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Futurize 1, futurize 2, pasteurize.

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This commit is contained in:
Brendan Smithyman
2016-07-16 14:17:02 -05:00
parent 362975d2bd
commit ca8d8f8c2d
197 changed files with 2618 additions and 1235 deletions
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tests/base/test_optimizers.py
+17 -11
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@@ -1,3 +1,9 @@
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import division
from __future__ import absolute_import
from future import standard_library
standard_library.install_aliases()
import unittest
from SimPEG import Solver
from SimPEG.Mesh import TensorMesh
@@ -19,16 +25,16 @@ class TestOptimizers(unittest.TestCase):
GN = Optimization.GaussNewton()
xopt = GN.minimize(Rosenbrock,np.array([0,0]))
x_true = np.array([1.,1.])
print 'xopt: ', xopt
print 'x_true: ', x_true
print('xopt: ', xopt)
print('x_true: ', x_true)
self.assertTrue(np.linalg.norm(xopt-x_true,2) < TOL, True)
def test_GN_quadratic(self):
GN = Optimization.GaussNewton()
xopt = GN.minimize(getQuadratic(self.A,self.b),np.array([0,0]))
x_true = np.array([5.,5.])
print 'xopt: ', xopt
print 'x_true: ', x_true
print('xopt: ', xopt)
print('x_true: ', x_true)
self.assertTrue(np.linalg.norm(xopt-x_true,2) < TOL, True)
def test_ProjGradient_quadraticBounded(self):
@@ -36,8 +42,8 @@ class TestOptimizers(unittest.TestCase):
PG.lower, PG.upper = -2, 2
xopt = PG.minimize(getQuadratic(self.A,self.b),np.array([0,0]))
x_true = np.array([2.,2.])
print 'xopt: ', xopt
print 'x_true: ', x_true
print('xopt: ', xopt)
print('x_true: ', x_true)
self.assertTrue(np.linalg.norm(xopt-x_true,2) < TOL, True)
def test_ProjGradient_quadratic1Bound(self):
@@ -46,8 +52,8 @@ class TestOptimizers(unittest.TestCase):
PG.lower, PG.upper = -2, 2
xopt = PG.minimize(getQuadratic(self.A,myB),np.array([0,0]))
x_true = np.array([2.,-1.])
print 'xopt: ', xopt
print 'x_true: ', x_true
print('xopt: ', xopt)
print('x_true: ', x_true)
self.assertTrue(np.linalg.norm(xopt-x_true,2) < TOL, True)
def test_NewtonRoot(self):
@@ -55,9 +61,9 @@ class TestOptimizers(unittest.TestCase):
x = np.array([np.pi-0.3, np.pi+0.1, 0])
xopt = Optimization.NewtonRoot(comments=False).root(fun,x)
x_true = np.array([np.pi,np.pi,0])
print 'Newton Root Finding'
print 'xopt: ', xopt
print 'x_true: ', x_true
print('Newton Root Finding')
print('xopt: ', xopt)
print('x_true: ', x_true)
self.assertTrue(np.linalg.norm(xopt-x_true,2) < TOL, True)
if __name__ == '__main__':