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Commits
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33ea89702e |
@@ -271,7 +271,8 @@ class Update_IRLS(InversionDirective):
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self.reg.curModel = self.invProb.curModel
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self.reg.curModel = self.invProb.curModel
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self.reg.gamma = self.gamma
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self.reg.gamma = self.gamma
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self.reg._W = None
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if getattr(self, 'phi_d_last', None) is None:
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if getattr(self, 'phi_d_last', None) is None:
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self.phi_d_last = self.invProb.phi_d
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self.phi_d_last = self.invProb.phi_d
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@@ -287,7 +288,8 @@ class Update_IRLS(InversionDirective):
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# Get phi_m at the end of current iteration
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# Get phi_m at the end of current iteration
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self.phi_m_last = self.invProb.phi_m_last
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self.phi_m_last = self.invProb.phi_m_last
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self.reg._W = None
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# Update the model used for the IRLS weights
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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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self.reg.curModel = self.invProb.curModel
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@@ -296,10 +298,10 @@ class Update_IRLS(InversionDirective):
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# Compute new model objective function value
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# Compute new model objective function value
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phim_new = self.reg.eval(self.invProb.curModel)
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phim_new = self.reg.eval(self.invProb.curModel)
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# Update gamma to scale the regularization between IRLS iterations
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# Update gamma to scale the regularization between IRLS iterations
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self.reg.gamma = self.phi_m_last / phim_new
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self.reg.gamma = self.phi_m_last / phim_new
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# Set the weighting matrix to None so that it is recomputed next time
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# Set the weighting matrix to None so that it is recomputed next time
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# it is called in the inversion
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# it is called in the inversion
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self.reg._W = None
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self.reg._W = None
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@@ -665,7 +665,6 @@ class Sparse(Simple):
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else:
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else:
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f_m = self.mapping * (self.curModel - self.reg.mref)
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f_m = self.mapping * (self.curModel - self.reg.mref)
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self.rs = self.R(f_m , self.eps_p, self.norms[0])
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self.rs = self.R(f_m , self.eps_p, self.norms[0])
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#print "Min rs: " + str(np.max(self.rs)) + "Max rs: " + str(np.min(self.rs))
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self.Rs = Utils.sdiag( self.rs )
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self.Rs = Utils.sdiag( self.rs )
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return Utils.sdiag((self.regmesh.vol*self.alpha_s*self.gamma*self.wght)**0.5)*self.Rs
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return Utils.sdiag((self.regmesh.vol*self.alpha_s*self.gamma*self.wght)**0.5)*self.Rs
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@@ -729,6 +728,7 @@ class Sparse(Simple):
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def W(self):
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def W(self):
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"""Full regularization matrix W"""
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"""Full regularization matrix W"""
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if getattr(self, '_W', None) is None:
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if getattr(self, '_W', None) is None:
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wlist = (self.Wsmall, self.Wsmooth)
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wlist = (self.Wsmall, self.Wsmooth)
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self._W = sp.vstack(wlist)
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self._W = sp.vstack(wlist)
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return self._W
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return self._W
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