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pointers to parent. rename _iter to iter

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rowanc1
2014-01-24 08:20:29 -07:00
parent 4d9f87807e
commit 56dc9a5591
6 changed files with 85 additions and 49 deletions
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SimPEG/Inversion.py
+1 -1
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@@ -4,7 +4,7 @@ from Optimization import Remember, IterationPrinters, StoppingCriteria
class BaseInversion(object):
"""BaseInversion(prob, reg, opt, data, **kwargs)
"""BaseInversion(objFunc, opt, **kwargs)
"""
__metaclass__ = Utils.Save.Savable
SimPEG/ObjFunction.py
+21 -6
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@@ -1,16 +1,16 @@
from SimPEG import Utils, np, sp
class BaseObjFunction(object):
"""docstring for BaseObjFunction"""
"""BaseObjFunction(data, reg, **kwargs)"""
__metaclass__ = Utils.Save.Savable
beta = Utils.ParameterProperty('beta', default=None, doc='Regularization trade-off parameter')
beta = Utils.ParameterProperty('beta', default=1, doc='Regularization trade-off parameter')
debug = False #: Print debugging information
counter = None #: Set this to a SimPEG.Utils.Counter() if you want to count things
name = 'BaseObjFunction' #: Name of the objective function
name = 'Base Objective Function' #: Name of the objective function
u_current = None #: The most current evaluated field
m_current = None #: The most current model
@@ -25,12 +25,27 @@ class BaseObjFunction(object):
print 'Objective function has switched to a new parent!'
self._parent = p
@property
def inv(self): return self.parent
@property
def objFunc(self): return self
@property
def opt(self): return getattr(self.parent,'opt',None)
@property
def prob(self): return self.data.prob
@property
def mesh(self): return self.data.prob.mesh
@property
def model(self): return self.data.prob.model
def __init__(self, data, reg, **kwargs):
Utils.setKwargs(self, **kwargs)
self.data = data
self.reg = reg
self.reg.parent = self
@Utils.callHooks('startup')
@@ -41,7 +56,7 @@ class BaseObjFunction(object):
"""
if self.debug: print 'Calling ObjFunction.startup'
if not hasattr(self.reg, '_mref'):
if self.reg.mref is None:
print 'Regularization has not set mref. SimPEG will set it to m0.'
self.reg.mref = m0
@@ -226,8 +241,8 @@ class BetaSchedule(Utils.Parameter):
self.beta = self.estimateBeta0()
opt = self.parent.parent.opt
if opt._iter > 0 and opt._iter % self.coolingRate == 0:
if self.debug: print 'BetaSchedule is cooling Beta. Iteration: %d' % opt._iter
if opt.iter > 0 and opt.iter % self.coolingRate == 0:
if self.debug: print 'BetaSchedule is cooling Beta. Iteration: %d' % opt.iter
self.beta /= self.coolingFactor
return self.beta
SimPEG/Optimization.py
+23 -23
View File
@@ -9,11 +9,11 @@ class StoppingCriteria(object):
"""docstring for StoppingCriteria"""
iteration = { "str": "%d : maxIter = %3d <= iter = %3d",
"left": lambda M: M.maxIter, "right": lambda M: M._iter,
"left": lambda M: M.maxIter, "right": lambda M: M.iter,
"stopType": "critical"}
iterationLS = { "str": "%d : maxIterLS = %3d <= iterLS = %3d",
"left": lambda M: M.maxIterLS, "right": lambda M: M._iterLS,
"left": lambda M: M.maxIterLS, "right": lambda M: M.iterLS,
"stopType": "critical"}
armijoGoldstein = { "str": "%d : ft = %1.4e <= alp*descent = %1.4e",
@@ -21,11 +21,11 @@ class StoppingCriteria(object):
"stopType": "optimal"}
tolerance_f = { "str": "%d : |fc-fOld| = %1.4e <= tolF*(1+|f0|) = %1.4e",
"left": lambda M: 1 if M._iter==0 else abs(M.f-M.f_last), "right": lambda M: 0 if M._iter==0 else M.tolF*(1+abs(M.f0)),
"left": lambda M: 1 if M.iter==0 else abs(M.f-M.f_last), "right": lambda M: 0 if M.iter==0 else M.tolF*(1+abs(M.f0)),
"stopType": "optimal"}
moving_x = { "str": "%d : |xc-x_last| = %1.4e <= tolX*(1+|x0|) = %1.4e",
"left": lambda M: 1 if M._iter==0 else norm(M.xc-M.x_last), "right": lambda M: 0 if M._iter==0 else M.tolX*(1+norm(M.x0)),
"left": lambda M: 1 if M.iter==0 else norm(M.xc-M.x_last), "right": lambda M: 0 if M.iter==0 else M.tolX*(1+norm(M.x0)),
"stopType": "optimal"}
tolerance_g = { "str": "%d : |proj(x-g)-x| = %1.4e <= tolG = %1.4e",
@@ -56,12 +56,12 @@ class StoppingCriteria(object):
class IterationPrinters(object):
"""docstring for IterationPrinters"""
iteration = {"title": "#", "value": lambda M: M._iter, "width": 5, "format": "%3d"}
iteration = {"title": "#", "value": lambda M: M.iter, "width": 5, "format": "%3d"}
f = {"title": "f", "value": lambda M: M.f, "width": 10, "format": "%1.2e"}
norm_g = {"title": "|proj(x-g)-x|", "value": lambda M: norm(M.projection(M.xc - M.g) - M.xc), "width": 15, "format": "%1.2e"}
totalLS = {"title": "LS", "value": lambda M: M._iterLS, "width": 5, "format": "%d"}
totalLS = {"title": "LS", "value": lambda M: M.iterLS, "width": 5, "format": "%d"}
iterationLS = {"title": "#", "value": lambda M: (M._iter, M._iterLS), "width": 5, "format": "%3d.%d"}
iterationLS = {"title": "#", "value": lambda M: (M.iter, M.iterLS), "width": 5, "format": "%3d.%d"}
LS_ft = {"title": "ft", "value": lambda M: M._LS_ft, "width": 10, "format": "%1.2e"}
LS_t = {"title": "t", "value": lambda M: M._LS_t, "width": 10, "format": "%0.5f"}
LS_armijoGoldstein = {"title": "f + alp*g.T*p", "value": lambda M: M.f + M.LSreduction*M._LS_descent, "width": 16, "format": "%1.2e"}
@@ -188,15 +188,15 @@ class Minimize(object):
x0 = x0
xc = x0
_iter = _iterLS = 0
iter = iterLS = 0
:param numpy.ndarray x0: initial x
:rtype: None
:return: None
"""
self._iter = 0
self._iterLS = 0
self.iter = 0
self.iterLS = 0
x0 = self.projection(x0) # ensure that we start of feasible.
self.x0 = x0
@@ -268,7 +268,7 @@ class Minimize(object):
pass
def stoppingCriteria(self, inLS=False):
if self._iter == 0:
if self.iter == 0:
self.f0 = self.f
self.g0 = self.g
return Utils.checkStoppers(self, self.stoppers if not inLS else self.stoppersLS)
@@ -360,21 +360,21 @@ class Minimize(object):
"""
# Projected Armijo linesearch
self._LS_t = 1
self._iterLS = 0
while self._iterLS < self.maxIterLS:
self.iterLS = 0
while self.iterLS < self.maxIterLS:
self._LS_xt = self.projection(self.xc + self._LS_t*p)
self._LS_ft = self.evalFunction(self._LS_xt, return_g=False, return_H=False)
self._LS_descent = np.inner(self.g, self._LS_xt - self.xc) # this takes into account multiplying by t, but is important for projection.
if self.stoppingCriteria(inLS=True): break
self._iterLS += 1
self.iterLS += 1
self._LS_t = self.LSshorten*self._LS_t
if self.debugLS:
if self._iterLS == 1: self.printInit(inLS=True)
if self.iterLS == 1: self.printInit(inLS=True)
self.printIter(inLS=True)
if self.debugLS and self._iterLS > 0: self.printDone(inLS=True)
if self.debugLS and self.iterLS > 0: self.printDone(inLS=True)
return self._LS_xt, self._iterLS < self.maxIterLS
return self._LS_xt, self.iterLS < self.maxIterLS
@Utils.count
def modifySearchDirectionBreak(self, p):
@@ -416,7 +416,7 @@ class Minimize(object):
# store old values
self.f_last = self.f
self.x_last, self.xc = self.xc, xt
self._iter += 1
self.iter += 1
if self.debug: self.printDone()
@@ -613,7 +613,7 @@ class ProjectedGradient(Minimize, Remember):
f_current_decrease = self.f_last - self.f
self.comment = ''
if self._iter < 1:
if self.iter < 1:
# Note that this is reset on every CG iteration.
self.f_decrease_max = -np.inf
else:
@@ -684,7 +684,7 @@ class BFGS(Minimize, Remember):
return self.bfgs(-self.g)
def _doEndIteration_BFGS(self, xt):
if self._iter is 0:
if self.iter is 0:
self.g_last = self.g
return
@@ -817,7 +817,7 @@ class NewtonRoot(object):
"""
if self.comments: print 'Newton Method:\n'
self._iter = 0
self.iter = 0
while True:
r, J = fun(x, return_g=True)
@@ -851,10 +851,10 @@ class NewtonRoot(object):
rt = fun(xt, return_g=False)
x = xt
self._iter += 1
self.iter += 1
if norm(rt) < self.tol:
break
if self._iter > self.maxIter:
if self.iter > self.maxIter:
print 'NewtonRoot stopped by maxIters. norm: %4.4e' % norm(rt)
break
SimPEG/Regularization.py
+23 -7
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@@ -25,14 +25,30 @@ class BaseRegularization(object):
self.mesh = mesh
self.model = model
mref = Utils.ParameterProperty('mref', default=None, doc='Reference model.')
@property
def mref(self):
if getattr(self, '_mref', None) is None:
return np.zeros(self.model.nP);
return self._mref
@mref.setter
def mref(self, value):
self._mref = value
def parent(self):
"""This is the parent of the regularization."""
return getattr(self,'_parent',None)
@parent.setter
def parent(self, p):
if getattr(self,'_parent',None) is not None:
print 'Regularization has switched to a new parent!'
self._parent = p
@property
def inv(self): return self.parent.inv
@property
def objFunc(self): return self.parent
@property
def reg(self): return self
@property
def opt(self): return self.parent.opt
@property
def prob(self): return self.parent.prob
@property
def data(self): return self.parent.data
@property
SimPEG/Utils/Save.py
+2 -2
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@@ -57,7 +57,7 @@ class SimPEGTable:
# At the start of every iteration we will create a inversion iteration node.
def _doStartIteration_hdf5_inv(invObj):
invObj._invNodeIt = invObj._invNode.addGroup('%d'%(invObj._iter+1))
invObj._invNodeIt = invObj._invNode.addGroup('%d'%(invObj.iter+1))
preIteration(invObj._invNodeIt)
invObj.hook(_doStartIteration_hdf5_inv, overwrite=True)
@@ -78,7 +78,7 @@ class SimPEGTable:
invObj.hook(_finish_hdf5_inv, overwrite=True)
def _doStartIteration_hdf5_opt(optObj):
optObj._optNodeIt = optObj.parent._invNode.addGroup('%d.%d'%(optObj.parent._iter, optObj._iter))
optObj._optNodeIt = optObj.parent._invNode.addGroup('%d.%d'%(optObj.parent.iter, optObj.iter))
preIteration(optObj._optNodeIt)
invObj.opt.hook(_doStartIteration_hdf5_opt, overwrite=True)
SimPEG/Utils/__init__.py
+15 -10
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@@ -299,25 +299,30 @@ class Parameter(object):
hook(self._parent, _startup_paramProperty, name=startupName, overwrite=True)
@property
def opt(self):
return self.parent.parent.opt
def inv(self): return self.parent.inv
@property
def objFunc(self):
return self.parent
def objFunc(self): return self.parent.objFunc
@property
def reg(self):
return self.parent.reg
def opt(self): return self.parent.opt
@property
def reg(self): return self.parent.reg
@property
def data(self): return self.parent.data
@property
def prob(self): return self.parent.prob
@property
def model(self): return self.parent.model
@property
def mesh(self): return self.parent.mesh
def initialize(self):
pass
def get(self):
if (self.current is None or
not self.opt._iter == self.currentIter):
not self.opt.iter == self.currentIter):
self.current = self.nextIter()
self.currentIter = self.opt._iter
self.currentIter = self.opt.iter
return self.current
def nextIter(self):