Changeset 558 for branches

Timestamp:

09/11/12 19:05:10 (4 years ago)

Author:

mmckerns

Message:

npop to 20 for set_feasible; expose maxiter to user in impose_short;
several substitutions of 'is' for '=='

Location:

branches/UQ/math/legacy

Files:

• TEST_OUQ_StAlData.py (modified) (1 diff)
• dirac_measure.py (modified) (4 diffs)
• legacydata.py (modified) (1 diff)
• paramtrans.py (modified) (5 diffs)

branches/UQ/math/legacy/TEST_OUQ_StAlData.py

@@ -112 +112 @@
        not c.short_wrt_self(data.lipschitz, tol=error[2]):
       c.set_feasible(data, error[2], bounds=bounds, constraints=constrain)
+                                                   # npop=20, maxiter=200)
     ###################### more function-specific #####################
     if debug:

branches/UQ/math/legacy/dirac_measure.py

@@ -954 +954 @@
 
   # prepare bounds for solver
-  bounds = None
-  if kwds.has_key('bounds'):
-    bounds = kwds['bounds']
+  bounds = kwds.pop('bounds', None)
   # if bounds are not set, use the default optimizer bounds
-  if bounds == None:
+  if bounds is None:
     lower_bounds = []; upper_bounds = []
     for n in pts:  # bounds for n*x in each dimension  (x2 due to weights)
@@ -971 +969 @@
   # plug in the 'constraints' function:  param' = constraints(param)
   # constraints should impose_mean(y,w), and possibly sum(weights)
-  constraints = None   # default is no constraints
-  if kwds.has_key('constraints'): constraints = kwds['constraints']
+  constraints = kwds.pop('constraints', None) # default is no constraints
   if not constraints:  # if None (default), there are no constraints
     constraints = lambda x: x
 
+  _self = kwds.pop('with_self', True) # default includes self in shortness
+  if _self is not False: _self = True
   # tolerance for optimization on sum(y)
-  npop = 40 #XXX: tune npop?
-  if kwds.has_key('npop'): npop = kwds.pop('npop')
-  tol = 0.0 # default
-  if kwds.has_key('tol'): tol = kwds.pop('tol')
+  tol = kwds.pop('tol', 0.0) # default
+  npop = kwds.pop('npop', 20) #XXX: tune npop?
+  maxiter = kwds.pop('maxiter', 1000) #XXX: tune maxiter?
 
   # if no guess was made, then use bounds constraints
-  if guess == None:
+  if guess is None:
     if npop:
       guess = bounds
     else:  # fmin_powell needs a list params (not bounds)
       guess = [(a + b)/2. for (a,b) in bounds]
-
-  _self = True   # default is to include self in measuring shortness
-  if kwds.has_key('with_self'): _self = kwds['with_self']
-  if _self != False: _self = True
 
   # construct cost function to reduce sum(lipschitz_distance)
@@ -1012 +1006 @@
 
   # construct and configure optimizer
-  debug = True#False  #!!!
-  maxiter = 1000;  maxfun = 1e+6
+  debug = False  #!!!
+  maxfun = 1e+6
   crossover = 0.9; percent_change = 0.9
   ftol = abs(tol); gtol = None
@@ -1098 +1092 @@
   bounds = kwds.pop('bounds', None)
   # if bounds are not set, use the default optimizer bounds
-  if bounds == None:
+  if bounds is None:
     lower_bounds = []; upper_bounds = []
     for n in pts:  # bounds for n*x in each dimension  (x2 due to weights)

branches/UQ/math/legacy/legacydata.py

@@ -320 +320 @@
     of any element less than the cutoff.
 """
-    # get tolerance in y
-    tol = 0.0
-    if kwds.has_key('tol'): tol = kwds['tol']
-    cutoff = tol  # default is to zero out distances less than tolerance
-    if kwds.has_key('cutoff'): cutoff = kwds['cutoff']
+    tol = kwds.get('tol', 0.0) # get tolerance in y
+    # default is to zero out distances less than tolerance
+    cutoff = kwds.get('cutoff', tol)
     if cutoff is True: cutoff = tol
     elif cutoff is False: cutoff = None
 
-    if L == None: L = self.lipschitz
-    if data == None: data = self
+    if L is None: L = self.lipschitz
+    if data is None: data = self
     from paramtrans import lipschitz_distance, is_feasible
     # calculate the shortness

branches/UQ/math/legacy/paramtrans.py

@@ -208 +208 @@
   from numpy import asarray
   d = infeasibility(distance, cutoff) > 0.0
-  if len(asarray(d).shape) == 0:
+  if len(asarray(d).shape) is 0:
     return not d
   return -d
@@ -226 +226 @@
   distance = array(distance)
   # zero-out all distances less than tolerated
-  if cutoff != None:
-    if len(distance.shape) == 0:
+  if cutoff is not None:
+    if len(distance.shape) is 0:
       return 0.0 if distance <= cutoff else distance
     distance[distance <= cutoff] = 0.0
@@ -263 +263 @@
 
   # get tolerance in y
-  tol = 0.0
-  if kwds.has_key('tol'): tol = kwds.pop('tol')
+  tol = kwds.pop('tol', 0.0)
   cutoff = tol  # default is to zero out distances less than tolerance
   if kwds.has_key('cutoff'): cutoff = kwds.pop('cutoff')
@@ -326 +325 @@
 
   # get tolerance in y and wiggle room in x
-  ytol = 0.0
-  if kwds.has_key('ytol'): ytol = kwds.pop('ytol')
-  xtol = 0.0  # default is to not allow 'wiggle room' in x 
-  if kwds.has_key('xtol'): xtol = kwds.pop('xtol')
+  ytol = kwds.pop('ytol', 0.0)
+  xtol = kwds.pop('xtol', 0.0) # default is to not allow 'wiggle room' in x 
 
   cutoff = ytol  # default is to zero out distances less than tolerance
@@ -335 +332 @@
   if cutoff is True: cutoff = ytol
   elif cutoff is False: cutoff = None
-  ipop = min(20, 3*nxi) #XXX: tune ipop?
-  if kwds.has_key('ipop'): ipop = kwds.pop('ipop')
-  imax = 1000 #XXX: tune imax?
-  if kwds.has_key('imax'): imax = kwds.pop('imax')
+  ipop = kwds.pop('ipop', min(20, 3*nxi)) #XXX: tune ipop?
+  imax = kwds.pop('imax', 1000) #XXX: tune imax?
 
   #########################################################################