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Possibly deal with a good chunk of the old_divs

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This commit is contained in:
Brendan Smithyman
2016-07-17 16:02:43 -05:00
parent 7189ec5b2f
commit f7a70aa6a7
85 changed files with 369 additions and 454 deletions
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SimPEG/Regularization.py
+15 -16
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@@ -5,7 +5,6 @@ from __future__ import unicode_literals
from future import standard_library
standard_library.install_aliases()
from builtins import object
from past.utils import old_div
from . import Utils, Maps, Mesh
import numpy as np
import scipy.sparse as sp
@@ -143,7 +142,7 @@ class RegularizationMesh(object):
:return: averaging matrix from active x-faces to active cell centers
"""
if getattr(self, '_aveCC2Fx', None) is None:
self._aveCC2Fx = Utils.sdiag(old_div(1.,(self.aveFx2CC.T).sum(1))) * self.aveFx2CC.T
self._aveCC2Fx = Utils.sdiag(1./(self.aveFx2CC.T).sum(1)) * self.aveFx2CC.T
return self._aveCC2Fx
@property
@@ -165,7 +164,7 @@ class RegularizationMesh(object):
:return: averaging matrix from active y-faces to active cell centers
"""
if getattr(self, '_aveCC2Fy', None) is None:
self._aveCC2Fy = Utils.sdiag(old_div(1.,(self.aveFy2CC.T).sum(1))) * self.aveFy2CC.T
self._aveCC2Fy = Utils.sdiag(1./(self.aveFy2CC.T).sum(1)) * self.aveFy2CC.T
return self._aveCC2Fy
@property
@@ -187,7 +186,7 @@ class RegularizationMesh(object):
:return: averaging matrix from active z-faces to active cell centers
"""
if getattr(self, '_aveCC2Fz', None) is None:
self._aveCC2Fz = Utils.sdiag(old_div(1.,(self.aveFz2CC.T).sum(1))) * self.aveFz2CC.T
self._aveCC2Fz = Utils.sdiag(1./(self.aveFz2CC.T).sum(1)) * self.aveFz2CC.T
return self._aveCC2Fz
@property
@@ -899,28 +898,28 @@ class Tikhonov(Simple):
class Sparse(Simple):
"""
The regularization is:
.. math::
R(m) = \\frac{1}{2}\mathbf{(m-m_\\text{ref})^\\top W^\\top R^\\top R W(m-m_\\text{ref})}
where the IRLS weight
.. math::
R = \eta TO FINISH LATER!!!
So the derivative is straight forward:
.. math::
R(m) = \mathbf{W^\\top R^\\top R W (m-m_\\text{ref})}
The IRLS weights are recomputed after each beta solves.
It is strongly recommended to do a few Gauss-Newton iterations
before updating.
"""
# set default values
eps_p = 1e-1 # Threshold value for the model norm
eps_q = 1e-1 # Threshold value for the model gradient norm
@@ -1003,7 +1002,7 @@ class Sparse(Simple):
def R(self, f_m , eps, exponent):
# Eta scaling is important for mix-norms...do not mess with it
eta = (eps**(1.-old_div(exponent,2.)))**0.5
r = old_div(eta, (f_m**2.+ eps**2.)**(old_div((1.-old_div(exponent,2.)),2.)))
eta = (eps**(1.-exponent/2.))**0.5
r = eta / (f_m**2.+ eps**2.)**((1.-exponent/2.)/2.)
return r