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Update directives

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Add IRLS example
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D Fournier
2016-04-03 17:18:39 -07:00
parent 16d62a6d0a
commit 8f73b2e7be
4 changed files with 160 additions and 99 deletions
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SimPEG/Directives.py
+61 -40
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@@ -237,39 +237,41 @@ class SaveOutputDictEveryIteration(_SaveEveryIteration):
# Save the file as a npz
np.savez('{:03d}-{:s}'.format(self.opt.iter,self.fileName), iter=self.opt.iter, beta=self.invProb.beta, phi_d=self.invProb.phi_d, phi_m=self.invProb.phi_m, phi_ms=phi_ms, phi_mx=phi_mx, phi_my=phi_my, phi_mz=phi_mz,f=self.opt.f, m=self.invProb.curModel,dpred=self.invProb.dpred)
class SaveOutputDictEveryIteration(_SaveEveryIteration):
"""SaveOutputDictEveryIteration
A directive that saves some relevant information from the inversion run to a numpy .npz dictionary file (see numpy.savez function for further info).
"""
def initialize(self):
print "SimPEG.SaveOutputDictEveryIteration will save your inversion progress as dictionary: '%s-###.npz'"%self.fileName
def endIter(self):
# Save the data.
ms = self.reg.Ws * ( self.reg.mapping * (self.invProb.curModel - self.reg.mref) )
phi_ms = 0.5*ms.dot(ms)
if self.reg.mrefInSmooth == True:
mref = self.reg.mref
else:
mref = 0
mx = self.reg.Wx * ( self.reg.mapping * (self.invProb.curModel - mref) )
phi_mx = 0.5 * mx.dot(mx)
if self.prob.mesh.dim==2:
my = self.reg.Wy * ( self.reg.mapping * (self.invProb.curModel - mref) )
phi_my = 0.5 * my.dot(my)
else:
phi_my = 'NaN'
if self.prob.mesh.dim==3 and 'CYL' not in self.prob.mesh._meshType:
mz = self.reg.Wz * ( self.reg.mapping * (self.invProb.curModel - mref) )
phi_mz = 0.5 * mz.dot(mz)
else:
phi_mz = 'NaN'
# Save the file as a npz
np.savez('{:s}-{:03d}'.format(self.fileName,self.opt.iter), iter=self.opt.iter, beta=self.invProb.beta, phi_d=self.invProb.phi_d, phi_m=self.invProb.phi_m, phi_ms=phi_ms, phi_mx=phi_mx, phi_my=phi_my, phi_mz=phi_mz,f=self.opt.f, m=self.invProb.curModel,dpred=self.invProb.dpred)
#==============================================================================
# class SaveOutputDictEveryIteration(_SaveEveryIteration):
# """SaveOutputDictEveryIteration
# A directive that saves some relevant information from the inversion run to a numpy .npz dictionary file (see numpy.savez function for further info).
# """
#
# def initialize(self):
# print "SimPEG.SaveOutputDictEveryIteration will save your inversion progress as dictionary: '%s-###.npz'"%self.fileName
#
# def endIter(self):
# # Save the data.
# ms = self.reg.Ws * ( self.reg.mapping * (self.invProb.curModel - self.reg.mref) )
# phi_ms = 0.5*ms.dot(ms)
# if self.reg.mrefInSmooth == True:
# mref = self.reg.mref
# else:
# mref = 0
# mx = self.reg.Wx * ( self.reg.mapping * (self.invProb.curModel - mref) )
# phi_mx = 0.5 * mx.dot(mx)
# if self.prob.mesh.dim==2:
# my = self.reg.Wy * ( self.reg.mapping * (self.invProb.curModel - mref) )
# phi_my = 0.5 * my.dot(my)
# else:
# phi_my = 'NaN'
# if self.prob.mesh.dim==3 and 'CYL' not in self.prob.mesh._meshType:
# mz = self.reg.Wz * ( self.reg.mapping * (self.invProb.curModel - mref) )
# phi_mz = 0.5 * mz.dot(mz)
# else:
# phi_mz = 'NaN'
#
#
# # Save the file as a npz
# np.savez('{:s}-{:03d}'.format(self.fileName,self.opt.iter), iter=self.opt.iter, beta=self.invProb.beta, phi_d=self.invProb.phi_d, phi_m=self.invProb.phi_m, phi_ms=phi_ms, phi_mx=phi_mx, phi_my=phi_my, phi_mz=phi_mz,f=self.opt.f, m=self.invProb.curModel,dpred=self.invProb.dpred)
#
#==============================================================================
# class UpdateReferenceModel(Parameter):
@@ -295,6 +297,8 @@ class update_IRLS(InversionDirective):
# Scale the regularization for changes in norm
if getattr(self, 'phi_m_last', None) is not None:
self.reg.curModel = self.invProb.curModel
self.reg.gamma = 1.
phim_new = self.reg.eval(self.invProb.curModel)
self.gamma = self.phi_m_last / phim_new
@@ -320,22 +324,39 @@ class update_IRLS(InversionDirective):
# Update the model used for the IRLS weights
self.reg.curModel = self.invProb.curModel
# Update the pre-conditioner
diagA = np.sum(self.prob.G**2.,axis=0) + self.invProb.beta*(self.reg.W.T*self.reg.W).diagonal() * (self.reg.mapping * np.ones(self.reg.curModel.size))**2.
PC = Utils.sdiag(diagA**-1.)
self.opt.approxHinv = PC
# Temporarely set gamma to 1.
self.reg.gamma = 1.
# Compute change in model objective function and update scaling
phim_new = self.reg.eval(self.invProb.curModel)
self.reg.gamma = self.phi_m_last / phim_new
# TO DO: Re-scale beta if too much change in misfit
self.invProb.beta = self.invProb.beta * self.phi_d_last / self.invProb.phi_d
# TO DO: Check optimization class, data misfit not matching reality
#dpred = self.prob.fields(self.invProb.curModel)
#phid = self.invProb.dmisfit.eval(self.invProb.curModel)
#print self.survey.std[0]
#print phid
#print self.invProb.phi_d
#print self.invProb.phi_d_last
self.invProb.beta = self.invProb.beta * self.survey.nD*0.5 / self.invProb.phi_d
class update_lin_PreCond(InversionDirective):
def endIter(self):
# Cool the threshold parameter
if getattr(self.opt, 'approxHinv', None) is not None:
# Update the pre-conditioner
diagA = np.sum(self.prob.G**2.,axis=0) + self.invProb.beta*(self.reg.W.T*self.reg.W).diagonal() * (self.reg.mapping * np.ones(self.reg.curModel.size))**2.
PC = Utils.sdiag(diagA**-1.)
self.opt.approxHinv = PC
print 'Updated pre-cond'
#==============================================================================
# import pylab as plt
# plt.figure()