    #from problem_function import setup_problem
+    from problem_evaluator import setup_problem
+    problem = setup_problem()
+
+
+    # Gradients
+    evaluator = problem.objectives['f'].evaluator
+
+    variables = obunch()
+    variables.x1 = 0.
+    variables.x2 = 0.
+    variables.x3 = np.array([0.,0.,0.])
+
+    grad_1 = evaluator.gradient(variables)
+
+    print grad_1
+
+    evaluator.gradient = opt.gradients.FiniteDifference(evaluator.function)
+
+    grad_2 = evaluator.gradient(variables)
+
+
+    print grad_2
+
+    return
+
+    # ------------------------------------------------------------------
+    #   Setup Driver
+    # ------------------------------------------------------------------    
+
+    driver = opt.drivers.SLSQP()
+    driver.max_iterations = 1000
+    driver.verbose = True
+
+    # ------------------------------------------------------------------
+    #   Run the Problem
+    # ------------------------------------------------------------------        
+
+    results = driver.run(problem)
+
+    print 'Results:'
+    print results
+
+
+    # ------------------------------------------------------------------
+    #   Check Results
+    # ------------------------------------------------------------------
+
+    # the expected results
+    truth = problem.truth
+
+    # the checking function
+    def check(a,b):
+        return np.abs(a-b)
+
+    delta = truth.do_recursive(check,results)
+
+    print 'Error to Expected:'
+    print delta
+
+    assert np.all( delta.pack_array() < 1e-6 )
+    assert len( delta.pack_array() ) == 10
+
+    # done!
+    return
+
+#: def main()
+
+
+# ----------------------------------------------------------------------        
+#   Call Main
+# ----------------------------------------------------------------------    
+
+if __name__ == '__main__':
+
+    main()