Changeset 560

Timestamp:

09/14/12 16:42:47 (4 years ago)

Author:

mmckerns

Message:

update Lan's collapse code to as used in SURF final report;
add Lan's semilog plotter script

Location:

branches/collapse

Files:

• SEMILOG_PLOTS.py (added)
• TEST_OUQ_surrogate_diam.py (modified) (14 diffs)
• collapse_termination.py (modified) (2 diffs)

branches/collapse/TEST_OUQ_surrogate_diam.py

@@ -1 +1 @@
 #!/usr/bin/env python
 
-debug = False
+debug = True #False
 MINMAX = -1  ## NOTE: sup = maximize = -1; inf = minimize = 1
 #######################################################################
@@ -8 +8 @@
 #######################################################################
 npop = 40
-maxiter = 1000
+maxiter = 6000
 maxfun = 1e+6
-convergence_tol = 1e-6; ngen = 40
 crossover = 0.9
 percent_change = 0.9
+convergence_tol = 1e-6
+tolW = 0.05; tolP_range_fr = 0.05
+ngen = 200; ngcol = 200
 
 
@@ -18 +20 @@
 # the model function
 #######################################################################
-from surrogate import marc_surr as model
-
+from surrogate import marc_surr as model0
 
 #######################################################################
@@ -26 +27 @@
 def optimize(cost,_bounds,_constraints):
   from mystic.solvers import DifferentialEvolutionSolver2
- #from mystic.termination import ChangeOverGeneration as COG
-  from mystic.termination import Or
-  from collapse_termination import CLPS_Weight as WCLPS
-  from collapse_termination import CLPS_Location as LCLPS
   from mystic.strategy import Best1Exp
   from mystic.monitors import VerboseMonitor, Monitor
   from mystic.tools import random_seed
 
- #random_seed(123)
-
-  stepmon = VerboseMonitor(2)
+  random_seed(122)
+
+  stepmon = VerboseMonitor(10,10)
  #stepmon = Monitor()
   evalmon = Monitor()
@@ -50 +47 @@
   solver.SetGenerationMonitor(stepmon)
   solver.SetConstraints(_constraints)
-
+ 
+  ##############################################################################
+  
+                 ## THE FOLLOWING TERMINATION AND CONSTRAINTS ##
+                 ## CAN BE MOVED TO A NEW MODULE AND IMPORTED ##
+  
+  ######################### TERMINATION CONDITIONS #############################
+  from mystic.termination import Or  
+  from mystic.termination import ChangeOverGeneration as COG
+  from collapse_termination_v2 import CLPS_Weight as WCLPS
+  from collapse_termination_v2 import CLPS_Position as PCLPS  
+  from mystic.math.measures import _nested_split  
+  
   tol = convergence_tol
-  _WCLPS = WCLPS(npts, tolG=tol, generations=ngen)
-  _LCLPS = LCLPS(npts, tolL=[1,1,0.03], tolG=tol, generations=ngen) #FIXME
-  CLPS = Or( _WCLPS, _LCLPS )
-  solver.Solve(cost,termination=CLPS,strategy=Best1Exp, \
-               CrossProbability=crossover,ScalingFactor=percent_change)
-
+  _COG = COG(tol,ngen)
+  
+  w_lb, x_lb = _nested_split(lb, npts)
+  w_ub, x_ub = _nested_split(ub, npts)
+  range_x = []
+  for i in range(len(x_lb)):
+      range_x.append(abs(x_lb[i][0] - x_ub[i][0]))  
+  tolP = [ele*tolP_range_fr for ele in range_x] 
+  if debug: print 'tolP = %s' %tolP
+  
+  indexW_ids = [range(nx) for nx in npts]  # list of indices for CLPS_Weight termination check
+  indexP_ids = []  # list of indices for CLPS_Position termination check  
+  
+  x = []
+  _WCLPS = []
+  _PCLPS = []
+  
+  for m in range(len(indexW_ids)):
+      for idx in indexW_ids[m]:
+          _WCLPS.append(WCLPS(npts, tol_weight = tolW, generations=ngcol, axisW = m, indexW = idx))
+          
+  for m in range(len(indexP_ids)):
+      for pair in indexP_ids[m]:
+          _PCLPS.append( PCLPS(npts, tol_position=tolP, generations=ngcol, \
+                                axisP = m, index1 = pair[0], index2  = pair[1])) 
+  x.extend(_WCLPS)
+  x.extend(_PCLPS)
+  x.append(_COG)
+  trm = Or(*x)
+  
+  ######################## NEW CONSTRAINTS AFTER COLLAPSE ######################
+  
+  list_vanished_weights = [] # list of [measure, index] lists
+  list_collapsed_pairs = [] # list of [measure, pair] lists, where pair = [index1, index2]  
+  
+  for i in range(len(npts)):
+    c = []
+    for idx in range(npts[i]):
+        for jdx in range(idx,npts[i]):
+            if idx !=jdx:
+                c.append([idx, jdx])
+    indexP_ids.append(c)  
+  
+  def _set_weight_to_zero(rv, npts, axisW, indexW):
+    from mystic.math.dirac_measure import product_measure
+    c = product_measure()
+    c.load(rv, npts)
+    c[axisW][indexW].weight = 0
+    return c.flatten()
+  
+  def _set_same_positions(rv, npts, axisP, index1, index2):
+    from mystic.math.dirac_measure import product_measure
+    c = product_measure()
+    c.load(rv, npts)
+    c[axisP][index1].weight += c[axisP][index2].weight
+    c[axisP][index2].weight = 0
+    c[axisP][index2].position = c[axisP][index1].position
+    return c.flatten() 
+  
+  def new_constraints(rv, list_collapsed_pairs, list_vanished_weights):
+    for ele in list_vanished_weights:
+      rv = _set_weight_to_zero(rv, npts, axisW = ele[0], indexW = ele[1])
+    for mem in list_collapsed_pairs:
+      rv = _set_same_positions(rv, npts, axisP = mem[0], \
+              index1 = mem[1][0], index2 = mem[1][1])            
+    return _constraints(rv)   
+  
+  ##############################################################################
+  
+  
+  ############################ RUN THE SOLVER ##################################
+  collapse_data = {}
+  tot_evaluations = 0 # total number of function eveluations
+  while _COG(solver) != True:         
+    solver.Solve(cost,termination=trm,strategy=Best1Exp, \
+               CrossProbability=crossover,ScalingFactor=percent_change) 
+    tot_evaluations += solver.evaluations    
+    collapse_data[stepmon._step] = trm(solver, True)
+    
+    if debug:
+      print 'last_itr_evaluations = %s' %solver.evaluations
+      print "Generation  %d " %stepmon._step 
+    
+    int_trm = trm(solver, info = 'self')  
+    for ele in range(len(int_trm)):
+      cond = int_trm[ele]
+      string = cond(solver, info = True)
+      string1 = string.replace(',', ' ')
+      string2 = string1.replace('&', ' ')
+      string3 = string2.replace(':', ' ')
+      lst = string3.split()
+      
+      if lst[0] == 'CLPS_Weight':
+          for i in range(len(lst)):
+              if lst[i] == 'measure':
+                  axisW = int(lst[i+1])
+                  if lst[i+2] == 'index': indexW = int(lst[i+3])
+                  
+          if debug:
+            print 'axisW = %s' %axisW
+            print 'indexW = %s' %indexW
+          
+          list_vanished_weights.append([axisW, indexW]) 
+          indexW_ids[axisW].remove(indexW)
+                 
+      if lst[0] == 'CLPS_Position':
+          for i in range(len(lst)):  
+              if lst[i] == 'measure':        
+                  axisP = int(lst[i+1])
+                  if lst[i+2] == 'indices': 
+                      index1 = int(lst[i+3].lstrip('('))
+                      index2 = int(lst[i+4].rstrip(')'))
+          if debug:
+            print "axisP = %s" %axisP
+            print "index1 = %s" %index1
+            print "index2 = %s" %index2
+          
+          list_collapsed_pairs.append([axisP, [index1, index2]])
+          indexP_ids[axisP].remove([index1,index2])          
+      
+      ##### AFTER COLLAPSE OCCURRED CHANGE THE TERMINATION AND CONSTRAINTS #####
+                    
+      def _new_constraints(rv):
+        return new_constraints(rv, list_collapsed_pairs, list_vanished_weights)      
+      solver.SetConstraints(_new_constraints)
+      
+      x = []
+      _WCLPS = []
+      _PCLPS = []
+      for m in range(len(indexW_ids)):
+          for idx in indexW_ids[m]:
+              _WCLPS.append(WCLPS(npts, generations=ngcol, axisW = m, indexW = idx))
+              
+      for m in range(len(indexP_ids)):
+          for pair in indexP_ids[m]:
+              _PCLPS.append( PCLPS(npts, tol_position=tolP, generations=ngcol, \
+                                    axisP = m, index1 = pair[0], index2  = pair[1])) 
+      x.extend(_WCLPS)
+      x.extend(_PCLPS)
+      x.append(_COG)
+      trm = Or(*x)
+      
+      if debug:
+        print 'list_vanished_weights = '
+        print  list_vanished_weights
+        print 'list_collapsed_pairs = '
+        print  list_collapsed_pairs                         
+  
   solved = solver.bestSolution
  #print "solved: %s" % solver.Solution()
@@ -65 +216 @@
   from mystic.munge import write_support_file
   write_support_file(stepmon)
-  return solved, func_max, func_evals
+  '''
+  if collapse_data:
+    f = open('collapse_data.txt','w')
+    f.write('COLLAPSE DATA = %s' %collapse_data)
+    f.close()'''
+  return solved, func_max, tot_evaluations, collapse_data
 
 
@@ -90 +246 @@
 
   # generate primary constraints function
-  def constraints(rv):
+  def constraints(rv, cnstr_x=None):
     c = product_measure()
     c.load(rv, npts)
@@ -103 +259 @@
     if not (E <= float(target[0] + error[0])) \
     or not (float(target[0] - error[0]) <= E):
-      c.set_expect((target[0],error[0]), model, (x_lb,x_ub))
+      c.set_expect((target[0],error[0]), model, (x_lb,x_ub), cnstr_x)
     ###################### end function-specific ######################
     # extract weights and positions
     return c.flatten()
 
-  # generate maximizing function
+  # generate maximizing functio
   def cost(rv):
     c = product_measure()
@@ -119 +275 @@
 
   # maximize
-  solved, func_max, func_evals = optimize(cost,(lb,ub,npts),constraints)
+  solved, func_max, tot_evaluations, collapse_data = optimize(cost,(lb,ub,npts),constraints)
 
   if MINMAX == 1:
@@ -125 +281 @@
   else:
     print "func_maximum: %s" % func_max  # sup
-  print "func_evals: %s" % func_evals
-
-  return solved, func_max
+  print "tot_evaluations: %s" % tot_evaluations
+
+  return solved, func_max, collapse_data
 
 
@@ -134 +290 @@
 #######################################################################
 if __name__ == '__main__':
-  function_name = model.__name__
+  from time import clock
+  start = clock()
+  
+  from mystic.tools import wrap_function
+  from mystic.monitors import Null
+  fmon = Null()
+  model_evaluations, model = wrap_function(model0, [], fmon)  
+  function_name = model0.__name__
 
   H_mean = 6.5    #NOTE: SET THE 'mean' HERE!
   H_range = 1.0   #NOTE: SET THE 'range' HERE!
-  nx = 2  #NOTE: SET THE NUMBER OF 'h' POINTS HERE!
-  ny = 2  #NOTE: SET THE NUMBER OF 'a' POINTS HERE!
-  nz = 2  #NOTE: SET THE NUMBER OF 'v' POINTS HERE!
+  nx = 5  #NOTE: SET THE NUMBER OF 'h' POINTS HERE!
+  ny = 5  #NOTE: SET THE NUMBER OF 'a' POINTS HERE!
+  nz = 5  #NOTE: SET THE NUMBER OF 'v' POINTS HERE!
   target = (H_mean,)
   error = (H_range,)
@@ -158 +321 @@
   print "...SETTINGS..."
   print "npop = %s" % npop
+  print "ngen = %s" %ngen
+  print "ngcol = %s" %ngcol
   print "maxiter = %s" % maxiter
   print "maxfun = %s" % maxfun
@@ -192 +357 @@
   npts = (nx,ny,nz)
   bounds = (lower_bounds,upper_bounds)
-  solved, diameter = maximize(pars,npts,bounds)
+  solved, diameter, collapse_data = maximize(pars,npts,bounds)
 
   from numpy import array
@@ -203 +368 @@
 
   print "expect: %s" % str( c.get_expect(model) )
-
+  
+  elapsed = (clock() - start)
+  
+  print "########################"
+  print 'RUNTIME = %s' %elapsed
+  print 'COLLAPSE DATA= %s' %collapse_data
+  print 'MODEL Evaluations = %s' %model_evaluations
 # EOF

branches/collapse/collapse_termination.py

@@ -1 @@
-debug = True #False
-
-def CLPS_Weight(npts, tolW=0.05, tolG=10e-6, generations=70):
+debug = False #True
+
+def CLPS_Weight(npts, tol_weight=0.05, generations=200, axisW = '', \
+                indexW = ''):
     """ Termination condition for vanishing weights.
     if info = False:
@@ -7 +8 @@
     if info = True: 
        prints out a message saying for which points the weight vanished
+       
+    In this code, weight collapse is the event where the weight of a support 
+    point (its maximum accros last given number of generations) has been 
+    less than or equal to the tollerance level, tol_weight. 
+    
+    If axisW and index are not declared, the function returns whenever weight 
+    has vanished for any point. If declared (axisW and index are tuple or 
+    integer objects) then the function returns only if collapse had occured f
+    or points with given indices. Note that neither or both axisW and indexW 
+    must be declared, otherwise an error is printed.
     
     npts = (nx, ny, ...) is the tuple of the support point numbers for each
     input."""
+    # from mystic.tools import isNull
+    from mystic.monitors import Null, Monitor, VerboseMonitor
+    monitor = Monitor()
+    if debug: monitor = VerboseMonitor(2, 2)
+      
+
+    def _CLPS_Weight(inst, info=False):
+        ###FIXME: this should probably be in Solver #######
+        # if isNull(inst._stepmon): inst._stepmon = monitor
+        ###################################################
+        
+        ######################## TO BE DELETED##################################
+        #print "HERE I AM and the last 10 best solutions are: "
+        #print inst._stepmon.x[-10:]
+        ########################################################################          
+        
+        ############## PARAMETERS (length, and number of emasures) #############
+        hist_lg = len(inst._stepmon.x)
+        if hist_lg <= generations: return False
+        
+        from mystic.math.measures import _nested_split   
+        w_bestSolution, x_bestSolution = \
+                _nested_split(inst.bestSolution, npts)
+        ########################################################################
+        collapseWeight_msg = ''
+                
+        if isinstance(axisW, int) and isinstance(indexW, int):
+            max_weight = 0
+            for n in range(-generations, 0):
+                weights, positions = _nested_split(inst._stepmon.x[n], npts)
+                if max_weight < weights[axisW][indexW]:
+                    max_weight = weights[axisW][indexW]
+            if max_weight <= tol_weight:
+                collapseWeight_msg += 'measure: %s & index: %s, ' \
+                %(axisW,indexW)                
+            
+        elif isinstance(axisW, tuple) and isinstance(indexW, int):
+            max_weight = [0]*len(axisW)
+            
+            for n in range(-generations, 0):
+                weights, positions = _nested_split(inst._stepmon.x[n], npts)
+                for i in range(len(max_weight)):
+                    if max_weight[i] < weights[axisW[i]][indexW]:
+                        max_weight[i] = weights[axisW[i]][indexW]
+            for k in range(len(max_weight)):
+                if max_weight[k] <= tol_weight:
+                    collapseWeight_msg += 'measure: %s & index: %s, ' \
+                    %(axisW[k],indexW)   
+                    
+        elif isinstance(axisW, int) and isinstance(indexW, tuple):
+            max_weight = [0]*len(indexW)
+                        
+            for n in range(-generations, 0):
+                weights, positions = _nested_split(inst._stepmon.x[n], npts)
+                for i in range(len(max_weight)):
+                    if max_weight[i] < weights[axisW][indexW[i]]:
+                        max_weight[i] = weights[axisW][indexW[i]]
+            for k in range(len(max_weight)):
+                if max_weight[k] <= tol_weight:
+                    collapseWeight_msg += 'measure: %s & index: %s, ' \
+                    %(axisW,indexW[k])
+                    
+        elif (isinstance(axisW, tuple) and isinstance(indexW, tuple)) or \
+            (axisW == '' and indexW == ''): #Note that can't change to not axisW
+                                            # and not indexW since those can be zero indices
+            if (axisW == '' and indexW == ''): 
+                max_weight = [[0] * nx for nx in npts]
+            else:
+                max_weight = [[0]*len(axisW)]*len(indexW)
+                
+            for n in range(-generations, 0):
+                weights, positions = _nested_split(inst._stepmon.x[n], npts)
+                
+                for i in range(len(max_weight)):   
+                    for j in range(len(max_weight[i])):
+                        if (axisW =='' and indexW == ''):
+                            if  max_weight[i][j] < weights[i][j]:
+                                max_weight[i][j] = weights[i][j]
+                        else:
+                            if  max_weight[i][j] < weights[axisW[i]][index[j]]:
+                                max_weight[i][j] = weights[axisW[i]][index[j]]
+                                            
+            for k in range(len(max_weight)):
+                for l in range(len(max_weight[k])):
+                    if max_weight[k][l] <= tol_weight:
+                        if (axisW == '' and indexW == ''):
+                            collapseWeight_msg += 'measure: %s & index: %s, ' \
+                            %(k,l)
+                        else:                           
+                            collapseWeight_msg += 'measure: %s & index: %s, ' \
+                            %(axisW[k],indexW[l])
+                            
+        else: 
+            print 'Error, wrong arguments of CLPS_Weight'
+            return False
+                                               
+        if info:
+            if collapseWeight_msg == '': msg = ''
+            else:
+                msg0 = 'CLPS_Weight with: '
+                msg = msg0 + collapseWeight_msg
+                msg2 = msg0 + '\n npts = %s, tol_weight = %s, generations = %s: \n' \
+                % (npts, tol_weight, generations) 
+                msg2 += 'vanishing weights of support points: '
+                msg2 += collapseWeight_msg
+                if debug: print msg2
+            return msg
+
+        if collapseWeight_msg == '': return False
+        else: return True
+    
+    return _CLPS_Weight
+            
+    
+                        
+def CLPS_Position(npts, tol_position, generations=200, axisP = '', \
+                  index1 = '', index2 = ''):
+    
+    """ Termination condition for collapsing positions of support points.
+    if info = False:
+       returns true if the positions collapse for any 2 support 
+       points in a measure
+    if info = True: 
+       prints out a message saying for which point-pairs the location collapsed.
+    
+    In this code, collapse is the event where the distance between 2 support 
+    points (its maximum accros last given number of generations) has been 
+    less than the tollerance level, tol_position. 
+    
+    If axisP and index1 and index2 are not declared, the function returns 
+    whenever any 2 points collapse in position. If declared (axisP is a tuple 
+    or and integer, index1 and index2 are integers) then the function returns 
+    only if collapse had occured for points with given indices.
+    
+    npts = (nx, ny, ...) is the tuple of the support point numbers for each
+    input."""
+    
     from mystic.tools import isNull
     from mystic.monitors import Null, Monitor, VerboseMonitor
     monitor = Monitor()
-    if debug: monitor = VerboseMonitor(2)
-
-    def _CLPS_Weight(inst, info=False):
+    if debug: monitor = VerboseMonitor(2, 2)
+    
+    # CHECK THAT index1, and index2 are integers if declared, and either none or 
+    # both are empty
+    
+    def _CLPS_Position(inst, info=False):
         ###FIXME: this should probably be in Solver #######
         if isNull(inst._stepmon): inst._stepmon = monitor
         ###################################################
 
-        lg = len(inst._stepmon)
-        if lg <= generations: return False
-        
-        from numpy import array, ones, transpose
-        from mystic.math.dirac_measure import product_measure
-        
-        Last_bestSolution = product_measure()
-        Last_bestSolution.load(inst._stepmon.x[-1], npts)
-            
-        Gen_bestSolution = product_measure()
-        Gen_bestSolution.load(inst._stepmon.x[-generations], npts)
-        
-        dim = len(Last_bestSolution) # Number of measures
-        collapseWeight  = ''      
-
+        ############## PARAMETERS (length, and number of emasures) #############
+        hist_lg = len(inst._stepmon)
+        if hist_lg <= generations: return False
+        
+        from mystic.math.measures import _nested_split  
+        w_bestSolution, x_bestSolution = \
+                _nested_split(inst.bestSolution, npts)
+        
+        if axisP == '':
+            dim = len(w_bestSolution) # Number of measures
+            axis = range(dim)
+        else:
+            axis = axisP            
+            if isinstance( axisP, int ): dim  = 1
+            else: dim = len(list(axisP))
+        
+        ########################################################################
+        collapsePairs_msg  = ''      
+        
+        from numpy import array, zeros, ones, transpose            
         for i in range(dim):
-            Last_collapseWeight = array(Last_bestSolution[i].weights) <= tolW
-            Gen_collapseWeight = array(Gen_bestSolution[i].weights) <= tolW
-            for k in range(len(Last_collapseWeight)):
-                if all([Last_collapseWeight[k], Gen_collapseWeight[k]]):
-                       collapseWeight += ('measure %s: index: %s; ' %(i,k))
+            if isinstance(axis, int): ax_idx = axis
+            else: ax_idx = axis[i]
+                        
+            if isinstance(index1, int):
+                if isinstance(index2, int):
+                    nx = 1
+                    max_measure_diffLoc = 0
+                else:
+                    print "Error argument declaration index2 is \
+                    empty while index1 is not"
+                    return False   #FIXME
+            else:
+                if isinstance(index2, int):
+                    print "Error argument declaration index1 is \
+                    empty while index2 is not"
+                    return False   #FIXME
+                else:
+                    nx = npts[ax_idx]
+                    max_measure_diffLoc = zeros((nx, nx))     
+                            
+            for n in range(-generations, 0):
+                weights, positions = _nested_split(inst._stepmon.x[n], npts)
+                if nx == 1:                       
+                    diffLoc = abs(positions[ax_idx][index1] - \
+                                  positions[ax_idx][index2])
+                    if max_measure_diffLoc < diffLoc:
+                                            max_measure_diffLoc= diffLoc                      
+                else:
+                    diffLoc = abs(transpose(positions[ax_idx]*ones((nx, nx))) - \
+                                      positions[ax_idx]*ones((nx, nx)))                                      
+                    for k in range(nx):
+                        for l in range(nx):
+                            if max_measure_diffLoc[(k, l)] < diffLoc[(k, l)]:
+                                max_measure_diffLoc[(k, l)] = diffLoc[(k, l)]
+
+            if nx ==1 and max_measure_diffLoc <= tol_position[ax_idx]: 
+                    collapsePairs_msg += ('measure: %s & indices: (%s,%s),'\
+                                          %(ax_idx,index1,index2))
+               
+            else:            
+                for k in range(nx):
+                    for l in range(k,nx):
+                        if k != l and max_measure_diffLoc[(k,l)] <= \
+                           tol_position[ax_idx]:
+                            collapsePairs_msg += ('measure: %s & indices: (%s,%s),' \
+                            %(ax_idx,k,l))
+    
         if info:
-            if collapseWeight == '': msg = ''
-            else:
-                msg = 'CLPS_Weight:\n'
-                msg += 'npts = %s, tolW = %s, tolG = %s, generations = %s;\n' \
-                % (npts, tolW, tolG, generations) 
-                msg += 'vanishing weights of support points: '
-                msg += collapseWeight
-                #if debug: print msg
+            if collapsePairs_msg == '': msg = ''
+            else:
+                msg0 = 'CLPS_Position with: '
+                msg = msg0 + collapsePairs_msg
+                msg2 = msg0 + '\n npts = %s, tol_position = %s, generations = %s:\n' \
+                % (npts, tol_position, generations) 
+                msg2 += 'collapse of support points: '
+                msg2 += collapsePairs_msg
+                if debug: print msg2
             return msg
-
-        if collapseWeight == '': return False
+        
+        if collapsePairs_msg == '': return False
         else: return True
     
-    return _CLPS_Weight
-            
-    
-            
-            
-def CLPS_Location(npts, tolL, tolG=10e-6, generations=30):
-    
-    """ Termination condition for collapsing locations of support points.
-    if info = False:
-       returns true if the locations collapse for any 2 support 
-       points in a measure
-    if info = True: 
-       prints out a message saying for which point-pairs the location collapsed.
-    
-    npts = (nx, ny, ...) is the tuple of the support point numbers for each
-    input."""
-    from mystic.tools import isNull
-    from mystic.monitors import Null, Monitor, VerboseMonitor
-    monitor = Monitor()
-    if debug: monitor = VerboseMonitor(2)
-    
-    def _CLPS_Location(inst, info=False):
-        ###FIXME: this should probably be in Solver #######
-        if isNull(inst._stepmon): inst._stepmon = monitor
-        ###################################################
-
-        lg = len(inst._stepmon)
-        if lg <= generations: return False
-        
-        from numpy import array, ones, transpose
-        from mystic.math.dirac_measure import product_measure
-        
-        Last_bestSolution = product_measure()
-        Last_bestSolution.load(inst._stepmon.x[-1], npts)
-        
-        Gen_bestSolution = product_measure()
-        Gen_bestSolution.load(inst._stepmon.x[-generations], npts)
-        
-        dim = len(Last_bestSolution) # Number of measures
-        collapsePairs  = ''      
-  
-        for i in range(dim):
-            x = array(Last_bestSolution[i].coords)   
-            Last_DiffLoc = transpose(x*ones((len(x), len(x)))) - \
-                    x*ones((len(x), len(x)))
-                    
-            y = array(Gen_bestSolution[i].coords)     
-                    
-            Gen_DiffLoc = transpose(y*ones((len(y), len(y)))) - \
-                            y*ones((len(y), len(y))) 
-                                      
-            collapseLoc1 = (abs(Last_DiffLoc) <= tolL[i]) 
-            collapseLoc2 = (abs(Gen_DiffLoc) <= tolL[i])
-            
-            for k in range(len(x)):
-                for l in range(len(x)):
-                    if k != l and all([collapseLoc1[(k,l)], \
-                                       collapseLoc2[(k, l)]]):
-                        collapsePairs += ('measure %s: indices (%s,%s); ' \
-                                          %(i,k, l))
-        
-        if info:
-            if collapsePairs == '': msg = ''
-            else:
-                msg = 'CLPS_Location:\n'
-                msg += 'npts = %s, tolL = %s, tolG = %s, generations = %s;\n' \
-                % (npts, tolL, tolG, generations) 
-                msg += 'collapse of support points: '
-                msg += collapsePairs
-                #if debug: print msg
-            return msg
-
-        if collapsePairs == '': return False
-        else: return True
-    
-    return _CLPS_Location
-        
-
+    return _CLPS_Position
+        
+