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some cleanup inside of sparse regularization

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Lindsey Heagy
2016-03-10 14:25:53 -08:00
parent 33c9059e4e
commit ef4513bcd4
3 changed files with 57 additions and 65 deletions
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SimPEG/Directives.py
+26 -28
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@@ -285,45 +285,43 @@ class SaveOutputDictEveryIteration(_SaveEveryIteration):
class update_IRLS(InversionDirective):
m = None
eps_min = None
factor = None
gamma = None
phi_m_last = None
eps_min = None
factor = None
gamma = None
phi_m_last = None
def initialize(self):
def initialize(self):
# Scale the regularization for changes in norm
if getattr(self, 'phi_m_last', None) is not None:
self.reg.gamma = 1.
phim_new = self.reg.eval(self.invProb.curModel)
self.gamma = self.phi_m_last / phim_new
# Scale the regularization for changes in norm
if getattr(self, 'phi_m_last', None) is not None:
self.reg.gamma = 1.
phim_new = self.reg.eval(self.invProb.curModel)
self.gamma = self.phi_m_last / phim_new
self.reg.gamma = self.gamma
self.reg.gamma = self.gamma
def endIter(self):
# Cool the threshold parameter
if getattr(self, 'factor', None) is not None:
eps = self.reg.eps / self.factor
def endIter(self):
# Cool the threshold parameter
if getattr(self, 'factor', None) is not None:
eps = self.reg.eps / self.factor
if getattr(self, 'eps_min', None) is not None:
self.reg.eps = np.max([self.eps_min,eps])
else:
self.reg.eps = eps
if getattr(self, 'eps_min', None) is not None:
self.reg.eps = np.max([self.eps_min,eps])
else:
self.reg.eps = eps
# Update the model used for the IRLS weights
if getattr(self, 'm', None) is None:
self.reg.m = self.invProb.curModel
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.m.size))**2.
PC = Utils.sdiag(diagA**-1.)
# 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.m.size))**2.
PC = Utils.sdiag(diagA**-1.)
self.opt.approxHinv = PC
self.opt.approxHinv = PC
phim_new = self.reg.eval(self.invProb.curModel)
self.reg.gamma = self.reg.gamma * self.invProb.phi_m_last / phim_new
phim_new = self.reg.eval(self.invProb.curModel)
self.reg.gamma = self.reg.gamma * self.invProb.phi_m_last / phim_new
#==============================================================================
# import pylab as plt