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Updates to DCProblem and testing.

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This commit is contained in:
Rowan Cockett
2013-11-04 15:11:10 -08:00
parent df51919d81
commit 6f141ecaf2
7 changed files with 129 additions and 61 deletions
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SimPEG/forward/DCProblem.py
+41 -35
View File
@@ -7,6 +7,7 @@ import numpy as np
import scipy.sparse as sp
import scipy.sparse.linalg as linalg
class DCProblem(ModelTransforms.LogModel, Problem):
"""
**DCProblem**
@@ -18,6 +19,11 @@ class DCProblem(ModelTransforms.LogModel, Problem):
super(DCProblem, self).__init__(mesh)
self.mesh.setCellGradBC('neumann')
def reshapeFields(self, u):
if len(u.shape) == 1:
u = u.reshape([-1, self.RHS.shape[1]], order='F')
return u
def createMatrix(self, m):
"""
Makes the matrix A(m) for the DC resistivity problem.
@@ -38,11 +44,25 @@ class DCProblem(ModelTransforms.LogModel, Problem):
A = D*Msig*G
return A.tocsc()
def dpred(self, m, u=None):
"""
Predicted data.
.. math::
d_\\text{pred} = Pu(m)
"""
if u is None:
u = self.field(m)
u = self.reshapeFields(u)
return mkvc(self.P*u)
def field(self, m):
A = self.createMatrix(m)
solve = Solver(A)
phi = solve.solve(self.RHS)
return phi
return mkvc(phi)
def J(self, m, v, u=None):
"""
@@ -69,6 +89,8 @@ class DCProblem(ModelTransforms.LogModel, Problem):
if u is None:
u = self.field(m)
u = self.reshapeFields(u)
P = self.P
D = self.mesh.faceDiv
G = self.mesh.cellGrad
@@ -83,13 +105,18 @@ class DCProblem(ModelTransforms.LogModel, Problem):
dCdm[:, i] = D * ( sdiag( G * ui ) * ( Av_dm * ( mT_dm * v ) ) )
solve = Solver(dCdu)
# solve = linalg.factorized(dCdu)
Jv = - P * solve.solve(dCdm)
return Jv
return mkvc(Jv)
def Jt(self, m, v, u=None):
"""Takes data, turns it into a model..ish"""
if u is None:
u = self.field(m)
u = self.reshapeFields(u)
v = self.reshapeFields(v)
P = self.P
D = self.mesh.faceDiv
G = self.mesh.cellGrad
@@ -147,7 +174,7 @@ if __name__ == '__main__':
import matplotlib.pyplot as plt
# Create the mesh
h1 = np.ones(100)
h1 = np.ones(20)
h2 = np.ones(100)
mesh = TensorMesh([h1,h2])
@@ -156,12 +183,12 @@ if __name__ == '__main__':
sig2 = np.log(0.01)
# Create a synthetic model from a block in a half-space
p0 = [20, 20]
p1 = [50, 50]
p0 = [5, 10]
p1 = [15, 50]
condVals = [sig1, sig2]
mSynth = ModelBuilder.defineBlockConductivity(p0,p1,mesh.gridCC,condVals)
plt.colorbar(mesh.plotImage(mSynth))
# plt.show()
plt.show()
# Set up the projection
nelec = 50
@@ -184,7 +211,9 @@ if __name__ == '__main__':
dobs, Wd = synthetic.createData(mSynth, std=0.05)
u = synthetic.field(mSynth)
# mesh.plotImage(u[:,10], showIt=False)
u = synthetic.reshapeFields(u)
mesh.plotImage(u[:,10])
# plt.show()
# Now set up the problem to do some minimization
problem = DCProblem(mesh)
@@ -194,39 +223,16 @@ if __name__ == '__main__':
problem.std = dobs*0 + 0.05
m0 = mesh.gridCC[:,0]*0+sig2
# Adjoint Test
u = np.random.rand(mesh.nC, problem.RHS.shape[1])
v = np.random.rand(mesh.nC)
w = np.random.rand(*dobs.shape)
Jv = mkvc(problem.J(mSynth, v, u=u))
print mkvc(w).dot(Jv)
print v.dot(problem.Jt(mSynth, w, u=u))
# Check Derivative
dm = np.random.randn(*m0.shape)
for alp in np.logspace(-2,-6, 5):
a = problem.dpred(m0)
b = problem.dpred(m0 + alp*dm)
c = problem.J(m0, alp*dm)
print np.linalg.norm(a-b), np.linalg.norm(a-b+c)
# derChk = lambda m: [problem.dpred(m), problem.J(mSynth,m)]
# checkDerivative(derChk, mSynth)
opt = inverse.InexactGaussNewton(maxIterLS=20, maxIter=3)
opt = inverse.InexactGaussNewton(maxIterLS=20, maxIter=10, tolF=1e-6, tolX=1e-6, tolG=1e-6, maxIterCG=6)
reg = Regularization(mesh)
inv = inverse.Inversion(problem, reg, opt)
inv = inverse.Inversion(problem, reg, opt, beta0=1e4)
# Check Derivative
derChk = lambda m: [inv.dataObj(m), inv.dataObjDeriv(m)]
checkDerivative(derChk, mSynth)
print inv.dataObj(m0)
print inv.dataObj(mSynth)
SimPEG/inverse/Inversion.py
+1 -1
View File
@@ -16,7 +16,7 @@ class Inversion(object):
self.setKwargs(**kwargs)
def setKwargs(self, **kwargs):
# Set the variables, throw an error if they don't exist.
"""Sets key word arguments (kwargs) that are present in the object, throw an error if they don't exist."""
for attr in kwargs:
if hasattr(self, attr):
setattr(self, attr, kwargs[attr])
SimPEG/inverse/Optimize.py
+4 -14
View File
@@ -39,8 +39,7 @@ class Minimize(object):
self.setKwargs(**kwargs)
def setKwargs(self, **kwargs):
"""Sets key word arguments (kwargs) that are present in the object."""
# Set the variables, throw an error if they don't exist.
"""Sets key word arguments (kwargs) that are present in the object, throw an error if they don't exist."""
for attr in kwargs:
if hasattr(self, attr):
setattr(self, attr, kwargs[attr])
@@ -161,7 +160,7 @@ class Minimize(object):
def printInit(self):
"""
printInit is called at the beginning of the optimization routine.
**printInit** is called at the beginning of the optimization routine.
If there is a parent object, printInit will check for a
parent.printInit function and call that.
@@ -177,7 +176,7 @@ class Minimize(object):
def printIter(self):
"""
printIter is called directly after function evaluations.
**printIter** is called directly after function evaluations.
If there is a parent object, printIter will check for a
parent.printIter function and call that.
@@ -191,7 +190,7 @@ class Minimize(object):
def printDone(self):
"""
printDone is called at the end of the optimization routine.
**printDone** is called at the end of the optimization routine.
If there is a parent object, printDone will check for a
parent.printDone function and call that.
@@ -386,12 +385,3 @@ if __name__ == '__main__':
print "xOpt=[%f, %f]" % (xOpt[0], xOpt[1])
xOpt = SteepestDescent(maxIter=30, maxIterLS=15,tolF=1e-10,tolX=1e-10,tolG=1e-10).minimize(Rosenbrock, x0)
print "xOpt=[%f, %f]" % (xOpt[0], xOpt[1])
def simplePass(x):
return np.sin(x), sdiag(np.cos(x))
def simpleFail(x):
return np.sin(x), -sdiag(np.cos(x))
checkDerivative(simplePass, np.random.randn(5), plotIt=False)
checkDerivative(simpleFail, np.random.randn(5), plotIt=False)
SimPEG/tests/TestUtils.py
+31 -4
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@@ -1,12 +1,15 @@
import numpy as np
import matplotlib.pyplot as plt
from pylab import norm
from SimPEG.utils import mkvc
from SimPEG.utils import mkvc, sdiag
from SimPEG import utils
from SimPEG.mesh import TensorMesh, LogicallyOrthogonalMesh
import numpy as np
import unittest
import inspect
happiness = ['The test be workin!', 'You get a gold star!', 'Yay passed!', 'Happy little convergence test!', 'That was easy!', 'Testing is important.', 'You are awesome.', 'Go Test Go!', 'Once upon a time, a happy little test passed.', 'And then everyone was happy.']
sadness = ['No gold star for you.','Try again soon.','Thankfully, persistence is a great substitute for talent.','It might be easier to call this a feature...','Coffee break?', 'Boooooooo :(', 'Testing is important. Do it again.']
class OrderTest(unittest.TestCase):
"""
@@ -157,9 +160,10 @@ class OrderTest(unittest.TestCase):
print '---------------------------------------------'
passTest = np.mean(np.array(order)) > self.tolerance*self._expectedOrder
if passTest:
print ['The test be workin!', 'You get a gold star!', 'Yay passed!', 'Happy little convergence test!', 'That was easy!'][np.random.randint(5)]
print happiness[np.random.randint(len(happiness))]
else:
print 'Failed to pass test on ' + self._meshType + '.'
print sadness[np.random.randint(len(sadness))]
print ''
self.assertTrue(passTest)
@@ -222,7 +226,11 @@ def checkDerivative(fctn, x0, num=7, plotIt=True, dx=None):
for i in range(num):
Jt = fctn(x0+t[i]*dx)
E0[i] = l2norm(Jt[0]-Jc[0]) # 0th order Taylor
E1[i] = l2norm(Jt[0]-Jc[0]-t[i]*Jc[1].dot(dx)) # 1st order Taylor
if inspect.isfunction(Jc[1]):
E1[i] = l2norm(Jt[0]-Jc[0]-t[i]*Jc[1](dx)) # 1st order Taylor
else:
# We assume it is a numpy.ndarray
E1[i] = l2norm(Jt[0]-Jc[0]-t[i]*Jc[1].dot(dx)) # 1st order Taylor
order0 = np.log10(E0[:-1]/E0[1:])
order1 = np.log10(E1[:-1]/E1[1:])
print "%d\t%1.2e\t%1.3e\t\t%1.3e\t\t%1.3f" % (i, t[i], E0[i], E1[i], np.nan if i == 0 else order1[i-1])
@@ -238,9 +246,12 @@ def checkDerivative(fctn, x0, num=7, plotIt=True, dx=None):
passTest = belowTol or correctOrder
if passTest:
print "%s PASS! %s\n" % ('='*25, '='*25)
print "%s PASS! %s" % ('='*25, '='*25)
print happiness[np.random.randint(len(happiness))]+'\n'
else:
print "%s\n%s FAIL! %s\n%s" % ('*'*57, '<'*25, '>'*25, '*'*57)
print sadness[np.random.randint(len(sadness))]+'\n'
if plotIt:
plt.figure()
@@ -254,3 +265,19 @@ def checkDerivative(fctn, x0, num=7, plotIt=True, dx=None):
plt.show()
return passTest
if __name__ == '__main__':
def simplePass(x):
return np.sin(x), sdiag(np.cos(x))
def simpleFunction(x):
return np.sin(x), lambda xi: sdiag(np.cos(x))*xi
def simpleFail(x):
return np.sin(x), -sdiag(np.cos(x))
checkDerivative(simplePass, np.random.randn(5), plotIt=False)
checkDerivative(simpleFunction, np.random.randn(5), plotIt=False)
checkDerivative(simpleFail, np.random.randn(5), plotIt=False)
SimPEG/tests/test_forward_DCproblem.py
+20 -6
View File
@@ -3,9 +3,11 @@ import unittest
from SimPEG.mesh import TensorMesh
from SimPEG.utils import ModelBuilder, sdiag
from SimPEG.forward import Problem, SyntheticProblem
from SimPEG.forward.DCProblem import DCProblem, DCutils
from SimPEG.forward.DCProblem import *
from TestUtils import checkDerivative
from scipy.sparse.linalg import dsolve
from SimPEG.regularization import Regularization
from SimPEG import inverse
class DCProblemTests(unittest.TestCase):
@@ -34,7 +36,7 @@ class DCProblemTests(unittest.TestCase):
elecend = 0.5+spacelec*(nelec-1)
elecLocR = np.linspace(elecini, elecend, nelec)
rxmidLoc = (elecLocR[0:nelec-1]+elecLocR[1:nelec])*0.5
q, Q, rxmidloc = DCutils.genTxRxmat(nelec, spacelec, surfloc, elecini, mesh)
q, Q, rxmidloc = genTxRxmat(nelec, spacelec, surfloc, elecini, mesh)
P = Q.T
# Create some data
@@ -52,22 +54,27 @@ class DCProblemTests(unittest.TestCase):
problem.RHS = q
problem.W = Wd
problem.dobs = dobs
problem.std = dobs*0 + 0.05
opt = inverse.InexactGaussNewton(maxIterLS=20, maxIter=10, tolF=1e-6, tolX=1e-6, tolG=1e-6, maxIterCG=6)
reg = Regularization(mesh)
inv = inverse.Inversion(problem, reg, opt, beta0=1e4)
self.inv = inv
self.reg = reg
self.p = problem
self.mesh = mesh
self.m0 = mSynth
self.dobs = dobs
def test_misfit(self):
print 'SimPEG.forward.DCProblem: Testing Misfit'
derChk = lambda m: [self.p.misfit(m), self.p.misfitDeriv(m)]
derChk = lambda m: [self.p.dpred(m), lambda mx: self.p.J(self.m0, mx)]
passed = checkDerivative(derChk, self.m0, plotIt=False)
self.assertTrue(passed)
def test_adjoint(self):
# Adjoint Test
u = np.random.rand(self.mesh.nC)
u = np.random.rand(self.mesh.nC*self.p.RHS.shape[1])
v = np.random.rand(self.mesh.nC)
w = np.random.rand(self.dobs.shape[0])
wtJv = w.dot(self.p.J(self.m0, v, u=u))
@@ -75,6 +82,13 @@ class DCProblemTests(unittest.TestCase):
passed = (wtJv - vtJtw) < 1e-10
self.assertTrue(passed)
def test_dataObj(self):
derChk = lambda m: [self.inv.dataObj(m), self.inv.dataObjDeriv(m)]
checkDerivative(derChk, self.m0, plotIt=False)
def test_modelObj(self):
derChk = lambda m: [self.reg.modelObj(m), self.reg.modelObjDeriv(m)]
checkDerivative(derChk, self.m0, plotIt=False)
if __name__ == '__main__':
SimPEG/tests/test_forward_problem.py
+9 -1
View File
@@ -2,6 +2,7 @@ import numpy as np
import unittest
from SimPEG.mesh import TensorMesh
from SimPEG.forward import Problem
from SimPEG.regularization import Regularization
from TestUtils import checkDerivative
from scipy.sparse.linalg import dsolve
@@ -15,7 +16,7 @@ class ProblemTests(unittest.TestCase):
c = np.array([1, 4])
self.mesh2 = TensorMesh([a, b], np.array([3, 5]))
self.p2 = Problem(self.mesh2)
self.reg = Regularization(self.mesh2)
def test_modelTransform(self):
print 'SimPEG.forward.Problem: Testing Model Transform'
@@ -23,6 +24,13 @@ class ProblemTests(unittest.TestCase):
passed = checkDerivative(lambda m : [self.p2.modelTransform(m), self.p2.modelTransformDeriv(m)], m, plotIt=False)
self.assertTrue(passed)
def test_regularization(self):
derChk = lambda m: [self.reg.modelObj(m), self.reg.modelObjDeriv(m)]
mSynth = np.random.randn(self.mesh2.nC)
checkDerivative(derChk, mSynth, plotIt=False)
if __name__ == '__main__':
unittest.main()
SimPEG/tests/test_utils.py
+23
View File
@@ -1,6 +1,28 @@
import numpy as np
import unittest
from SimPEG.utils import mkvc, ndgrid, indexCube, sdiag, inv3X3BlockDiagonal, inv2X2BlockDiagonal
from SimPEG.tests import checkDerivative
class TestCheckDerivative(unittest.TestCase):
def test_simplePass(self):
def simplePass(x):
return np.sin(x), sdiag(np.cos(x))
passed = checkDerivative(simplePass, np.random.randn(5), plotIt=False)
self.assertTrue(passed, True)
def test_simpleFunction(self):
def simpleFunction(x):
return np.sin(x), lambda xi: sdiag(np.cos(x))*xi
passed = checkDerivative(simpleFunction, np.random.randn(5), plotIt=False)
self.assertTrue(passed, True)
def test_simpleFail(self):
def simpleFail(x):
return np.sin(x), -sdiag(np.cos(x))
passed = checkDerivative(simpleFail, np.random.randn(5), plotIt=False)
self.assertTrue(not passed, True)
class TestSequenceFunctions(unittest.TestCase):
@@ -85,5 +107,6 @@ class TestSequenceFunctions(unittest.TestCase):
self.assertTrue(np.linalg.norm(Z3.todense().ravel(), 2) < 1e-12)
if __name__ == '__main__':
unittest.main()