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rowanc1
97 lines
3.0 KiB
Python
97 lines
3.0 KiB
Python
import Utils, Survey, Problem, numpy as np, scipy.sparse as sp, gc
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from Utils.SolverUtils import *
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import DataMisfit
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import Regularization
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class BaseInvProblem(object):
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"""BaseInvProblem(dmisfit, reg, opt)"""
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__metaclass__ = Utils.SimPEGMetaClass
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beta = 1.0 #: 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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dmisfit = None #: DataMisfit
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reg = None #: Regularization
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opt = None #: Optimization program
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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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def __init__(self, dmisfit, reg, opt, **kwargs):
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Utils.setKwargs(self, **kwargs)
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assert isinstance(dmisfit, DataMisfit.BaseDataMisfit), 'dmisfit must be a DataMisfit class.'
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assert isinstance(reg, Regularization.BaseRegularization), 'reg must be a Regularization class.'
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self.dmisfit = dmisfit
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self.reg = reg
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self.opt = opt
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self.prob, self.survey = dmisfit.prob, dmisfit.survey
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#TODO: Remove: (and make iteration printers better!)
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self.opt.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 InvProblem.startup'
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if self.reg.mref is None:
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print 'SimPEG.InvProblem will set Regularization.mref 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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print 'SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv. \n ***Done using direct methods***'
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self.opt.bfgsH0 = Solver(self.reg.eval2Deriv(self.m_current))
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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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forward = self.prob
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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.dmisfit.eval(m, u=u)
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phi_m = self.reg.eval(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.dmisfit.evalDeriv(m, u=u)
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phi_mDeriv = self.reg.evalDeriv(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.dmisfit.eval2Deriv(m, v, u=u)
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phi_m2Deriv = self.reg.eval2Deriv(m, v=v)
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return phi_d2Deriv + self.beta * phi_m2Deriv
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H = sp.linalg.LinearOperator( (m.size, m.size), H_fun, dtype=m.dtype )
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out += (H,)
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return out if len(out) > 1 else out[0]
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