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Merge branch 'feat/sparse-regularization' into patch/sparse-dcip
# Conflicts: # SimPEG/Examples/__init__.py # SimPEG/Optimization.py
This commit is contained in:
Lindsey Heagy
+84
-46
@@ -237,39 +237,41 @@ class SaveOutputDictEveryIteration(_SaveEveryIteration):
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# Save the file as a npz
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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)
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class SaveOutputDictEveryIteration(_SaveEveryIteration):
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"""SaveOutputDictEveryIteration
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A directive that saves some relevant information from the inversion run to a numpy .npz dictionary file (see numpy.savez function for further info).
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"""
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def initialize(self):
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print "SimPEG.SaveOutputDictEveryIteration will save your inversion progress as dictionary: '%s-###.npz'"%self.fileName
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def endIter(self):
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# Save the data.
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ms = self.reg.Ws * ( self.reg.mapping * (self.invProb.curModel - self.reg.mref) )
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phi_ms = 0.5*ms.dot(ms)
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if self.reg.mrefInSmooth == True:
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mref = self.reg.mref
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else:
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mref = 0
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mx = self.reg.Wx * ( self.reg.mapping * (self.invProb.curModel - mref) )
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phi_mx = 0.5 * mx.dot(mx)
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if self.prob.mesh.dim==2:
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my = self.reg.Wy * ( self.reg.mapping * (self.invProb.curModel - mref) )
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phi_my = 0.5 * my.dot(my)
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else:
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phi_my = 'NaN'
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if self.prob.mesh.dim==3 and 'CYL' not in self.prob.mesh._meshType:
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mz = self.reg.Wz * ( self.reg.mapping * (self.invProb.curModel - mref) )
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phi_mz = 0.5 * mz.dot(mz)
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else:
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phi_mz = 'NaN'
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# Save the file as a npz
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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)
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#==============================================================================
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# class SaveOutputDictEveryIteration(_SaveEveryIteration):
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# """SaveOutputDictEveryIteration
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# A directive that saves some relevant information from the inversion run to a numpy .npz dictionary file (see numpy.savez function for further info).
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# """
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#
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# def initialize(self):
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# print "SimPEG.SaveOutputDictEveryIteration will save your inversion progress as dictionary: '%s-###.npz'"%self.fileName
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#
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# def endIter(self):
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# # Save the data.
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# ms = self.reg.Ws * ( self.reg.mapping * (self.invProb.curModel - self.reg.mref) )
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# phi_ms = 0.5*ms.dot(ms)
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# if self.reg.mrefInSmooth == True:
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# mref = self.reg.mref
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# else:
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# mref = 0
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# mx = self.reg.Wx * ( self.reg.mapping * (self.invProb.curModel - mref) )
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# phi_mx = 0.5 * mx.dot(mx)
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# if self.prob.mesh.dim==2:
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# my = self.reg.Wy * ( self.reg.mapping * (self.invProb.curModel - mref) )
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# phi_my = 0.5 * my.dot(my)
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# else:
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# phi_my = 'NaN'
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# if self.prob.mesh.dim==3 and 'CYL' not in self.prob.mesh._meshType:
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# mz = self.reg.Wz * ( self.reg.mapping * (self.invProb.curModel - mref) )
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# phi_mz = 0.5 * mz.dot(mz)
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# else:
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# phi_mz = 'NaN'
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#
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#
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# # Save the file as a npz
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# 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)
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#
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#==============================================================================
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# class UpdateReferenceModel(Parameter):
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@@ -295,6 +297,8 @@ class update_IRLS(InversionDirective):
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# Scale the regularization for changes in norm
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if getattr(self, 'phi_m_last', None) is not None:
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self.reg.curModel = self.invProb.curModel
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self.reg.gamma = 1.
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phim_new = self.reg.eval(self.invProb.curModel)
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self.gamma = self.phi_m_last / phim_new
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@@ -320,25 +324,59 @@ class update_IRLS(InversionDirective):
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# Update the model used for the IRLS weights
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self.reg.curModel = self.invProb.curModel
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# Update the pre-conditioner
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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.
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PC = Utils.sdiag(diagA**-1.)
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self.opt.approxHinv = PC
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# Temporarely set gamma to 1.
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self.reg.gamma = 1.
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# Compute change in model objective function and update scaling
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phim_new = self.reg.eval(self.invProb.curModel)
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self.reg.gamma = self.phi_m_last / phim_new
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# TO DO: Re-scale beta if too much change in misfit
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self.invProb.beta = self.invProb.beta * self.phi_d_last / self.invProb.phi_d
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#==============================================================================
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# import pylab as plt
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# plt.figure()
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# ax = plt.subplot(221)
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# self.prob.mesh.plotSlice(self.invProb.curModel, ax = ax, normal = 'Z', ind=-5, clim = (0, 0.005))
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#==============================================================================
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# TO DO: Check optimization class, data misfit not matching reality
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#dpred = self.prob.fields(self.invProb.curModel)
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#phid = self.invProb.dmisfit.eval(self.invProb.curModel)
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#print self.survey.std[0]
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#print phid
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#print self.invProb.phi_d
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#print self.invProb.phi_d_last
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self.invProb.beta = self.invProb.beta * self.survey.nD*0.5 / self.invProb.phi_d
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class update_lin_PreCond(InversionDirective):
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def endIter(self):
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# Cool the threshold parameter
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if getattr(self.opt, 'approxHinv', None) is not None:
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# Update the pre-conditioner
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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.
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PC = Utils.sdiag(diagA**-1.)
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self.opt.approxHinv = PC
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print 'Updated pre-cond'
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class update_Wj(InversionDirective):
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"""
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Create approx-sensitivity base weighting
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"""
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k = None
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def endIter(self):
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if self.opt.iter == 2:
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m = self.invProb.curModel
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if self.k is None:
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self.k = int(self.survey.nD/10)
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def JtJv(v):
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Jv = self.prob.Jvec(m, v)
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return self.prob.Jtvec(m,Jv)
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JtJdiag = Utils.diagEst(JtJv,len(m),k=self.k)
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JtJdiag = JtJdiag / max(JtJdiag)
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self.reg.wght = JtJdiag
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