Merge pull request #21 from capaulson/Python3-Support

Python3 support
capaulson · May 30, 2017 · c0ba9af · c0ba9af
2 parents 81adb25 + f7ed958
commit c0ba9af
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Showing 14 changed files with 127 additions and 129 deletions.
diff --git a/examples/2D_leave_n_out.py b/examples/2D_leave_n_out.py
@@ -1,3 +1,4 @@
+from __future__ import print_function
 __author__ = 'cpaulson'
 import pyKriging
 from pyKriging.krige import kriging
@@ -15,7 +16,7 @@
 # We generate our observed values based on our sampling plan and the test function
 y = testfun(X)
 
-print 'Setting up the Kriging Model'
+print('Setting up the Kriging Model')
 cvMSE = []
 # Now that we have our initial data, we can create an instance of a kriging model
 k = kriging(X, y, testfunction=testfun, name='simple', testPoints=300)
@@ -26,7 +27,7 @@
 
 k.plot()
 for i in range(15):
-    print i
+    print(i)
     newpoints = k.infill(1)
     for point in newpoints:
         # print 'Adding point {}'.format(point)
@@ -43,15 +44,15 @@
 
 # #And plot the model
 
-print 'Now plotting final results...'
+print('Now plotting final results...')
 # k.plot()
 
 
-print k.testPoints
-print k.history['points']
-print k.history['rsquared']
-print k.history['avgMSE']
-print cvMSE
+print(k.testPoints)
+print(k.history['points'])
+print(k.history['rsquared'])
+print(k.history['avgMSE'])
+print(cvMSE)
 from matplotlib import pylab as plt
 plt.plot(range(len(k.history['rsquared'])), k.history['rsquared'])
 plt.plot(range(len(cvMSE)), cvMSE)

diff --git a/examples/2D_model_convergence.py b/examples/2D_model_convergence.py
@@ -1,3 +1,4 @@
+from __future__ import print_function
 __author__ = 'cpaulson'
 import pyKriging
 from pyKriging.krige import kriging
@@ -13,7 +14,7 @@
 # We generate our observed values based on our sampling plan and the test function
 y = testfun(X)
 
-print 'Setting up the Kriging Model'
+print('Setting up the Kriging Model')
 
 # Now that we have our initial data, we can create an instance of a kriging model
 k = kriging(X, y, testfunction=testfun, name='simple', testPoints=250)
@@ -25,17 +26,17 @@
 while k.history['rsquared'][-1] < 0.9999:
     newpoints = k.infill(2)
     for point in newpoints:
-        print 'Adding point {}'.format(point)
+        print('Adding point {}'.format(point))
         k.addPoint(point, testfun(point)[0])
     k.train()
     k.snapshot()
-    print 'Current rsquared is: {}'.format(k.history['rsquared'][-1])
+    print('Current rsquared is: {}'.format(k.history['rsquared'][-1]))
 
-print 'The prediction has converged, with {} number of points in the model'.format(k.n)
+print('The prediction has converged, with {} number of points in the model'.format(k.n))
 
 # #And plot the model
 
-print 'Now plotting final results...'
+print('Now plotting final results...')
 k.plot()
 
 
diff --git a/examples/2D_simple_train.py b/examples/2D_simple_train.py
@@ -1,3 +1,4 @@
+from __future__ import print_function
 __author__ = 'cpaulson'
 import pyKriging
 from pyKriging.krige import kriging
@@ -13,7 +14,7 @@
 # We generate our observed values based on our sampling plan and the test function
 y = testfun(X)
 
-print 'Setting up the Kriging Model'
+print('Setting up the Kriging Model')
 
 # Now that we have our initial data, we can create an instance of a kriging model
 k = kriging(X, y, testfunction=testfun, name='simple', testPoints=250)
@@ -23,14 +24,14 @@
 for i in range(5):
     newpoints = k.infill(2)
     for point in newpoints:
-        print 'Adding point {}'.format(point)
+        print(('Adding point {}'.format(point)))
         k.addPoint(point, testfun(point)[0])
     k.train()
     k.snapshot()
 
 # #And plot the model
 
-print 'Now plotting final results...'
+print('Now plotting final results...')
 k.plot()
 
 
diff --git a/examples/2D_simple_train_expected_improvement.py b/examples/2D_simple_train_expected_improvement.py
@@ -1,3 +1,4 @@
+from __future__ import print_function
 __author__ = 'cpaulson'
 import pyKriging
 from pyKriging.krige import kriging
@@ -15,7 +16,7 @@
 optimizer = 'ga'
 
 # Now that we have our initial data, we can create an instance of a kriging model
-print 'Setting up the Kriging Model'
+print('Setting up the Kriging Model')
 k = kriging(X, y, testfunction=testfun, name='simple_ei', testPoints=300)
 k.train(optimizer=optimizer)
 k.snapshot()
@@ -25,7 +26,7 @@
 for i in range(5):
     newpoints = k.infill(1, method='error')
     for point in newpoints:
-        print 'Adding point {}'.format(point)
+        print('Adding point {}'.format(point))
         k.addPoint(point, testfun(point)[0])
     k.train(optimizer=optimizer)
     k.snapshot()
@@ -34,13 +35,13 @@
 for i in range(5):
     newpoints = k.infill(1, method='ei')
     for point in newpoints:
-        print 'Adding point {}'.format(point)
+        print('Adding point {}'.format(point))
         k.addPoint(point, testfun(point)[0])
     k.train(optimizer=optimizer)
     k.snapshot()
 
 # And plot the results
-print 'Now plotting final results...'
+print('Now plotting final results...')
 k.plot()
 
 

diff --git a/examples/2d_regression_Kriging.py b/examples/2d_regression_Kriging.py
@@ -1,3 +1,4 @@
+from __future__ import print_function
 __author__ = 'cpaulson'
 import sys
 sys.path.insert(0, '../')
@@ -16,26 +17,26 @@
 
 # We generate our observed values based on our sampling plan and the test function
 y = testfun(X)
-print X, y
+print(X, y)
 
 testfun = pyKriging.testfunctions().branin
 
 
-print 'Setting up the Kriging Model'
+print('Setting up the Kriging Model')
 
 # Now that we have our initial data, we can create an instance of a kriging model
 k = regression_kriging(X, y, testfunction=testfun, name='simple', testPoints=250)
 k.train(optimizer='pso')
 k1 = kriging(X, y, testfunction=testfun, name='simple', testPoints=250)
 k1.train(optimizer='pso')
-print k.Lambda
+print(k.Lambda)
 k.snapshot()
 
 
 for i in range(1):
     newpoints = k.infill(5)
     for point in newpoints:
-        print 'Adding point {}'.format(point)
+        print('Adding point {}'.format(point))
         newValue = testfun(point)[0]
         k.addPoint(point, newValue)
         k1.addPoint(point, newValue)
@@ -45,8 +46,8 @@
 #
 # # #And plot the model
 
-print 'Now plotting final results...'
-print k.Lambda
+print('Now plotting final results...')
+print(k.Lambda)
 k.plot(show=False)
 k1.plot()
 

diff --git a/examples/3d_Simple_Train.py b/examples/3d_Simple_Train.py
@@ -1,3 +1,4 @@
+from __future__ import print_function
 import pyKriging
 from pyKriging.krige import kriging
 from pyKriging.samplingplan import samplingplan
@@ -23,13 +24,13 @@
 # The infill method can be  used for this
 # In this example, we will add nine points in three batches. The model gets trained after each stage
 for i in range(10):
-    print k.history['rsquared'][-1]
-    print 'Infill iteration {0}'.format(i + 1)
+    print(k.history['rsquared'][-1])
+    print('Infill iteration {0}'.format(i + 1))
     infillPoints = k.infill(10)
 
     # Evaluate the infill points and add them back to the Kriging model
     for point in infillPoints:
-        print 'Adding point {}'.format(point)
+        print('Adding point {}'.format(point))
         k.addPoint(point, testfun(point)[0])
 
     # Retrain the model with the new points added in to the model

diff --git a/examples/coKriging.py b/examples/coKriging.py
@@ -1,3 +1,4 @@
+from __future__ import print_function
 __author__ = 'cpaulson'
 
 import sys
@@ -14,8 +15,8 @@ def cheap(X):
     C=-5
     D=0
 
-    print X
-    print ((X+D)*6-2)
+    print(X)
+    print(((X+D)*6-2))
     return A*np.power( ((X+D)*6-2), 2 )*np.sin(((X+D)*6-2)*2)+((X+D)-0.5)*B+C
 
 def expensive(X):
@@ -30,7 +31,7 @@ def expensive(X):
 
 ck = coKriging.coKriging(Xc, yc, Xe, ye)
 ck.thetac = np.array([1.2073])
-print ck.Xc
+print(ck.Xc)
 ck.updateData()
 ck.updatePsi()
 ck.neglnlikehood()

diff --git a/pyKriging/GlobalSensitivity.py b/pyKriging/GlobalSensitivity.py
@@ -83,8 +83,8 @@ def sensitivity_Sobol(self, Model, plot=0):
     data = np.genfromtxt(dataFile, delimiter=' ', invalid_raise=False)
     X = data[:,[3,4]]
     VAS = data[:, [5]][:,0]
-    print VAS
+    print(VAS)
     AASM = data[:, [6]][:,0]
-    print AASM
+    print(AASM)
     VAD = data[:, [7]][:,0]
-    print VAD
+    print(VAD)
diff --git a/pyKriging/coKriging.py b/pyKriging/coKriging.py
@@ -40,7 +40,7 @@ def __init__(self, Xc, yc, Xe, ye):
         self.one=ones([self.ne+self.nc,1])
         self.y=[self.yc, self.ye]
 
-        print 'here1'
+        print('here1')
 
     def reorder_data(self):
         xe = []
@@ -53,7 +53,7 @@ def reorder_data(self):
 
         for enu,entry in enumerate(self.Xc):
             if entry in self.Xe:
-                print 'Found this value in XE!!'
+                print('Found this value in XE!!')
                 for enu1,test in enumerate(self.Xe):
                     # if entry[0] == test[0] and  entry[1] == test[1]:
                     if entry == test:
@@ -87,25 +87,25 @@ def updateData(self):
     def traincheap(self):
         self.kc = kriging(self.Xc, self.yc)
         self.kc.train()
-        print
+        print()
 
 
     def distanceXc(self):
         self.distanceXc = np.zeros((self.nc,self.nc, self.k))
         for i in range( self.nc ):
-            for j in xrange(i+1,self.nc):
+            for j in range(i+1,self.nc):
                 self.distanceXc[i][j] = np.abs((self.Xc[i]-self.Xc[j]))
 
     def distanceXe(self):
         self.distanceXe = np.zeros((self.ne,self.ne, self.k))
         for i in range( self.ne ):
-            for j in xrange(i+1,self.ne):
+            for j in range(i+1,self.ne):
                 self.distanceXe[i][j] = np.abs((self.Xe[i]-self.Xe[j]))
 
     def distanceXcXe(self):
         self.distanceXcXe = np.zeros((self.nc,self.ne, self.k))
         for i in range( self.nc ):
-            for j in xrange(self.ne):
+            for j in range(self.ne):
                 self.distanceXcXe[i][j] = np.abs((self.Xc[i]-self.Xe[j]))
 
 
@@ -118,13 +118,13 @@ def updatePsi(self):
         # print self.pc
         # print self.distanceXc
         newPsicXc = np.exp(-np.sum(self.thetac*np.power(self.distanceXc,self.pc), axis=2))
-        print newPsicXc[0]
+        print(newPsicXc[0])
         self.PsicXc = np.triu(newPsicXc,1)
         self.PsicXc = self.PsicXc + self.PsicXc.T + np.mat(eye(self.nc))+np.multiply(np.mat(eye(self.nc)),np.spacing(1))
         self.UPsicXc = np.linalg.cholesky(self.PsicXc)
         self.UPsicXc = self.UPsicXc.T
-        print self.PsicXc[0]
-        print self.UPsicXc
+        print(self.PsicXc[0])
+        print(self.UPsicXc)
         exit()
 
         newPsicXe = np.exp(-np.sum(self.thetac*np.power(self.distanceXe,self.pc), axis=2))
@@ -149,22 +149,22 @@ def neglnlikehood(self):
         self.muc = c/f
         # This only works if yc is transposed, then its a scalar under two layers of arrays. Correct? Not sure
 
-        print 'y',self.yd.T
+        print('y',self.yd.T)
         a = np.linalg.solve(self.UPsicXe.T, self.yd)
-        print 'a',a
+        print('a',a)
         b = np.linalg.solve(self.UPsicXe, a)
-        print 'b', b
+        print('b', b)
         c = ones([self.ne,1]) * b
-        print 'c', c
+        print('c', c)
 
         d = np.linalg.solve(self.UPsicXe.T, ones([self.ne,1], dtype=float))
-        print d
+        print(d)
 
         e = np.linalg.solve(self.UPsicXe, d)
-        print e
+        print(e)
 
         f = ones([self.ne,1]).T * e
-        print f
+        print(f)
 
         self.mud= c/f
 
@@ -175,9 +175,9 @@ def neglnlikehood(self):
 
 
 
-        print self.ne
-        print self.mud
-        print self.UPsicXe.T
+        print(self.ne)
+        print(self.mud)
+        print(self.UPsicXe.T)
         a = np.linalg.solve(self.UPsicXe.T,(self.yd-ones([self.ne,1])*self.mud))/self.ne
         b = np.linalg.solve(self.UPsicXe, a)
         self.SigmaSqrd=(self.yd-ones([self.ne,1])*self.mud).T* b
@@ -197,7 +197,7 @@ def fe(X):
     return np.power(X[:,0], 2) + np.power(X[:,1], 2)
 
 if __name__=='__main__':
-    import samplingplan
+    from . import samplingplan
     import random
     sp = samplingplan.samplingplan(2)
     X = sp.optimallhc(20)