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rowanc1
221 lines
7.1 KiB
Python
221 lines
7.1 KiB
Python
import Utils, Survey, Problem, numpy as np, scipy.sparse as sp, gc
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class BaseObjFunction(object):
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"""BaseObjFunction(forward, reg, **kwargs)"""
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__metaclass__ = Utils.SimPEGMetaClass
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beta = 1.0 #: Regularization trade-off parameter
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debug = False #: Print debugging information
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counter = None #: Set this to a SimPEG.Utils.Counter() if you want to count things
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surveyPair = Survey.BaseSurvey
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problemPair = Problem.BaseProblem
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name = 'Base Objective Function' #: Name of the objective function
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u_current = None #: The most current evaluated field
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m_current = None #: The most current model
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@property
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def parent(self):
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"""This is the parent of the objective function."""
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return getattr(self,'_parent',None)
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@parent.setter
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def parent(self, p):
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if getattr(self,'_parent',None) is not None:
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print 'Objective function has switched to a new parent!'
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self._parent = p
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@property
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def inv(self): return self.parent
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@property
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def objFunc(self): return self
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@property
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def opt(self): return getattr(self.parent,'opt',None)
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def __init__(self, forward, reg, **kwargs):
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Utils.setKwargs(self, **kwargs)
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assert forward.ispaired, 'The forward problem and survey must be paired.'
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if isinstance(forward, self.surveyPair):
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self.survey = forward
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self.prob = forward.prob
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elif isinstance(forward, self.problemPair):
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self.prob = forward
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self.survey = forward.survey
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self.reg = reg
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self.reg.parent = self
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@Utils.callHooks('startup')
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def startup(self, m0):
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"""startup(m0)
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Called when inversion is first starting.
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"""
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if self.debug: print 'Calling ObjFunction.startup'
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if self.reg.mref is None:
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print 'Regularization has not set mref. SimPEG.ObjFunction will set it to m0.'
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self.reg.mref = m0
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self.phi_d = np.nan
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self.phi_m = np.nan
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self.m_current = m0
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@Utils.timeIt
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def evalFunction(self, m, return_g=True, return_H=True):
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"""evalFunction(m, return_g=True, return_H=True)
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"""
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self.u_current = None
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self.m_current = m
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gc.collect()
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u = self.prob.fields(m)
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self.u_current = u
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phi_d = self.dataObj(m, u=u)
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phi_m = self.reg.modelObj(m)
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self.dpred = self.survey.dpred(m, u=u) # This is a cheap matrix vector calculation.
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self.phi_d, self.phi_d_last = phi_d, self.phi_d
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self.phi_m, self.phi_m_last = phi_m, self.phi_m
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f = phi_d + self.beta * phi_m
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out = (f,)
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if return_g:
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phi_dDeriv = self.dataObjDeriv(m, u=u)
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phi_mDeriv = self.reg.modelObjDeriv(m)
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g = phi_dDeriv + self.beta * phi_mDeriv
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out += (g,)
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if return_H:
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def H_fun(v):
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phi_d2Deriv = self.dataObj2Deriv(m, v, u=u)
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phi_m2Deriv = self.reg.modelObj2Deriv(m, v=v)
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return phi_d2Deriv + self.beta * phi_m2Deriv
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operator = sp.linalg.LinearOperator( (m.size, m.size), H_fun, dtype=m.dtype )
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out += (operator,)
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return out if len(out) > 1 else out[0]
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@Utils.timeIt
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def dataObj(self, m, u=None):
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"""dataObj(m, u=None)
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:param numpy.array m: geophysical model
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:param numpy.array u: fields
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:rtype: float
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:return: data misfit
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The data misfit using an l_2 norm is:
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.. math::
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\mu_\\text{data} = {1\over 2}\left| \mathbf{W} \circ (\mathbf{d}_\\text{pred} - \mathbf{d}_\\text{obs}) \\right|_2^2
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Where P is a projection matrix that brings the field on the full domain to the data measurement locations;
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u is the field of interest; d_obs is the observed data; and W is the weighting matrix.
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"""
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# TODO: ensure that this is a data is vector and Wd is a matrix.
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R = self.survey.residualWeighted(m, u=u)
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return 0.5*np.vdot(R, R)
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@Utils.timeIt
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def dataObjDeriv(self, m, u=None):
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"""dataObjDeriv(m, u=None)
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:param numpy.array m: geophysical model
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:param numpy.array u: fields
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:rtype: numpy.array
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:return: data misfit derivative
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The data misfit using an l_2 norm is:
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.. math::
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\mu_\\text{data} = {1\over 2}\left| \mathbf{W} \circ (\mathbf{d}_\\text{pred} - \mathbf{d}_\\text{obs}) \\right|_2^2
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If the field, u, is provided, the calculation of the data is fast:
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.. math::
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\mathbf{d}_\\text{pred} = \mathbf{Pu(m)}
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\mathbf{R} = \mathbf{W} \circ (\mathbf{d}_\\text{pred} - \mathbf{d}_\\text{obs})
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Where P is a projection matrix that brings the field on the full domain to the data measurement locations;
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u is the field of interest; d_obs is the observed data; and W is the weighting matrix.
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The derivative of this, with respect to the model, is:
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.. math::
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\\frac{\partial \mu_\\text{data}}{\partial \mathbf{m}} = \mathbf{J}^\\top \mathbf{W \circ R}
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"""
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if u is None: u = self.prob.fields(m)
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R = self.survey.residualWeighted(m, u=u)
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dmisfit = self.prob.Jtvec(m, self.survey.Wd * R, u=u)
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return dmisfit
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@Utils.timeIt
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def dataObj2Deriv(self, m, v, u=None):
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"""dataObj2Deriv(m, v, u=None)
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:param numpy.array m: geophysical model
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:param numpy.array v: vector to multiply
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:param numpy.array u: fields
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:rtype: numpy.array
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:return: data misfit derivative
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The data misfit using an l_2 norm is:
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.. math::
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\mu_\\text{data} = {1\over 2}\left| \mathbf{W} \circ (\mathbf{d}_\\text{pred} - \mathbf{d}_\\text{obs}) \\right|_2^2
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If the field, u, is provided, the calculation of the data is fast:
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.. math::
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\mathbf{d}_\\text{pred} = \mathbf{Pu(m)}
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\mathbf{R} = \mathbf{W} \circ (\mathbf{d}_\\text{pred} - \mathbf{d}_\\text{obs})
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Where P is a projection matrix that brings the field on the full domain to the data measurement locations;
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u is the field of interest; d_obs is the observed data; and W is the weighting matrix.
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The derivative of this, with respect to the model, is:
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.. math::
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\\frac{\partial \mu_\\text{data}}{\partial \mathbf{m}} = \mathbf{J}^\\top \mathbf{W \circ R}
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\\frac{\partial^2 \mu_\\text{data}}{\partial^2 \mathbf{m}} = \mathbf{J}^\\top \mathbf{W \circ W J}
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"""
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if u is None: u = self.prob.fields(m)
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R = self.survey.residualWeighted(m, u=u)
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# TODO: abstract to different norms a little cleaner.
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# \/ it goes here. in l2 it is the identity.
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dmisfit = self.prob.Jtvec_approx(m, self.survey.Wd * self.survey.Wd * self.prob.Jvec_approx(m, v, u=u), u=u)
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return dmisfit
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