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Futurize 1, futurize 2, pasteurize.

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This commit is contained in:
Brendan Smithyman
2016-07-16 14:17:02 -05:00
parent 362975d2bd
commit ca8d8f8c2d
197 changed files with 2618 additions and 1235 deletions
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SimPEG/Optimization.py
+42 -33
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@@ -1,5 +1,16 @@
import Utils, numpy as np, scipy.sparse as sp
from Utils.SolverUtils import *
from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from builtins import super
from future import standard_library
standard_library.install_aliases()
from past.utils import old_div
from builtins import object
from . import Utils
import numpy as np, scipy.sparse as sp
from .Utils.SolverUtils import *
from future.utils import with_metaclass
norm = np.linalg.norm
@@ -78,13 +89,11 @@ class IterationPrinters(object):
phi_m = {"title": "phi_m", "value": lambda M: M.parent.phi_m, "width": 10, "format": "%1.2e"}
class Minimize(object):
class Minimize(with_metaclass(Utils.SimPEGMetaClass, object)):
"""
Minimize is a general class for derivative based optimization.
"""
__metaclass__ = Utils.SimPEGMetaClass
name = "General Optimization Algorithm" #: The name of the optimization algorithm
maxIter = 20 #: Maximum number of iterations
@@ -121,7 +130,7 @@ class Minimize(object):
@callback.setter
def callback(self, value):
if self.callback is not None:
print 'The callback on the %s Optimization was replaced.' % self.__name__
print('The callback on the %s Optimization was replaced.' % self.__name__)
self._callback = value
@@ -412,7 +421,7 @@ class Minimize(object):
:return: (xt, breakCaught) numpy.ndarray, bool
"""
self.printDone(inLS=True)
print 'The linesearch got broken. Boo.'
print('The linesearch got broken. Boo.')
return p, False
@Utils.count
@@ -493,14 +502,14 @@ class Remember(object):
def _doEndIterationRemember(self, *args):
for param in self._rememberThese:
if type(param) is str:
if self.debug: print 'Remember is remembering: ' + param
if self.debug: print('Remember is remembering: ' + param)
val = getattr(self, param, None)
if val is None and getattr(self, 'parent', None) is not None:
# Look to the parent for the param if not found here.
val = getattr(self.parent, param, None)
self._rememberList[param].append( val )
elif type(param) is tuple:
if self.debug: print 'Remember is remembering: ' + param[0]
if self.debug: print('Remember is remembering: ' + param[0])
self._rememberList[param[0]].append( param[1](self) )
@@ -587,19 +596,19 @@ class ProjectedGradient(Minimize, Remember):
self.aSet_prev = self.activeSet(self.xc)
allBoundsAreActive = sum(self.aSet_prev) == self.xc.size
if self.debug: print 'findSearchDirection: stopDoingPG: ', self.stopDoingPG
if self.debug: print 'findSearchDirection: explorePG: ', self.explorePG
if self.debug: print 'findSearchDirection: exploreCG: ', self.exploreCG
if self.debug: print 'findSearchDirection: aSet', np.sum(self.activeSet(self.xc))
if self.debug: print 'findSearchDirection: bSet', np.sum(self.bindingSet(self.xc))
if self.debug: print 'findSearchDirection: allBoundsAreActive: ', allBoundsAreActive
if self.debug: print('findSearchDirection: stopDoingPG: ', self.stopDoingPG)
if self.debug: print('findSearchDirection: explorePG: ', self.explorePG)
if self.debug: print('findSearchDirection: exploreCG: ', self.exploreCG)
if self.debug: print('findSearchDirection: aSet', np.sum(self.activeSet(self.xc)))
if self.debug: print('findSearchDirection: bSet', np.sum(self.bindingSet(self.xc)))
if self.debug: print('findSearchDirection: allBoundsAreActive: ', allBoundsAreActive)
if self.explorePG or not self.exploreCG or allBoundsAreActive:
if self.debug: print 'findSearchDirection.PG: doingPG'
if self.debug: print('findSearchDirection.PG: doingPG')
self._itType = 'SD'
p = -self.g
else:
if self.debug: print 'findSearchDirection.CG: doingCG'
if self.debug: print('findSearchDirection.CG: doingCG')
# Reset the max decrease each time you do a CG iteration
self.f_decrease_max = -np.inf
@@ -611,7 +620,7 @@ class ProjectedGradient(Minimize, Remember):
v = np.ones(shape[1])
i = np.where(iSet)[0]
j = np.arange(shape[1])
if self.debug: print 'findSearchDirection.CG: Z.shape', shape
if self.debug: print('findSearchDirection.CG: Z.shape', shape)
Z = sp.csr_matrix((v, (i, j)), shape=shape)
def reduceHess(v):
@@ -649,9 +658,9 @@ class ProjectedGradient(Minimize, Remember):
# if true go to CG
# don't do too many steps of PG in a row.
if self.debug: print 'doEndIteration.ProjGrad, f_current_decrease: ', f_current_decrease
if self.debug: print 'doEndIteration.ProjGrad, f_decrease_max: ', self.f_decrease_max
if self.debug: print 'doEndIteration.ProjGrad, stopDoingSD: ', self.stopDoingPG
if self.debug: print('doEndIteration.ProjGrad, f_current_decrease: ', f_current_decrease)
if self.debug: print('doEndIteration.ProjGrad, f_decrease_max: ', self.f_decrease_max)
if self.debug: print('doEndIteration.ProjGrad, stopDoingSD: ', self.stopDoingPG)
class BFGS(Minimize, Remember):
@@ -694,10 +703,10 @@ class BFGS(Minimize, Remember):
d = self.bfgsH0 * d #Assume that bfgsH0 is a SimPEG.Solver
else:
khat = 0 if nn is 0 else np.mod(n-nn+k,nn)
gamma = np.vdot(S[:,khat],d)/np.vdot(Y[:,khat],S[:,khat])
gamma = old_div(np.vdot(S[:,khat],d),np.vdot(Y[:,khat],S[:,khat]))
d = d - gamma*Y[:,khat]
d = self.bfgsrec(k-1,n,nn,S,Y,d)
d = d + (gamma - np.vdot(Y[:,khat],d)/np.vdot(Y[:,khat],S[:,khat]))*S[:,khat]
d = d + (gamma - old_div(np.vdot(Y[:,khat],d),np.vdot(Y[:,khat],S[:,khat])))*S[:,khat]
return d
def findSearchDirection(self):
@@ -837,7 +846,7 @@ class NewtonRoot(object):
return out if len(out) > 1 else out[0]
"""
if self.comments: print 'Newton Method:\n'
if self.comments: print('Newton Method:\n')
self.iter = 0
while True:
@@ -852,18 +861,18 @@ class NewtonRoot(object):
xt = x + dh
rt = fun(xt, return_g=False)
if self.comments and self.doLS: print '\tLinesearch:\n'
if self.comments and self.doLS: print('\tLinesearch:\n')
# Enter Linesearch
while True and self.doLS:
if self.comments: print '\t\tResid: %e\n'%norm(rt)
if self.comments: print('\t\tResid: %e\n'%norm(rt))
if norm(rt) <= norm(r) or norm(rt) < self.tol:
break
muLS = muLS*self.stepDcr
LScnt = LScnt + 1
print '.'
print('.')
if LScnt > self.maxLS:
print 'Newton Method: Line search break.'
print('Newton Method: Line search break.')
return None
xt = x + muLS*dh
rt = fun(xt, return_g=False)
@@ -873,7 +882,7 @@ class NewtonRoot(object):
if norm(rt) < self.tol:
break
if self.iter > self.maxIter:
print 'NewtonRoot stopped by maxIters (%d). norm: %4.4e' % (self.maxIter, norm(rt))
print('NewtonRoot stopped by maxIters (%d). norm: %4.4e' % (self.maxIter, norm(rt)))
break
return x
@@ -975,7 +984,7 @@ class ProjectedGNCG(BFGS, Minimize, Remember):
if cgiter == 1:
pc = dc
else:
betak = rd / rdlast
betak = old_div(rd, rdlast)
pc = dc + betak * pc
# Form product Hessian*pc.
@@ -983,12 +992,12 @@ class ProjectedGNCG(BFGS, Minimize, Remember):
Hp = (1-Active)*Hp
# Update delx and residual.
alphak = rd / np.dot(pc, Hp)
alphak = old_div(rd, np.dot(pc, Hp))
delx = delx + alphak*pc
resid = resid - alphak*Hp
rdlast = rd
if np.logical_or(norm(resid)/normResid0 <= self.tolCG, cgiter == self.maxIterCG):
if np.logical_or(old_div(norm(resid),normResid0) <= self.tolCG, cgiter == self.maxIterCG):
cgFlag = 1
# End CG Iterations
@@ -1008,4 +1017,4 @@ class ProjectedGNCG(BFGS, Minimize, Remember):
indx = ((self.xc<=self.lower) & (delx < 0)) | ((self.xc>=self.upper) & (delx > 0))
delx[indx] = 0.
return delx
return delx