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https://github.com/wassname/simpeg.git
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rowanc1
67b067d938
Added Parameter to Utils, which hints at where we are going with functions as parameters.
65 lines
2.2 KiB
Python
65 lines
2.2 KiB
Python
import unittest
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from SimPEG import Solver
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from SimPEG.Mesh import TensorMesh
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from SimPEG.Utils import sdiag
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import numpy as np
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import scipy.sparse as sp
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from SimPEG import Optimization
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from SimPEG.Tests import getQuadratic, Rosenbrock
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TOL = 1e-2
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class TestOptimizers(unittest.TestCase):
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def setUp(self):
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self.A = sp.identity(2).tocsr()
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self.b = np.array([-5,-5])
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def test_GN_Rosenbrock(self):
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GN = Optimization.GaussNewton()
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xopt = GN.minimize(Rosenbrock,np.array([0,0]))
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x_true = np.array([1.,1.])
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print 'xopt: ', xopt
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print 'x_true: ', x_true
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self.assertTrue(np.linalg.norm(xopt-x_true,2) < TOL, True)
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def test_GN_quadratic(self):
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GN = Optimization.GaussNewton()
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xopt = GN.minimize(getQuadratic(self.A,self.b),np.array([0,0]))
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x_true = np.array([5.,5.])
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print 'xopt: ', xopt
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print 'x_true: ', x_true
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self.assertTrue(np.linalg.norm(xopt-x_true,2) < TOL, True)
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def test_ProjGradient_quadraticBounded(self):
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PG = Optimization.ProjectedGradient(debug=True)
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PG.lower, PG.upper = -2, 2
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xopt = PG.minimize(getQuadratic(self.A,self.b),np.array([0,0]))
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x_true = np.array([2.,2.])
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print 'xopt: ', xopt
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print 'x_true: ', x_true
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self.assertTrue(np.linalg.norm(xopt-x_true,2) < TOL, True)
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def test_ProjGradient_quadratic1Bound(self):
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myB = np.array([-5,1])
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PG = Optimization.ProjectedGradient()
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PG.lower, PG.upper = -2, 2
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xopt = PG.minimize(getQuadratic(self.A,myB),np.array([0,0]))
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x_true = np.array([2.,-1.])
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print 'xopt: ', xopt
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print 'x_true: ', x_true
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self.assertTrue(np.linalg.norm(xopt-x_true,2) < TOL, True)
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def test_NewtonRoot(self):
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fun = lambda x, return_g=True: np.sin(x) if not return_g else ( np.sin(x), sdiag( np.cos(x) ) )
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x = np.array([np.pi-0.3, np.pi+0.1, 0])
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xopt = Optimization.NewtonRoot(comments=False).root(fun,x)
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x_true = np.array([np.pi,np.pi,0])
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print 'Newton Root Finding'
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print 'xopt: ', xopt
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print 'x_true: ', x_true
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self.assertTrue(np.linalg.norm(xopt-x_true,2) < TOL, True)
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if __name__ == '__main__':
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unittest.main()
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