Moved parameters to separate file. Documentation updates.

SimPEG/ObjFunction.py

@@ -1,11 +1,11 @@
-from SimPEG import Utils, np, sp
+import Utils, Parameters, numpy as np, scipy.sparse as sp
 
 class BaseObjFunction(object):
     """BaseObjFunction(data, reg, **kwargs)"""
 
     __metaclass__ = Utils.Save.Savable
 
-    beta    = Utils.ParameterProperty('beta', default=1, doc='Regularization trade-off parameter')
+    beta    = Parameters.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
@@ -213,84 +213,3 @@ class BaseObjFunction(object):
         dmisfit = self.data.prob.Jt_approx(m, self.data.Wd * self.data.Wd * self.data.prob.J_approx(m, v, u=u), u=u)
 
         return dmisfit
-
-
-
-class BetaSchedule(Utils.Parameter):
-    """BetaSchedule"""
-
-    beta0 = 'guess'      #: The initial Beta (regularization parameter)
-    beta0_ratio = 0.1    #: When beta0 is set to 'guess', estimateBeta0 is used with this ratio
-
-    coolingFactor = 2.
-    coolingRate = 3
-
-    beta = None          #: Beta parameter
-
-    def __init__(self, *args, **kwargs):
-        Utils.Parameter.__init__(self, *args, **kwargs)
-        Utils.setKwargs(self, **kwargs)
-
-    def initialize(self):
-        self.beta = self.beta0
-
-    @Utils.requires('parent')
-    def nextIter(self):
-        if self.beta is 'guess':
-            if self.debug: print 'BetaSchedule is estimating Beta0.'
-            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
-            self.beta /= self.coolingFactor
-
-        return self.beta
-
-    @Utils.requires('parent')
-    def estimateBeta0(self):
-        """estimateBeta0(u=None)
-
-            The initial beta is calculated by comparing the estimated
-            eigenvalues of JtJ and WtW.
-
-            To estimate the eigenvector of **A**, we will use one iteration
-            of the *Power Method*:
-
-            .. math::
-
-                \mathbf{x_1 = A x_0}
-
-            Given this (very course) approximation of the eigenvector,
-            we can use the *Rayleigh quotient* to approximate the largest eigenvalue.
-
-            .. math::
-
-                \lambda_0 = \\frac{\mathbf{x^\\top A x}}{\mathbf{x^\\top x}}
-
-            We will approximate the largest eigenvalue for both JtJ and WtW, and
-            use some ratio of the quotient to estimate beta0.
-
-            .. math::
-
-                \\beta_0 = \gamma \\frac{\mathbf{x^\\top J^\\top J x}}{\mathbf{x^\\top W^\\top W x}}
-
-
-            :param numpy.array u: fields
-            :param float ratio: desired ratio of the eigenvalues, default is 0.1
-            :rtype: float
-            :return: beta0
-        """
-        objFunc  = self.parent
-        data     = objFunc.data
-
-        m = objFunc.m_current
-        u = objFunc.u_current
-
-        if u is None:
-            u = data.prob.field(m)
-
-        x0 = np.random.rand(*m.shape)
-        t = x0.dot(objFunc.dataObj2Deriv(m,x0,u=u))
-        b = x0.dot(objFunc.reg.modelObj2Deriv()*x0)
-        return self.beta0_ratio*(t/b)