[Swift-commit] r5639 - in SwiftApps/SciColSim: . bin docs old

jonmon at ci.uchicago.edu jonmon at ci.uchicago.edu
Thu Feb 16 14:59:48 CST 2012


Author: jonmon
Date: 2012-02-16 14:59:48 -0600 (Thu, 16 Feb 2012)
New Revision: 5639

Added:
   SwiftApps/SciColSim/bin/
   SwiftApps/SciColSim/bin/convertbest.sh
   SwiftApps/SciColSim/bin/extract4plots
   SwiftApps/SciColSim/bin/getallparams.sh
   SwiftApps/SciColSim/bin/getparamtrace.sh
   SwiftApps/SciColSim/bin/paramtraceall.sh
   SwiftApps/SciColSim/bin/showbest.sh
   SwiftApps/SciColSim/colortext.swift
   SwiftApps/SciColSim/docs/
   SwiftApps/SciColSim/docs/EMAIL
   SwiftApps/SciColSim/docs/TimingEstimation.txt
   SwiftApps/SciColSim/docs/atomic_times.png
   SwiftApps/SciColSim/docs/atomic_times.txt
   SwiftApps/SciColSim/docs/plot_active.txt
   SwiftApps/SciColSim/docs/plot_cumulative.txt
   SwiftApps/SciColSim/docs/plot_ready_jobs.txt
   SwiftApps/SciColSim/docs/plotit
   SwiftApps/SciColSim/docs/sample.swift.output
   SwiftApps/SciColSim/docs/sample.testopt.py.output
   SwiftApps/SciColSim/old/
   SwiftApps/SciColSim/old/RunSwift.sh
   SwiftApps/SciColSim/old/annealing.open-issues.swift
   SwiftApps/SciColSim/old/beagle.xml
   SwiftApps/SciColSim/old/mathtest.swift
   SwiftApps/SciColSim/old/optimizer.protomods.cpp
   SwiftApps/SciColSim/old/optirun.swift
   SwiftApps/SciColSim/old/original.2011.1014/
   SwiftApps/SciColSim/old/sites.beagle.quick.xml
   SwiftApps/SciColSim/old/sites.beagle.xml
   SwiftApps/SciColSim/old/snapshots.2012.0123/
   SwiftApps/SciColSim/old/t1.py
   SwiftApps/SciColSim/old/t2.py
   SwiftApps/SciColSim/old/t3.py
   SwiftApps/SciColSim/old/tc
   SwiftApps/SciColSim/old/test-orig.sh
   SwiftApps/SciColSim/old/test-swift.sh
   SwiftApps/SciColSim/optimizer.orig-mac.cpp
Removed:
   SwiftApps/SciColSim/EMAIL
   SwiftApps/SciColSim/RunSwift.sh
   SwiftApps/SciColSim/TimingEstimation.txt
   SwiftApps/SciColSim/annealing.open-issues.swift
   SwiftApps/SciColSim/atomic_times.png
   SwiftApps/SciColSim/atomic_times.txt
   SwiftApps/SciColSim/basiclocal.xml
   SwiftApps/SciColSim/beagle.xml
   SwiftApps/SciColSim/cf
   SwiftApps/SciColSim/colortext.swift
   SwiftApps/SciColSim/convertbest.sh
   SwiftApps/SciColSim/extract4plots
   SwiftApps/SciColSim/getallparams.sh
   SwiftApps/SciColSim/getparamtrace.sh
   SwiftApps/SciColSim/local.xml
   SwiftApps/SciColSim/mathtest.swift
   SwiftApps/SciColSim/optimizer.orig-mac.cpp
   SwiftApps/SciColSim/optimizer.protomods.cpp
   SwiftApps/SciColSim/optirun.swift
   SwiftApps/SciColSim/original.2011.1014/
   SwiftApps/SciColSim/pads.similar.beagle.xml
   SwiftApps/SciColSim/paramtraceall.sh
   SwiftApps/SciColSim/plot_active.txt
   SwiftApps/SciColSim/plot_cumulative.txt
   SwiftApps/SciColSim/plot_ready_jobs.txt
   SwiftApps/SciColSim/plotit
   SwiftApps/SciColSim/sample.swift.output
   SwiftApps/SciColSim/sample.testopt.py.output
   SwiftApps/SciColSim/showbest.sh
   SwiftApps/SciColSim/sites.beagle.quick.xml
   SwiftApps/SciColSim/sites.beagle.xml
   SwiftApps/SciColSim/snapshots.2012.0123/
   SwiftApps/SciColSim/t1.py
   SwiftApps/SciColSim/t2.py
   SwiftApps/SciColSim/t3.py
   SwiftApps/SciColSim/tc
   SwiftApps/SciColSim/test-orig.sh
   SwiftApps/SciColSim/test-swift.sh
Modified:
   SwiftApps/SciColSim/optimizer.cpp
Log:
reorganization of the SciColSim directory



Deleted: SwiftApps/SciColSim/EMAIL
===================================================================
--- SwiftApps/SciColSim/EMAIL	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/EMAIL	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,474 +0,0 @@
-==== Email trail (cronological order):
-
----------------------------------------------------------------------------------------
-
-On 10/15/11 8:30 PM, "Michael Wilde" <wilde at mcs.anl.gov> wrote:
-
-> Hi Andrey,
-> 
-> I've got a basic serial version of optimizer.cpp running on Beagle (on a login
-> node).  Now Im trying to parallelize it there, and have some questions:
-> 
-> 1) You set NWorkers to a constant, 24. In multi_loss, you have this code:
-> 
->     for(int i=0; i<Nworkers; i++){
->         for(int j=0; j<5; j++){
->             un[i]->set_parameter(params[j],j);
->         }
->         for(int i=0; i<Nworkers; i++){
->             dispatch_group_async(group, CustomQueues[i], ^{
->                 un[i]->evolve_to_target_and_save(istart, iend, Results,
-> Counters);
->             });
->             istart += step;
->             iend = min(istart+step,N);
->         }
->     }
-> 
-> Can you explain the intention here? I think the innermost loop is clear: run
-> evolve() 24 times in parallel, partitioning the istart..iend range among the
-> 24 workers.  But I dont understand the outermost loop, which seems to do the
-> entire inner loop 24 (NWorkers) times. I can understand the idea of doing the
-> entire inner loop some number of times. But from the above, I presume that
-> evolve would be run NWorkers^2 times, or 24*24 times.  Was that the intention?
-> 
-> 2) If you had many processors available (as you do on Beagle) would you want
-> to run set NWorkers higher? I think you mentioned something in out discussion
-> Friday about "a thousand" workers being useful. Ie, NWorkers for the innermost
-> loop could be 1000.  Was that really what you meant? What would be a
-> mathematically/scientifically useful value for the NWorkers in the innermost
-> loop?
-> 
-> Further: if you do want an NWorkers >> 24, would you still do the evolve
-> NWorkers^2 times?  I dont think you'd really want 1000*1000 = 1M evolve calls,
-> given how many outer loops you have at higher levels of the code, including in
-> the Python wrapper.
-> 
-> 3) We'll need to discuss what values make sense for the outer loops
-> (multi_annealing and the .py wrapper) once I have this working in parallel.
-> 
-> 4) Can you give me a sentence or two about what this code is doing?  I think I
-> understand the outer annealing logic, but I have not dug into the code within
-> evolve...() at all. I think you might have explained it once back in January
-> (before you recoded in C) but I dont recall. If you have a paper or a web page
-> on what youre computing here, that would be interesting for me to read, and to
-> help write a slide on this for the Beagle review.
-
-
------
-
-
-Re: Status and questions on optimizer code
-From :	 andrey rzhetsky <arzhetsk at medicine.bsd.uchicago.edu>
-Subject :	Re: Status and questions on optimizer code
-To :	 Michael Wilde <wilde at mcs.anl.gov>	
-Sun, Oct 16, 2011 08:28 AM
-Hi Mike,
-
-1. I think, you just uncovered a stupid bug on my part -- thank you!  There
-should be only one loop (the outer one).
-2. Yes, of course -- 1000, or even 10000 (I can increase the number of
-repeats then).
-3. OK
-4. The code simulates exploration of a chemical network by a research
-community.  The five major parameters determine the strategy of exploration,
-"target" is the number of new interactions discovered and the loss is the
-number of experiments per one new positive finding.  I can provide you with
-figures and slides, if that would help.
-
-Thank you!
-
-With kind regards,
-
-Andrey
-
-
----------------------------------------------------------------------------------------
-
-
-On 10/16/11 9:13 AM, "Michael Wilde" <wilde at mcs.anl.gov> wrote:
-
-> Hi Andrey,
-> 
-> Looking deeper, I think the bug was simply that the first for() statement
-> should have enclosed just the parameter setting loop.  In other words, the top
-> of multi_loss should start with:
-> 
->     for(int i=0; i<Nworkers; i++){
->         for(int j=0; j<5; j++){
->             un[i]->set_parameter(params[j],j);
->         }
->     }
-> 
-> Then the real work is done by the next loop:
->     for(i=0; i<Nworkers; i++){
-> 
-> Can you confirm that this looks correct to you?
-> 
-> I made that change, and the code now seems to run as I would expect. I will
-> send you some output as soon as I clean up my debugging output.
-> 
-> Next, I made the code run with 24-way parallelism on a Beagle login node using
-> "OpenMP", simply by adding one "pragma" statement in front of the main worker
-> loop above. So that part of the code now looks like this:
-> 
->     int i;
->     #pragma omp parallel for private (i)
->     for(i=0; i<Nworkers; i++){
-> 
-> and each call to evolve...() is now done in parallel (with all the Mac
-> dispatch statements commented out).  I will test, but I *think* that the same
-> code will run just as well in parallel on your multicore Macs, perhaps just a
-> *tiny* bit slower than under Grand Central Dispatch (likely not a noticeable
-> difference).
-> 
-> Now, we have 2 choices:
-> 
-> 1) I can simply replace the Python driver script with a Swift script, to do
-> many runs of the optimizer in parallel.  That would give you the ability to
-> run *many* 24-core optimization runs in parallel, each using 24 cores.  So for
-> example, in your current Python script you do this:
-> 
-> for target in range(58,1009,50):
->   for i in range(15):
-> 
-> So thats about 20 x 15 = 300 invocations of optimizer. I *think* that each of
-> these runs is totally independent and can run in parallel, correct?
-> 
-> So A simple Swift script not much longer than the Python script, along with a
-> few beagle-specific configuration files, will enable all 300 jobs to run in
-> parallel, giving you 300 x 24 (=7200) cores running in parallel. Of course,
-> you can seldom *get* that many cores because the machine is heavily loaded.
-> But you may be able to get 10-30 nodes on a daily basis.  We'll need to
-> experiment with this.
-> 
-> As a *next* step after, we should consider the benefits of changing the value
-> of NWorkers to > 24.  24 is the "easy" limit on Beagle because we can get
-> 24-way parallelism on each node with just that one "pragma" statement.  We can
-> get much greater parallelism with Swift in the inner loop, but for that we
-> need to break up the program a bit more, to have Swift run the inner loop as a
-> separate program, and then return the aggregated results in a file. Even for
-> this option, there are two alternative methods:
-> 
-> - we make optimizer call Swift once for each round of parallel annealing. This
-> is fairly easy. It is somewhat limiting to overall parallelism, in that only
-> one round at a time can run. But It may be very adequate.
-> 
-> - we break the program up further into parallelizable chunks, in which case
-> you have a lot of flexibility and the work always gets done in a near-optimal
-> manner regardless of the shape of a given optimization run (in terms of the
-> various nested loop sizes and evolve() execution times.
-> 
-> I think we'll need to discuss this in person over a whiteboard, but I think I
-> have enough knowledge of the program to at least show you a few alternatives.
-> 
-> The main question at the moment, I think, is simply to understand the
-> math/science benefits of extending NWorkers beyond the "low hanging fruit"
-> limit of 24.  What is your assessment of that benefit, Andrey?
-> 
-
------
-
------ Forwarded Message -----
-From: "andrey rzhetsky" <arzhetsk at medicine.bsd.uchicago.edu>
-To: "Michael Wilde" <wilde at mcs.anl.gov>
-Sent: Sunday, October 16, 2011 12:08:25 PM
-Subject: Re: Status and questions on optimizer code
-
-Mike,
-
-It would be fantastic to have 1000 or 10000 workers (with larger number of
-re-runs -- it would improve precision of my analysis drastically!).
-
-All the very best,
-
-Andrey
-
----------------------------------------------------------------------------------------
-
-On 10/17/11 8:29 AM, "Michael Wilde" <wilde at mcs.anl.gov> wrote:
-
-> Hi Andrey,
-> 
-> Can we meet today to discuss the optimizer? I'd like to show you what Ive done
-> and discuss with you next steps towards getting you running on Beagle. I can
-> meet any time from 10:30 to 3:00.
-> 
-> Do you already have a CI and Beagle login and a project set up for Beagle use?
-> If not, we should get that started.
-> 
-> On the technical side, I have a question about the the typical shape of your
-> optimization runs.
-> 
-> With the sample Python script you gave me, I think we have the following
-> nested iterations in the code:
-> 
-> 20 targets (parallel)
->   15 repeats (parallel)
->     100 Annealing_cycles (serial)
->        6 repeats (serial)
->          1000 to 10000 annealing_repeats (parallel)   # NOTE (1/29 mw): should this be called "reruns"?
->            evolve()
-> 
-> The main question I have at this point is regarding the strategy for
-> increasing the innermost annealing repeats (currently 1,000 divided among 24
-> workers; desired to increase to 10,000).
-> 
-> The outermost loops in my Swift tests are done in parallel. Thus we can have a
-> 300 optimizations going in parallel and 24 annealings in parallel for a total
-> of 7,200 parallel tasks.
-> 
-> The question is: if you will always have a sizeable number of parallel
-> iterations in the outer loops, we dont need to change anything in the inner
-> loop to get more parallelism. In other words, we already have more parallelism
-> than we have CPUs available.
-> 
-> 7200 CPUs is about 42% of the overall Beagle system.  It will be very rare
-> that we we could get that many cores all at once.  But  think we can regularly
-> get say 500 to 2000 cores on a daily basis.
-> 
-> On the other hand, if you expect to regularly run tests of *single* annealing
-> cycles and want to speed those up, then indeed it may be worth changing the
-> code structure.
-> 
-> When me meet I'll try to give you an idea of whats involved. Basically we need
-> to change the structure of the annealing loop to create a function
-> "multi_loss_setup" as a separate executable which defines the annealing
-> parameters and writes them to a file; make multi_loss a separate executable;
-> create another executable "multi_loss_summarize" which reduces the results.
-> We can probably combine multi_loss_summarize into multi_loss_setup.
-> 
-> This is not very hard to do, but still sounds to me like a week of programming
-> to get all all restructured and tested.  Before investing that effort, we
-> should discuss if it will give you any additional performance gains over just
-> running many optimizations in parallel.
-> 
-> I need to run timings on the annealing cycles to see how that change across
-> the parameter space, to see if we can just increase the repeats to 10,000 with
-> no changes to the code. I think the feasibility of doing this the "easy way"
-> is based on how long the longest annealings take at the high end of the
-> parameter space.
-> 
-> Regards,
-> 
-> - Mike
-
------
-
------ Forwarded Message -----
-From: "Andrey Rzhetsky" <arzhetsk at medicine.bsd.uchicago.edu>
-To: "Michael Wilde" <wilde at mcs.anl.gov>
-Sent: Monday, October 17, 2011 8:40:17 AM
-Subject: Re: Meet today to discuss optimizer?
-
-Hi Mike,
-
-The 6 (or more) annealing repeats can be run in parallel too.
-
-Unfortunately, around 10:15 I have to rush to Evanston to CBC meeting for
-the rest of the day (we can chat before, if you have a minute, I am in my
-office).
-
-I don't have Beagle login, unfortunately.
-
-Typically, I will have a sizeable outer loop, so, probably, the current
-24-worker setup is fine.
-
-Thank you very much for helping me out!
-
-All the best,
-
-Andrey
-
-
----------------------------------------------------------------------------------------
-
-On 10/18/11 1:52 PM, "Michael Wilde" <wilde at mcs.anl.gov> wrote:
-
-> Hi Andrey,
-> 
-> Here's a quick update:
-> 
-> - I am now running the optimizer on Beagle compute nodes under Swift.
-> 
-> I attach a few tar files of sample runs at reduced parameter values (to shrink
-> the run time for debugging and learning the code's behavior);
-> 
-> Now Im trying to run some subset of the full-length parameters you gave me in
-> the python file.  Ive got 3 Beagle compute nodes allocated at the moment (72
-> cores total) and Im seeing these times from multi_loss with N=1000 repeats:
-> 
-> sandbox$ grep multi_  ./jobs/*/*/output/*.out
-> ./jobs/0/optimizer-01p7lhhk/output/T408.R1.out:multi_loss(N=1000) elapsed
-> time: 122.742 seconds 2.04571 minutes
-> ./jobs/0/optimizer-01p7lhhk/output/T408.R1.out:multi_loss(N=1000) elapsed
-> time: 123.979 seconds 2.06631 minutes
-> ./jobs/0/optimizer-01p7lhhk/output/T408.R1.out:multi_loss(N=1000) elapsed
-> time: 123.624 seconds 2.0604 minutes
-> ./jobs/t/optimizer-t0p7lhhk/output/T958.R1.out:multi_loss(N=1000) elapsed
-> time: 1431.09 seconds 23.8514 minutes
-> ./jobs/x/optimizer-x0p7lhhk/output/T708.R1.out:multi_loss(N=1000) elapsed
-> time: 627.074 seconds 10.4512 minutes
-> ./jobs/x/optimizer-x0p7lhhk/output/T708.R1.out:multi_loss(N=1000) elapsed
-> time: 790.652 seconds 13.1775 minutes
-> 
-> 
-> Each run of optimizer is going to a file name T(target).R(repeat).out
-> 
-> So we're seeing 23.8 mins for 1000 repeats at target=958 and 10-13 mins at
-> target=708. The 1000 repeats are spread over 24 cores each.
-> 
-> Whats your time availability later in the week to discuss this further, and to
-> see if either (a) I can show you how to run this version or (b) we can get a
-> set of production run descriptions from you and you can run them yourself?
-> 
-> In the compressed tar file at http://www.ci.uchicago.edu/~wilde/AR.snap.01.tgz
-> you will find:
-> 
-> - the swift script that I use instead of the Python driver to run the
-> optimizer in parallel (along beagle.xml that specifies scheduler parameters
-> for Beagle like time, cores, queue name and project ID)
-> 
-> - the slightly modified version of optimizer (changes in multi_loss() to
-> correct the loops, changes to use OpenMP instead of Grand Central Dispatch,
-> and a few changes in output logging).
-> 
-> - a few run directories of runs with shortened parameter settings.
-> 
-> If we continue working together on this, we should set up a way to share code
-> using a repository like Subversion (svn).  Thats pretty easy once you master a
-> few basic commands.
-> 
-> Regards,
-> 
-> - Mike
-> 
-> 
-
------
-
------ Forwarded Message -----
-From: "Andrey Rzhetsky" <arzhetsk at medicine.bsd.uchicago.edu>
-To: "Michael Wilde" <wilde at mcs.anl.gov>
-Sent: Tuesday, October 18, 2011 3:57:36 PM
-Subject: Re: Meet today to discuss optimizer?
-
-Mike,
-
-Thank you!  Are you around now?  I would be also happy to carve some time
-tomorrow, if this works for you.
-
-With kind regards,
-
-Andrey
-
-
----------------------------------------------------------------------------------------
-
-
-On 10/19/11 12:10 PM, "Michael Wilde" <wilde at mcs.anl.gov> wrote:
-
-> Hi Andrey,
-> 
-> Im in meetings today till about 3PM. Are you available at say 3:30 or later?
-> 
-> I did a larger run last night. Only one smaller optimizer run *fully*
-> finished, but many others made significant progress.  The results are at:
-> 
->   http://www.ci.uchicago.edu/~wilde/AR.optimizer.out.2010.1018.tgz
-> 
-> If you have time, could you take a look at that run and see if the
-> optimizations look like they have been running as expected? Ive made only a
-> few cosmetic changes to your debug output.
-> 
-> I submitted the run at 21:20; it started running at about 21:27; by about
-> 23:10 it had acquired 12 nodes (x 24 cores each). It ended about 23:18 when
-> the first job exceeded its time limit of 5 hours. Im still trying to calibrate
-> how much time each optimizer invocation needs, and whether some of the
-> internal iterations can be further spread out.  Also how to organize the run
-> so that optimizations that time out  can be re-run with the smallest
-> reasonable failure unit.
-> 
->
-
----------------------------------------------------------------------------------------
-
-
------ Forwarded Message -----
-From: "andrey rzhetsky" <arzhetsk at medicine.bsd.uchicago.edu>
-To: "Michael Wilde" <wilde at mcs.anl.gov>
-Sent: Wednesday, October 26, 2011 8:40:21 PM
-Subject: Re: Question on inner annealing loop
-
-Mike,
-
-
-> Im confused on 3 points here:
-> 
-> - the inner loop would always be done between 1 and 5 times, right?
-
-Correct.
-
-> - could each of those times really be done in parallel? (I'll try to determine
-> this by inspection).
-
-Not really -- the acceptance of parameter changes depends on the loss in
-between.
-
-> - when we last met in your office, I *thought* you indicated that this inner
-> loop could be done just *once*.  Was that what you meant?  And if so, for
-> which of the 5 vars?
-
-Nope, has to be repeated over and over.
-
-All the very best,
-
-Andrey
-
-
----------------------------------------------------------------------------------------
-
-
-On 10/26/11 10:42 PM, "Michael Wilde" <wilde at mcs.anl.gov> wrote:
-
-> OK, all that makes sense, Andrey. But then do you recall what you suggested
-> when we met?
-> 
-> Lets label the loops as follows:
-> 
-> a) 20 targets (parallel)
-> b)   15 repeats (parallel)
-> c)     100 Annealing_cycles (serial)
-> d)        6 repeats (serial)
-> e)         1000 to 10000 annealing_repeats (parallel)
-> f)            evolve()
-> 
-> What I recalled from our last discussion was that I should reduce loop (c)
-> from 100 to 50 or 25, and loop (d) to 1.  But since reducing loop (d) doesn't
-> make sense, do you recall suggesting any other reduction?
-> 
-> If not, no problem, I think I know how to proceed.
-> 
-> Thanks,
-> 
-> - Mike
-> 
-> 
------
-
------ Forwarded Message -----
-From: "andrey rzhetsky" <arzhetsk at medicine.bsd.uchicago.edu>
-To: "Michael Wilde" <wilde at mcs.anl.gov>
-Sent: Thursday, October 27, 2011 2:54:06 AM
-Subject: Re: Question on inner annealing loop
-
-Hi Mike,
-
-I suggested reducing (b) to 1.
-
-With kind regards,
-
-Andrey
-
-
-

Deleted: SwiftApps/SciColSim/RunSwift.sh
===================================================================
--- SwiftApps/SciColSim/RunSwift.sh	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/RunSwift.sh	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,49 +0,0 @@
-#!/bin/bash
-
-# ./Runswift local to run on sandbox
-# ./Runswift clustersmall to run on beagle pbs cluster at small scale
-# ./Runswift clusterquick to run on beagle pbs cluster at large scale
-
-escapecode=$(echo -n -e '\033')
-
-count=$(head -1 counter.txt);
-expr $count + 1 > counter.txt
-mkdir run$count
-cp /home/ketan/SciColSim/*.swift run$count/
-cp /home/ketan/SciColSim/sites.beagle.xml run$count/
-cp /home/ketan/SciColSim/sites.beagle.quick.xml run$count/
-cp local.xml run$count/
-cp /home/ketan/SciColSim/tc run$count/
-cp /home/ketan/SciColSim/movie_graph.txt run$count/
-cp /home/ketan/SciColSim/cf run$count/
-cd run$count
-
-if [ $1 = "local" ]
-then
- #SWIFT_HEAP_MAX=7000M swift -tc.file tc -sites.file local.xml -config cf annealing.swift -e33="$escapecode" -nworkers=36 >& swift.out
- #Total jobs = 6 * 1 * 120/20 * 3 * 100 = 10,800
- SWIFT_HEAP_MAX=7000M swift -tc.file tc -sites.file local.xml -config cf annealing.swift -e33="$escapecode" -nworkers=6 -minrange=58 -maxrange=64 -rangeinc=1 -evoreruns=120 -nreps=1 -alphai=0 -alpham=0 -beta=4.0 -gamma=50.0 -delta=-1 -annealingcycles=100 -rerunsperapp=20  >& swift.out
-
-elif [ $1 = "clusterbig" ]
-then
- SWIFT_HEAP_MAX=7000M swift -tc.file tc -sites.file sites.beagle.xml -config cf annealing.swift -e33="$escapecode" -nworkers=24 -rangeinc=50 -evoreruns=960 -startingjump=2.3 -alphai=0 -alpham=0 -beta=4.0 -gamma=50.0 -delta=-1 -annealingcycles=100 -rerunsperapp=192  >& swift.out
-
-elif [ $1 = "clustersmall" ]
-then
- SWIFT_HEAP_MAX=7000M swift -tc.file tc -sites.file sites.beagle.xml -config cf annealing.swift \-e33="$escapecode" \
-      >& swift.out
-
-elif [ $1 = "clusterquick" ]
-then
-#target_innovation=(1009-58)/50=~20
-#repeats=nreps=1
-# 3 repeats constant (serial)
-#annealing_cycles=100 (serial)
-#rerunsperapp=192
-#evoreruns=960
-#J=evoreruns/rerunsperapp=960/192=5
-
-#Total parallel jobs = (maxrange-minrange)/rangeinc * nreps * (evoreruns/rerunsperapp) = (1009-58)/50 * 1 * 960/192 = 20*5 = 100 Jobs = 2400 openmp jobs in parallel
- SWIFT_HEAP_MAX=7000M swift -tc.file tc -sites.file sites.beagle.quick.xml -config cf annealing.swift -e33="$escapecode" -nworkers=24 -minrange=58 -maxrange=1009 -rangeinc=50 -evoreruns=960 -nreps=1 -alphai=0 -alpham=0 -beta=4.0 -gamma=50.0 -delta=-1 -annealingcycles=100 -rerunsperapp=192  >& swift.out
-fi
-

Deleted: SwiftApps/SciColSim/TimingEstimation.txt
===================================================================
--- SwiftApps/SciColSim/TimingEstimation.txt	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/TimingEstimation.txt	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,95 +0,0 @@
-SciColSim on Beagle
-===================
-
-Loop Structure
---------------
-Following is the loop structure for the SciColSim application at 'full-scale' values:
-
-~20 target innovation values
- ~15 repeats
-  100 annealing cycles (serial)
-     3 non-fixed vars    (serial)
-       1,000 reruns (=>evolve_reruns/reruns_per_app))
-         call evolve (1 to 50 seconds per rerun)
-
-
-Total number of jobs are given by the following expression:
-
-Number_of_jobs = target_innovation_values x repeats x fixed_reps x annealing_cycles x evolve_reruns/reruns_per_app x num_workers
-
-               = ceil((max_range - min_range)/range_inc) x repeats x fixed_reps x annealing_cycles x evolve_reruns/reruns_per_app x num_workers
-               
-               = ceil((1009 - 58)/50) x 15 x 3 x 100 x 960/192 x 24
-
-               = 20 x 15 x 3 x 100 x 5 x 24
-
-               = 10,800,000
-
-               = 20 x 15 x 3 x 100 x 1000 x 10 = total core-seconds
-               
-               = 900M core seconds
-               
-               = 900M / 1000 = 900,000 seconds on 1000 cores
-
-               = 250 hours = 10 days
-
-
-
-Estimated Runtime on a small scale (2 laptops or 24 cores)
-
-         =10,800,000/24 x (1 to 50 sec)
-
-         =450,000 to 22,500,000 seconds
-
-         =5.2 hours to 260 days
-
-Estimated Runtime on medium scale (40 Beagle nodes or 960 cores) 
-
-         =10,800,000/960 x (1 to 50 sec)
-        
-         =11,250 to 562,500 seconds
-        
-         =3.12 hours to 6.5 days
-
-
-Atomic job times of application
-multi_loss(N=1, target=58) elapsed time: 0.116707 seconds 0.00194512 minutes
-
-multi_loss(N=1, target=108) elapsed time: 0.26613 seconds 0.0044355 minutes
-
-multi_loss(N=1, target=158) elapsed time: 0.379991 seconds 0.00633318 minutes
-
-multi_loss(N=1, target=208) elapsed time: 0.576198 seconds 0.0096033 minutes
-
-multi_loss(N=1, target=258) elapsed time: 1.50121 seconds 0.0250203 minutes
-
-multi_loss(N=1, target=308) elapsed time: 1.09665 seconds 0.0182775 minutes
-
-multi_loss(N=1, target=358) elapsed time: 1.49523 seconds 0.0249205 minutes
-
-multi_loss(N=1, target=408) elapsed time: 2.3056 seconds 0.0384267 minutes
-
-multi_loss(N=1, target=458) elapsed time: 3.54418 seconds 0.0590697 minutes
-
-multi_loss(N=1, target=508) elapsed time: 5.10912 seconds 0.085152 minutes
-
-multi_loss(N=1, target=558) elapsed time: 6.88611 seconds 0.114768 minutes
-
-multi_loss(N=1, target=608) elapsed time: 8.7683 seconds 0.146138 minutes
-
-multi_loss(N=1, target=658) elapsed time: 10.0665 seconds 0.167775 minutes
-
-multi_loss(N=1, target=708) elapsed time: 12.7259 seconds 0.212098 minutes
-
-multi_loss(N=1, target=758) elapsed time: 14.8005 seconds 0.246675 minutes
-
-multi_loss(N=1, target=808) elapsed time: 16.8803 seconds 0.281338 minutes
-
-multi_loss(N=1, target=858) elapsed time: 19.7864 seconds 0.329774 minutes
-
-multi_loss(N=1, target=908) elapsed time: 23.4506 seconds 0.390843 minutes
-
-multi_loss(N=1, target=958) elapsed time: 28.7667 seconds 0.479445 minutes
-
-multi_loss(N=1, target=1008) elapsed time: 49.0841 seconds 0.818069 minutes
-

Deleted: SwiftApps/SciColSim/annealing.open-issues.swift
===================================================================
--- SwiftApps/SciColSim/annealing.open-issues.swift	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/annealing.open-issues.swift	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,379 +0,0 @@
-import "math";
-import "colortext";
-
-type file;
-
-type Res
-{
-    float loss;
-    float sdev;
-}
-
-global boolean FIX_VARIABLES = true;
-global int var_fixed[] = [1,1,0,0,0];
-global int Nworkers = @toint(@arg("nworkers","4"));
-global int rerunsPerApp;
-
-(float nx) newx(float x, float dx)
-{
-    float r = (random()) ; // / (pow(2.0,31.0)-1.0);
-    if (r > 0.5)
-    {
-        nx = x + (random())*dx; // /(pow(2.0,31.0)-1.0);
-    }
-    else
-    {
-        nx = x - (random())*dx; // /(pow(2.0,31.0)-1.0);
-    }
-    // tracef("newx(%f,%f)=%f\n",x,dx,nx);
-}
-
-app (file outfile, file loss) evolve (string args[], file graph)
-{
-    evolve @loss args stdout=@outfile ;  // graph is passed implicitly
-}
-
-app (file x) sumloss(file loss[])
-{
-    sumloss @filenames(loss) stdout=@x;
-}
-
-/*
-
-  Program structure:
-
-  main
-  optimizer_sweep() - formerly, python script
-  multi_annealing()
-  multi_loss()
-  evolve()
-  sumloss()
-*/
-
-(file bestfile, file maxfile) multi_annealing (
-    float T_start,
-    float T_end,
-    float Target_rejection,
-    int evolve_reruns,
-    float starting_jump,
-    float params0[],
-    float target_innov,
-    int annealing_cycles)
-{
-    int cycle=10;     // const
-    int NEVOPARAMS=5; // const - 5 params, alpha 1,m through delta, does not include target_innovation
-
-    float rejection[][];  // [i][j] where i is cycle and j is evolve-parameter (alpha_i, alpha_m, beta, gamma, delta)
-
-    float x[][], dx[][], curr_loss[], curr_sdev[];
-
-    Res mlres[][];
-    mlres[0][0] = multi_loss( 0, 0, params0, target_innov, evolve_reruns ); // FIXME: serves for all evolve-params ???
-    tracef("multi_annealing: AR: initial: %f +- %f\n",mlres[0][0].loss,mlres[0][0].sdev);
-
-    foreach j in [0:NEVOPARAMS-1]
-    {
-        x[0][j]=params0[j];
-        dx[0][j] = starting_jump;
-        rejection[0][j] = 0.0;
-        curr_loss[j] = mlres[0][0].loss;
-        curr_sdev[j] = mlres[0][0].sdev;
-    }
-
-    iterate iter_i
-    {    // foreach i in [1:annealing_cycles]
-        int i = iter_i + 1;
-
-        // set new temperature, rejection threshold, and dx values for this cycle
-
-        float temperature = T_start*exp( @tofloat(i-1)*(jlog(T_end)-jlog(T_start))/@tofloat(annealing_cycles));
-
-        tracef(@strcat("multi_annealing: AR: i=%i ....T = ",color(3,"%f"),"\n"), i, temperature);
-
-        // On each new "major" cycle within the annealing_cycles (other than the first) set new rejection and dx values
-
-        if ( i %% cycle == 1 && i > 1 )
-        {
-            tracef("multi_annealing: new cycle at i=%i\n",i);
-            tracef(color(Pink, "multi_annealing: AR: New cycle at %i: prev dx[0-4]=[%f %f %f %f %f]\n"),i,dx[i-1][0],dx[i-1][1],dx[i-1][2],dx[i-1][3],dx[i-1][4]);
-            foreach  k in [0:NEVOPARAMS-1]
-            {
-                float newrejection = rejection[i-1][k] / @tofloat(cycle);
-                if (newrejection > 0.0)
-                {
-                    dx[i][k] = dx[i-1][k] / (newrejection / Target_rejection);
-                    // FIXME: re-enable: rejection[i][k]=0.0;
-                }
-                else
-                {
-                    dx[i][k] = dx[i-1][k] * 2.0;
-                    // FIXME: re-enable: rejection[i][k]=rejection[i-1][k];
-                }
-                // FIXME: HANGS? : tracef(color(Red,"Recomputed rejection: i=%d k=%d dx[i][k]=%f\n"), i, k, dx[i][k]);
-            }
-            tracef(color(Blue, "multi_annealing: AR: New cycle at %i: dx[0-4]=[%f %f %f %f %f]\n"),i,dx[i][0],dx[i][1],dx[i][2],dx[i][3],dx[i][4]);
-        }
-        else
-        { // If not new cycle, set dx[i][*] from previous dx ([i-1]). rejection[i]j] is set later.
-            foreach  k in [0:NEVOPARAMS-1]
-            {
-                dx[i][k] = dx[i-1][k];
-            }
-        }
-        //foreach j in [0:NEVOPARAMS-1] { // Try a new value for each non-fixed param; then write results and accept or reject
-        iterate j
-        {  // Try a new value for each non-fixed param; then write results and accept or reject
-            // float try_x[];
-            int curr = (i * NEVOPARAMS) + j;
-            int prev = curr-1;
-            // tracef("in multi_annealing: i=%i j=%i curr=%i prev=%i\n", i, j, curr, prev);
-            if ( /*(!FIX_VARIABLES) || */ (var_fixed[j]==0) ) {  // Adjustable vars
-                // fixed=1,1,0,0,0: FIXME: FIX_VARIABLES flag has faulty logic but OK when TRUE
-                float try_x[];
-                foreach k in [0:NEVOPARAMS-1]
-                { // Select the evolve params to try
-                    if ( k < j )
-                    {
-                        try_x[k] = x[i][k]; // already set x[i][k]
-                    }
-                    else
-                    {
-                        if ( k == j )
-                        {
-                            try_x[k] = newx(x[i-1][j],dx[i-1][j]); // permute x[i-1][j]
-                        }
-                        else
-                        { // k > j
-                            try_x[k] = x[i-1][k]; // use x[i-1][k] (from prior cycle)
-                        }
-                    }
-                }
-                tracef(@strcat("multi_annealing: AR: ", color(10,"%f"), " ", color(9,"%i"),"\n"), try_x[j],j);
-                // Up to here, x[] and dx[] are only set for previous i
-                mlres[i][j] = multi_loss(i,j,try_x, target_innov, evolve_reruns); // do the N evolve()'s, N=evolve_reruns
-                tracef("multi_annealing: AR: %f +- %f\n", mlres[i][j].loss, mlres[i][j].sdev);
-                // Beyond this point, x[] and dx[] are being set for this i,j
-
-                float ALOT=100000000000.0; // 100,000,000,000. = 10^11
-                if (mlres[i][j].loss < ALOT)
-                {
-                    tracef("multi_annealing: AF: best_opt_some.txt: %f,%f,%f,%f,%f,%f,%f,%f\n",
-                           target_innov,mlres[i][j].loss,try_x[0],try_x[1],try_x[2],try_x[3],try_x[4],mlres[i][j].sdev);
-                    tracef(color(Red,"multi_annealing: AF: max_dist.txt - tbd\n")); // FIXME: max_dist is global set in evolve()
-                }
-                else
-                {  // does this ever occur? if so did we want to still do the ratio computation above???
-                    tracef("multi_annealing: Loss %f > ALOT at [i][j] = [%d][%d]\n", mlres[i][j].loss, i ,j);
-                }
-                float ratio = min(1.0, exp( -(mlres[i][j].loss-curr_loss[prev]) / temperature));
-                float r = (random()) ; // / (pow(2.0,31.0)-1.0);  // FIXME: AR: why all the 2^31's ???
-                tracef("multi_annealing: AR: %f vs %f\n", r, ratio);
-                if (r > ratio)
-                {  // Reject new parameter
-                    x[i][j] = x[i-1][j];
-                    rejection[i][j] = rejection[i-1][j] + 1.0;  // FIXME: AR: Is this correct? incr rejection?
-                    curr_loss[curr] = curr_loss[prev];
-                    curr_sdev[curr] = curr_sdev[prev];
-                    // FIXME: AR: the following prints seem to replicate values in the .cpp version - please clarify.
-                    tracef("multi_annealing: AR: %i,%i %i Did not accept: %f (%i)\n", i, j, i, try_x[j], j);
-                    tracef("multi_annealing: AR: %f %f %f %f %f\n", try_x[0],try_x[1],try_x[2],try_x[3],try_x[4]);
-                }
-                else
-                {           // Accept new parameter
-                    tracef("multi_annealing: Accepting try_x[j], i=%i j=%i\n",i,j);
-                    x[i][j] = try_x[j];
-                    rejection[i][j] = rejection[i-1][j];  // FIXME: AR: Is this correct? no incr of rejection?
-                    tracef("multi_annealing: Accepting try_x[j], i=%i j=%i try_x[j]=%f\n",i,j,try_x[j]);
-                    curr_loss[curr] = mlres[i][j].loss;
-                    curr_sdev[curr] = mlres[i][j].sdev;
-                    float rj[];
-                    foreach k in [0:NEVOPARAMS-1]
-                    {  // FIXME!!!
-                        if (k <= j)
-                        {
-                            rj[k] = rejection[i][k]; // Was either set from previous j or just set for this j
-                        }
-                        else
-                        {
-                            rj[k] = rejection[i-1][k]; // Not yet set, use previous
-                        }
-                    }
-                    tracef(@strcat("multi_annealing: AR: [%i][%i] ", color(8,"Rejection counts: "),
-                                   color(1,"%f"), " ", color(7,"%f"), " ", color(5,"%f"), " ", color(9,"%f"), " ", color(6,"%f"), "\n\n"),
-                           i, j, rj[0], rj[1], rj[2], rj[3], rj[4]);
-                    tracef(@strcat("multi_annealing: AR: %i ", color(8,"***** Did accept! "),
-                                   color(1,"%f"), " ", color(7,"%f"), " ", color(5,"%f"), " ", color(9,"%f"), " ", color(6,"%f"), "\n\n"),
-                           i, try_x[0], try_x[1], try_x[2], try_x[3], try_x[4]);
-                }
-            }
-            else
-            { // Fixed Vars
-                x[i][j] = x[i-1][j];
-                rejection[i][j] = rejection[i-1][j];
-                curr_loss[curr] = curr_loss[prev];
-                curr_sdev[curr] = curr_sdev[prev];
-                // dx[i][j] not set for fixed vars
-            }
-        } until(j == NEVOPARAMS-1);
-    } until(iter_i == (annealing_cycles-1));
-}
-
-(Res r) multi_loss( int ci, int cj, float x[], float target_innov, int evolve_reruns )
-// (Res r, Stats s) multi_loss( int ci, int cj, float x[], float target_innov, int evolve_reruns ) FIXME: To obtain stats
-{
-    file rfile[];
-    file ofile[]; // FIXME: to obtain timings and otehr stats
-    tracef("multi_loss: entered: ci=%i cj=%i target_innov=%f evolve_reruns=%i x=%q\n",ci, cj, target_innov,evolve_reruns,x);
-
-    int appCalls = @toint(@tofloat(evolve_reruns) / @tofloat(rerunsPerApp));  // FIXME: handle fractional issues and rounding etc. For now must divide evenly
-
-    tracef("multi_loss appCalls=%i\n", appCalls);
-    foreach i in [1:appCalls] {  // repeats of the evolove() - same as n_reruns
-        file outfile; // FIXME: map and save in future
-        string args[] = [ // FIXME: move this to a setargs() function
-            //    alpha_i        alpha_m        beta           gamma          delta          target_innov
-            @strcat(x[0]), @strcat(x[1]), @strcat(x[2]), @strcat(x[3]), @strcat(x[4]), @strcat(target_innov),
-
-            //     n_epochs n_steps evolve_reruns           range
-            //    "40000",  "20",   @strcat(evolve_reruns), "2",
-            "40000",  "20",   @strcat(rerunsPerApp),  "2",
-
-            //    verbose_level
-            "1",
-
-            //    T_start T_end   Annealing_steps Target_rejection Starting_jump
-            "2.",   "0.01", "2",            "0.3",           "2.3",
-
-            //    FREEZE: alpha_i alpha_m beta gamma delta
-            "1",    "1",    "0", "0",  "0",
-
-            //   operation-code:(m,a) Nworkers           seed
-            "m",                 @strcat(Nworkers), "1234567" ];
-
-        file graph <"movie_graph.txt">;
-        (outfile, rfile[i]) = evolve(args,graph);
-        // (ofile[i], rfile[i]) = evolve(args,graph);
-        tracef("multi_loss: i=%i calling evolve, args=%q\n", i, args);
-        // tracef("multi_loss: after evolve: i=%i %k %k\n", i, outfile, rfile[i]);
-    }
-    file sumfile = sumloss(rfile);
-    r = readData(sumfile);
-    tracef("multi_loss: returning: ci=%i cj=%i r.loss=%f r.sdev=%f\n",ci,cj,r.loss,r.sdev);
-    // file statfile = sumstats(ofile);  FIXME: to obtain timings and otehr stats
-    // s = readStat(statsfile);          FIXME: to obtain timings and otehr stats
-}
-
-optimizer_sweep() // Implements logic of python driver script
-{
-    int minrange=58;
-    int maxrange=59;
-    int rangeinc=50;
-
-    //int maxrange=1009;
-    //int maxrange=209;
-
-    // FIXME: add provision for random priming and random param values when x[i] == -100 (see optimizer.cpp main())
-
-    int nreps=1; // 15
-
-//    file bestfile <single_file_mapper; file=@strcat("output/T",target,".R",rep,".best_opt_some")>;
-//    file maxfile <single_file_mapper; file=@strcat("output/T",target,".R",rep,".max_dist")>;
-
-    foreach target_innov in [minrange:maxrange:rangeinc]
-    {
-        foreach rep in [1:nreps]
-        {
-            file outfile;  // <single_file_mapper; file=@strcat("output/T",target_innov,".R",rep,".out")>;
-            file lossfile; // <single_file_mapper; file=@strcat("output/T",target_innov,".R",rep,".loss_data")>;
-/*
-  (outfile,lossfile) = multi_annealing(
-  T_start          = 2.0,
-  T_end            = 0.01,
-  Target_rejection = 0.3,
-  evolve_reruns    = 10,
-  starting_jump    = 2.3,
-  params0[]        = [0.0, 0.0, 4.0, 50.0, -1.0],
-  @tofloat(target_innov),
-  annealing_cycles = 2);
-*/
-            (outfile,lossfile) = multi_annealing(
-                2.0,
-                0.01,
-                0.3,
-                100,
-                2.3,
-                [0.0, 0.0, 4.0, 50.0, -1.0],
-                @tofloat(target_innov),
-                30);
-        }
-    }
-}
-
-rerunsPerApp = 100;
-
-main()
-{
-    optimizer_sweep();
-}
-
-main();
-
-/*
-
-  Program structure:
-
-  main
-  optimizer_sweep()
-  multi_annealing()
-  multi_loss()
-  evolve()
-  sumloss()
-
-  Example parameter sets:
-
-  for target in range(58,59,50):
-  for i in range(1):
-  args="./toptimizer 0 0 4 50 -1 "+target+" 40000 20 75    2 1 2. 0.01 2 0.3 2.3 1 1 1 0 0 m // > out.T"+str(target)+".i"+str(i)
-  os.system(args);
-
-  string fastargs1[] = [
-  "0", "0", "4", "50", "-1", @strcat(target),
-  "40000", "20", "1000", "2",
-  "1",
-  "2.", "0.01", "100", "0.3", "2.3",
-  "1", "1", "0", "0", "0"];
-  string fastargs2[] = [
-  "0", "0", "4", "50", "-1", @strcat(target),
-  "40000", "20", "1000", "2",
-  "1",
-  "2.", "0.01",  "5", "0.3", "2.3",
-  "1", "1", "0", "0", "0", "m"];
-  string fastargs3[] = [
-  "0", "0", "4", "50", "-1", @strcat(target),
-  "40000", "20", @strcat(repeats), "2",
-  "1",
-  "2.", "0.01",  "2", "0.3", "2.3",
-  "1", "1", "0", "0", "0", "m"];
-*/
-
-(string args[]) setargs()
-{
-    // string longargs[] = @strcat("0 0 4 50 -1 ",target," 40000 20 1000 2 1 2. 0.01 100 0.3 2.3 1 1 0 0 0 m");
-
-    //  [alpha_i alpha_m beta gamma delta target_innov
-    //  [n_epochs n_steps n_reruns] [range]
-    //  [verbose_level]
-    //  [T_start T_end Annealing_steps Target_rejection Starting_jump]
-    //  [FREEZE_alpha_i FREEZE_alpha_m FREEZE_beta FREEZE_gamma FREEZE_delta] [operation-code:(m,a) Nworkers]
-}
-
-////////////////// HOLD JUNK
-
-//                    tracef(@strcat("multi_annealing: AR: %i ", color(8,"Rejection counts: "),
-//                                   color( /* 2 */ 1," %f"), "\n\n"),
-//                                   i, rejection[i][j] ); // , rejection[i][1], rejection[i][2], rejection[i][3], rejection[i][4]);
-// FIXME: determine correct rejection[] values to avoid hanging:
-//                    tracef(@strcat("multi_annealing: AR: %i ", color(8,"Rejection counts: "),
-//                                   color( /* 2 */ 1," %f"), color(7," %f"), color(5," %f"), color(9," %f"), color(6," %f"), "\n\n"),
-//                                   rejection[i][0], rejection[i][1], rejection[i][2], rejection[i][3], rejection[i][4]);
-// END FIXME

Deleted: SwiftApps/SciColSim/atomic_times.png
===================================================================
(Binary files differ)

Deleted: SwiftApps/SciColSim/atomic_times.txt
===================================================================
--- SwiftApps/SciColSim/atomic_times.txt	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/atomic_times.txt	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,21 +0,0 @@
- 58  0.116707 
-108  0.26613 
-158  0.379991
-208  0.576198
-258  1.50121
-308  1.09665
-358  1.49523
-408  2.3056
-458  3.54418
-508  5.10912
-558  6.88611
-608  8.7683
-658 10.0665
-708 12.7259
-758 14.8005
-808 16.8803
-858 19.7864
-908 23.4506
-958 28.7667
-1008 49.0841
-

Deleted: SwiftApps/SciColSim/basiclocal.xml
===================================================================
--- SwiftApps/SciColSim/basiclocal.xml	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/basiclocal.xml	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,10 +0,0 @@
-<config>
-
-   <pool handle="localhost">
-     <execution provider="local" url="none" />
-     <filesystem provider="local"/>
-     <workdirectory>/home/wilde/swift/lab/swiftwork</workdirectory>
-     <profile namespace="karajan" key="jobThrottle">0</profile>
-   </pool>
-
-</config>

Deleted: SwiftApps/SciColSim/beagle.xml
===================================================================
--- SwiftApps/SciColSim/beagle.xml	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/beagle.xml	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,60 +0,0 @@
-<config>
-
-<!-- From Glen: used w/ "Swift svn swift-r4813 (swift modified locally) cog-r3175" -->
-
-  <pool handle="beagle">
-
-    <execution provider="coaster" jobmanager="local:pbs" url="none"/>
-    <profile namespace="globus" key="providerAttributes">pbs.aprun;pbs.mpp;depth=24</profile>
-    <profile key="jobsPerNode" namespace="globus">1</profile>
-
-    <profile namespace="env" key="OMP_NUM_THREADS">24</profile>
-    <profile namespace="globus" key="maxwalltime">02:00:00</profile>
-    <profile namespace="globus" key="maxTime">14400</profile>
-    <profile namespace="globus" key="slots">20</profile>
-    <profile namespace="globus" key="nodeGranularity">1</profile>
-    <profile namespace="globus" key="maxNodes">1</profile>
-    <profile namespace="globus" key="lowOverAllocation">100</profile>
-    <profile namespace="globus" key="highOverAllocation">100</profile>
-    <profile namespace="karajan" key="jobThrottle">.15</profile>
-    <profile namespace="karajan" key="initialScore">10000</profile>
-
-    <profile namespace="globus" key="project">CI-MCB000119</profile>
-    <profile namespace="globus" key="queue">route</profile>
-
-    <filesystem provider="local"/>
-    <workdirectory >/lustre/beagle/wilde/swiftwork</workdirectory>
-  </pool>
-
-<!-- From Justin's swift-devel page: 
-
-<import file="sys.xml"/>
-<set name="wdir" value="/lustre/beagle/{user.name}/work"/>
-<echo message="setting workDirectory to: {wdir}"/>
-
-
-<pool handle="beagle-pbs">
-  <execution jobmanager="local:pbs" provider="coaster" url="none"/>
-  <profile namespace="globus" key="maxWallTime">1</profile>
-  <profile namespace="globus" key="maxTime">7200</profile>
-
-  <profile namespace="globus" key="providerAttributes">
-    pbs.aprun;pbs.mpp;depth=24
-  </profile>
-  <profile key="jobsPerNode" namespace="globus">24</profile>
-  <profile key="slots" namespace="globus">1</profile>
-  <profile key="nodeGranularity" namespace="globus">1</profile>
-  <profile key="maxNodes" namespace="globus">1</profile>
-  <profile key="queue" namespace="globus">batch</profile>
-  <profile key="jobThrottle" namespace="karajan">5.99</profile>
-  <profile key="initialScore" namespace="karajan">10000</profile>
-  <profile namespace="globus" key="project">_PROJECT_</profile>
-  <profile namespace="globus" key="project">_QUEUE_</profile>
-
-  <filesystem provider="local" url="none" />
-  <workdirectory>{wdir}</workdirectory>
-</pool>
-
--->
-
-</config>

Copied: SwiftApps/SciColSim/bin/convertbest.sh (from rev 5635, SwiftApps/SciColSim/convertbest.sh)
===================================================================
--- SwiftApps/SciColSim/bin/convertbest.sh	                        (rev 0)
+++ SwiftApps/SciColSim/bin/convertbest.sh	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,6 @@
+#! /bin/sh
+
+for f in best*.txt; do
+  out=$(basename $f .txt).fmt
+  ../showbest.sh <$f >$out
+done

Added: SwiftApps/SciColSim/bin/extract4plots
===================================================================
--- SwiftApps/SciColSim/bin/extract4plots	                        (rev 0)
+++ SwiftApps/SciColSim/bin/extract4plots	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,42 @@
+#!/bin/bash
+
+#usage: ./swiftoutput2plot <swift.outfile>
+
+SWIFTOUTFILE=$1
+
+#extract start time
+TMPDATE=`grep -i progress $SWIFTOUTFILE | head -n 1 | cut -f4-9 -d ' '`
+START_TIME=`date +%s -d "$TMPDATE"`
+
+#extract end time
+TMPDATE=`grep -i progress $SWIFTOUTFILE | tail -n 1 | cut -f4-9 -d ' '`
+END_TIME=`date +%s -d "$TMPDATE"`
+
+#duration
+DIFFTIME=$((END_TIME - START_TIME))
+
+#extract active runs in a file
+grep -o -i "Active:[0-9]*" $SWIFTOUTFILE | awk -F: '{print $2}' > active.txt
+
+#extract successful completions in a file
+grep -o -i "Successfully:[0-9]*" $SWIFTOUTFILE | awk -F: '{print $2}' > cumulative.txt
+
+#prepare tics
+activelines=`wc -l active.txt | awk '{print $1}'`
+cumulines=`wc -l cumulative.txt | awk '{print $1}'`
+
+activelinespertic=`echo "scale=5 ; $DIFFTIME / $activelines" | bc`
+seq 0 $activelinespertic $DIFFTIME > activetics.txt
+
+cumulinespertic=`echo "scale=5 ; $DIFFTIME / $cumulines" | bc`
+seq 0 $cumulinespertic $DIFFTIME > cumultics.txt
+
+#final plot data
+paste activetics.txt active.txt > plot_active.txt
+paste cumultics.txt cumulative.txt > plot_cumulative.txt
+
+grep  "T =" $SWIFTOUTFILE | awk '{print $6}' | cut -c8- | sed 's/....$//' > T.data
+
+grep multi_annealing $SWIFTOUTFILE | grep "1;30" | awk '{print $3}' | cut -c11- | sed 's/....$//' > anneal.data
+
+grep returning $SWIFTOUTFILE | awk '{print $3, $4, $5, $6}' | sed -e 's/'ci='//' -e 's/'cj='//' -e 's/'r.loss='//' -e 's/'r.sdev='//' | sort -t' ' -k 1,2n > multiloss.txt


Property changes on: SwiftApps/SciColSim/bin/extract4plots
___________________________________________________________________
Added: svn:executable
   + *

Copied: SwiftApps/SciColSim/bin/getallparams.sh (from rev 5635, SwiftApps/SciColSim/getallparams.sh)
===================================================================
--- SwiftApps/SciColSim/bin/getallparams.sh	                        (rev 0)
+++ SwiftApps/SciColSim/bin/getallparams.sh	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,10 @@
+#! /bin/sh
+
+# targetinno=${1:-58}
+
+grep 'calling evolve' swift.out | # grep ,$targetinno, | 
+  sed -e 's/^.*\[//' \
+      -e 's/\]$//' \
+      -e 's/,/ /g' \
+      -e 's/\(\......\)[0-9]* /\1 /g' |
+  awk '{print $3, $4, $5}' | uniq 

Added: SwiftApps/SciColSim/bin/getparamtrace.sh
===================================================================
--- SwiftApps/SciColSim/bin/getparamtrace.sh	                        (rev 0)
+++ SwiftApps/SciColSim/bin/getparamtrace.sh	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,9 @@
+#! /bin/sh
+
+targetinno=${1:-58}
+grep 'calling evolve' swift.out | grep ,$targetinno, | 
+  sed -e 's/^.*\[//' \
+      -e 's/\]$//' \
+      -e 's/,/ /g' \
+      -e 's/\(\......\)[0-9]* /\1 /g' |
+  awk '{print $3, $4, $5}' | uniq 


Property changes on: SwiftApps/SciColSim/bin/getparamtrace.sh
___________________________________________________________________
Added: svn:executable
   + *

Copied: SwiftApps/SciColSim/bin/paramtraceall.sh (from rev 5634, SwiftApps/SciColSim/paramtraceall.sh)
===================================================================
--- SwiftApps/SciColSim/bin/paramtraceall.sh	                        (rev 0)
+++ SwiftApps/SciColSim/bin/paramtraceall.sh	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,7 @@
+for ti in $(seq 58 70); do
+  echo
+  echo "=======" $ti ; /home/wilde/AndreysOptimizer/src/getparamtrace.sh $ti
+  echo
+done
+
+

Copied: SwiftApps/SciColSim/bin/showbest.sh (from rev 5634, SwiftApps/SciColSim/showbest.sh)
===================================================================
--- SwiftApps/SciColSim/bin/showbest.sh	                        (rev 0)
+++ SwiftApps/SciColSim/bin/showbest.sh	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,8 @@
+#! /bin/sh
+
+awk '{
+  printf( "N %2d %2d %10.5f %5.2f | %5.2f %10.5f [ %5.2f %5.2f %10.5f %10.5f %10.5f ] %10.5f\n",
+  $2, $3, $4, $5, $7, $8, $10, $11, $12, $13, $14, $16);
+}'
+
+

Deleted: SwiftApps/SciColSim/cf
===================================================================
--- SwiftApps/SciColSim/cf	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/cf	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,7 +0,0 @@
-wrapperlog.always.transfer=true
-sitedir.keep=true
-execution.retries=3
-lazy.errors=true
-status.mode=provider
-use.provider.staging=false
-provider.staging.pin.swiftfiles=false

Deleted: SwiftApps/SciColSim/colortext.swift
===================================================================
--- SwiftApps/SciColSim/colortext.swift	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/colortext.swift	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,90 +0,0 @@
-global string e33; // = @sprintf("\\e") or  @arg("e33");
-
-if( @arg("e33","null") == "null" ) {
-  e33 = @sprintf("\\e");
-}
-else {
-  e33 = @arg("e33");
-}
-
-global string endEscape = "[0m";
-
-// Color codes used in Andrey's cpp optimizer:
-
-global int Black = 0;
-global int Blue = 1;
-global int BlackOnBlue = 2;
-global int Pink = 3;
-global int GoldOnBlue = 4;
-global int BlueOnGray = 5;
-global int Red = 6;
-global int Gold = 7;
-global int BlueOnGold = 8;
-global int Gray = 9;
-global int BlackOnGray = 10;
-
-# Black = 0;	   
-# Blue = 1;	   
-# BlackOnBlue = 2; 
-# Pink = 3;	   
-# GoldOnBlue = 4;  
-# BlueOnGray = 5;  
-# Red = 6;	   
-# Gold = 7;	   
-# BlueOnGold = 8;  
-# Gray = 9;	   
-# BlackOnGray = 10;
-
-global string colorCode[] = [
- "[1;29m",      # Black = 0;	   
- "[1;34m",	# Blue = 1;	   
- "[1;44m",	# BlackOnBlue = 2; 
- "[1;35m",	# Pink = 3;	   
- "[1;33;44m",	# GoldOnBlue = 4;  
- "[1;47;34m",	# BlueOnGray = 5;  
- "[1;1;31m",	# Red = 6;	   
- "[1;1;33m",	# Gold = 7;	   
- "[1;1;43;34m",	# BlueOnGold = 8;  
- "[1;1;37m",	# Gray = 9;	   
- "[1;30;47m",	# BlackOnGray = 10;
-];
-
-(string s) color(int c, string ins)
-{
-  s = @strcat(e33,colorCode[c],ins,e33,endEscape);
-}
-
-(string s) ncolor(int c, string ins) // Can use this version if \\e handling is available in current Swift
-{
-  s = @sprintf(@strcat("\\e",colorCode[c],ins,"\\e",endEscape));  // sprintf applies \\e escape processing
-}
-
-(string s) OLDcolor(int c, string ins)
-{
-    switch(c){
-        case 0:
-            s = @strcat(e33,"[1;29m",ins,e33,"[0m");
-        case 1:
-            s = @strcat(e33,"[1;34m",ins,e33,"[0m");
-        case 2:
-            s = @strcat(e33,"[1;44m",ins,e33,"[0m");
-        case 3:
-            s = @strcat(e33,"[1;35m",ins,e33,"[0m");
-        case 4:
-            s = @strcat(e33,"[1;33;44m",ins,e33,"[0m");
-        case 5:
-            s = @strcat(e33,"[1;47;34m",ins,e33,"[0m");
-        case 6:
-            s = @strcat(e33,"[1;1;31m",ins,e33,"[0m");
-        case 7:
-            s = @strcat(e33,"[1;1;33m",ins,e33,"[0m");
-        case 8:
-            s = @strcat(e33,"[1;1;43;34m",ins,e33,"[0m");
-        case 9:
-            s = @strcat(e33,"[1;1;37m",ins,e33,"[0m");
-        case 10:
-            s = @strcat(e33,"[1;30;47m",ins,e33,"[0m");
-        default:
-            s = ins;
-    }
-}

Copied: SwiftApps/SciColSim/colortext.swift (from rev 5634, SwiftApps/SciColSim/colortext.swift)
===================================================================
--- SwiftApps/SciColSim/colortext.swift	                        (rev 0)
+++ SwiftApps/SciColSim/colortext.swift	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,90 @@
+global string e33; // = @sprintf("\\e") or  @arg("e33");
+
+if( @arg("e33","null") == "null" ) {
+  e33 = @sprintf("\\e");
+}
+else {
+  e33 = @arg("e33");
+}
+
+global string endEscape = "[0m";
+
+// Color codes used in Andrey's cpp optimizer:
+
+global int Black = 0;
+global int Blue = 1;
+global int BlackOnBlue = 2;
+global int Pink = 3;
+global int GoldOnBlue = 4;
+global int BlueOnGray = 5;
+global int Red = 6;
+global int Gold = 7;
+global int BlueOnGold = 8;
+global int Gray = 9;
+global int BlackOnGray = 10;
+
+# Black = 0;	   
+# Blue = 1;	   
+# BlackOnBlue = 2; 
+# Pink = 3;	   
+# GoldOnBlue = 4;  
+# BlueOnGray = 5;  
+# Red = 6;	   
+# Gold = 7;	   
+# BlueOnGold = 8;  
+# Gray = 9;	   
+# BlackOnGray = 10;
+
+global string colorCode[] = [
+ "[1;29m",      # Black = 0;	   
+ "[1;34m",	# Blue = 1;	   
+ "[1;44m",	# BlackOnBlue = 2; 
+ "[1;35m",	# Pink = 3;	   
+ "[1;33;44m",	# GoldOnBlue = 4;  
+ "[1;47;34m",	# BlueOnGray = 5;  
+ "[1;1;31m",	# Red = 6;	   
+ "[1;1;33m",	# Gold = 7;	   
+ "[1;1;43;34m",	# BlueOnGold = 8;  
+ "[1;1;37m",	# Gray = 9;	   
+ "[1;30;47m",	# BlackOnGray = 10;
+];
+
+(string s) color(int c, string ins)
+{
+  s = @strcat(e33,colorCode[c],ins,e33,endEscape);
+}
+
+(string s) ncolor(int c, string ins) // Can use this version if \\e handling is available in current Swift
+{
+  s = @sprintf(@strcat("\\e",colorCode[c],ins,"\\e",endEscape));  // sprintf applies \\e escape processing
+}
+
+(string s) OLDcolor(int c, string ins)
+{
+    switch(c){
+        case 0:
+            s = @strcat(e33,"[1;29m",ins,e33,"[0m");
+        case 1:
+            s = @strcat(e33,"[1;34m",ins,e33,"[0m");
+        case 2:
+            s = @strcat(e33,"[1;44m",ins,e33,"[0m");
+        case 3:
+            s = @strcat(e33,"[1;35m",ins,e33,"[0m");
+        case 4:
+            s = @strcat(e33,"[1;33;44m",ins,e33,"[0m");
+        case 5:
+            s = @strcat(e33,"[1;47;34m",ins,e33,"[0m");
+        case 6:
+            s = @strcat(e33,"[1;1;31m",ins,e33,"[0m");
+        case 7:
+            s = @strcat(e33,"[1;1;33m",ins,e33,"[0m");
+        case 8:
+            s = @strcat(e33,"[1;1;43;34m",ins,e33,"[0m");
+        case 9:
+            s = @strcat(e33,"[1;1;37m",ins,e33,"[0m");
+        case 10:
+            s = @strcat(e33,"[1;30;47m",ins,e33,"[0m");
+        default:
+            s = ins;
+    }
+}

Deleted: SwiftApps/SciColSim/convertbest.sh
===================================================================
--- SwiftApps/SciColSim/convertbest.sh	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/convertbest.sh	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,6 +0,0 @@
-#! /bin/sh
-
-for f in best*.txt; do
-  out=$(basename $f .txt).fmt
-  ../showbest.sh <$f >$out
-done

Copied: SwiftApps/SciColSim/docs/EMAIL (from rev 5634, SwiftApps/SciColSim/EMAIL)
===================================================================
--- SwiftApps/SciColSim/docs/EMAIL	                        (rev 0)
+++ SwiftApps/SciColSim/docs/EMAIL	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,474 @@
+==== Email trail (cronological order):
+
+---------------------------------------------------------------------------------------
+
+On 10/15/11 8:30 PM, "Michael Wilde" <wilde at mcs.anl.gov> wrote:
+
+> Hi Andrey,
+> 
+> I've got a basic serial version of optimizer.cpp running on Beagle (on a login
+> node).  Now Im trying to parallelize it there, and have some questions:
+> 
+> 1) You set NWorkers to a constant, 24. In multi_loss, you have this code:
+> 
+>     for(int i=0; i<Nworkers; i++){
+>         for(int j=0; j<5; j++){
+>             un[i]->set_parameter(params[j],j);
+>         }
+>         for(int i=0; i<Nworkers; i++){
+>             dispatch_group_async(group, CustomQueues[i], ^{
+>                 un[i]->evolve_to_target_and_save(istart, iend, Results,
+> Counters);
+>             });
+>             istart += step;
+>             iend = min(istart+step,N);
+>         }
+>     }
+> 
+> Can you explain the intention here? I think the innermost loop is clear: run
+> evolve() 24 times in parallel, partitioning the istart..iend range among the
+> 24 workers.  But I dont understand the outermost loop, which seems to do the
+> entire inner loop 24 (NWorkers) times. I can understand the idea of doing the
+> entire inner loop some number of times. But from the above, I presume that
+> evolve would be run NWorkers^2 times, or 24*24 times.  Was that the intention?
+> 
+> 2) If you had many processors available (as you do on Beagle) would you want
+> to run set NWorkers higher? I think you mentioned something in out discussion
+> Friday about "a thousand" workers being useful. Ie, NWorkers for the innermost
+> loop could be 1000.  Was that really what you meant? What would be a
+> mathematically/scientifically useful value for the NWorkers in the innermost
+> loop?
+> 
+> Further: if you do want an NWorkers >> 24, would you still do the evolve
+> NWorkers^2 times?  I dont think you'd really want 1000*1000 = 1M evolve calls,
+> given how many outer loops you have at higher levels of the code, including in
+> the Python wrapper.
+> 
+> 3) We'll need to discuss what values make sense for the outer loops
+> (multi_annealing and the .py wrapper) once I have this working in parallel.
+> 
+> 4) Can you give me a sentence or two about what this code is doing?  I think I
+> understand the outer annealing logic, but I have not dug into the code within
+> evolve...() at all. I think you might have explained it once back in January
+> (before you recoded in C) but I dont recall. If you have a paper or a web page
+> on what youre computing here, that would be interesting for me to read, and to
+> help write a slide on this for the Beagle review.
+
+
+-----
+
+
+Re: Status and questions on optimizer code
+From :	 andrey rzhetsky <arzhetsk at medicine.bsd.uchicago.edu>
+Subject :	Re: Status and questions on optimizer code
+To :	 Michael Wilde <wilde at mcs.anl.gov>	
+Sun, Oct 16, 2011 08:28 AM
+Hi Mike,
+
+1. I think, you just uncovered a stupid bug on my part -- thank you!  There
+should be only one loop (the outer one).
+2. Yes, of course -- 1000, or even 10000 (I can increase the number of
+repeats then).
+3. OK
+4. The code simulates exploration of a chemical network by a research
+community.  The five major parameters determine the strategy of exploration,
+"target" is the number of new interactions discovered and the loss is the
+number of experiments per one new positive finding.  I can provide you with
+figures and slides, if that would help.
+
+Thank you!
+
+With kind regards,
+
+Andrey
+
+
+---------------------------------------------------------------------------------------
+
+
+On 10/16/11 9:13 AM, "Michael Wilde" <wilde at mcs.anl.gov> wrote:
+
+> Hi Andrey,
+> 
+> Looking deeper, I think the bug was simply that the first for() statement
+> should have enclosed just the parameter setting loop.  In other words, the top
+> of multi_loss should start with:
+> 
+>     for(int i=0; i<Nworkers; i++){
+>         for(int j=0; j<5; j++){
+>             un[i]->set_parameter(params[j],j);
+>         }
+>     }
+> 
+> Then the real work is done by the next loop:
+>     for(i=0; i<Nworkers; i++){
+> 
+> Can you confirm that this looks correct to you?
+> 
+> I made that change, and the code now seems to run as I would expect. I will
+> send you some output as soon as I clean up my debugging output.
+> 
+> Next, I made the code run with 24-way parallelism on a Beagle login node using
+> "OpenMP", simply by adding one "pragma" statement in front of the main worker
+> loop above. So that part of the code now looks like this:
+> 
+>     int i;
+>     #pragma omp parallel for private (i)
+>     for(i=0; i<Nworkers; i++){
+> 
+> and each call to evolve...() is now done in parallel (with all the Mac
+> dispatch statements commented out).  I will test, but I *think* that the same
+> code will run just as well in parallel on your multicore Macs, perhaps just a
+> *tiny* bit slower than under Grand Central Dispatch (likely not a noticeable
+> difference).
+> 
+> Now, we have 2 choices:
+> 
+> 1) I can simply replace the Python driver script with a Swift script, to do
+> many runs of the optimizer in parallel.  That would give you the ability to
+> run *many* 24-core optimization runs in parallel, each using 24 cores.  So for
+> example, in your current Python script you do this:
+> 
+> for target in range(58,1009,50):
+>   for i in range(15):
+> 
+> So thats about 20 x 15 = 300 invocations of optimizer. I *think* that each of
+> these runs is totally independent and can run in parallel, correct?
+> 
+> So A simple Swift script not much longer than the Python script, along with a
+> few beagle-specific configuration files, will enable all 300 jobs to run in
+> parallel, giving you 300 x 24 (=7200) cores running in parallel. Of course,
+> you can seldom *get* that many cores because the machine is heavily loaded.
+> But you may be able to get 10-30 nodes on a daily basis.  We'll need to
+> experiment with this.
+> 
+> As a *next* step after, we should consider the benefits of changing the value
+> of NWorkers to > 24.  24 is the "easy" limit on Beagle because we can get
+> 24-way parallelism on each node with just that one "pragma" statement.  We can
+> get much greater parallelism with Swift in the inner loop, but for that we
+> need to break up the program a bit more, to have Swift run the inner loop as a
+> separate program, and then return the aggregated results in a file. Even for
+> this option, there are two alternative methods:
+> 
+> - we make optimizer call Swift once for each round of parallel annealing. This
+> is fairly easy. It is somewhat limiting to overall parallelism, in that only
+> one round at a time can run. But It may be very adequate.
+> 
+> - we break the program up further into parallelizable chunks, in which case
+> you have a lot of flexibility and the work always gets done in a near-optimal
+> manner regardless of the shape of a given optimization run (in terms of the
+> various nested loop sizes and evolve() execution times.
+> 
+> I think we'll need to discuss this in person over a whiteboard, but I think I
+> have enough knowledge of the program to at least show you a few alternatives.
+> 
+> The main question at the moment, I think, is simply to understand the
+> math/science benefits of extending NWorkers beyond the "low hanging fruit"
+> limit of 24.  What is your assessment of that benefit, Andrey?
+> 
+
+-----
+
+----- Forwarded Message -----
+From: "andrey rzhetsky" <arzhetsk at medicine.bsd.uchicago.edu>
+To: "Michael Wilde" <wilde at mcs.anl.gov>
+Sent: Sunday, October 16, 2011 12:08:25 PM
+Subject: Re: Status and questions on optimizer code
+
+Mike,
+
+It would be fantastic to have 1000 or 10000 workers (with larger number of
+re-runs -- it would improve precision of my analysis drastically!).
+
+All the very best,
+
+Andrey
+
+---------------------------------------------------------------------------------------
+
+On 10/17/11 8:29 AM, "Michael Wilde" <wilde at mcs.anl.gov> wrote:
+
+> Hi Andrey,
+> 
+> Can we meet today to discuss the optimizer? I'd like to show you what Ive done
+> and discuss with you next steps towards getting you running on Beagle. I can
+> meet any time from 10:30 to 3:00.
+> 
+> Do you already have a CI and Beagle login and a project set up for Beagle use?
+> If not, we should get that started.
+> 
+> On the technical side, I have a question about the the typical shape of your
+> optimization runs.
+> 
+> With the sample Python script you gave me, I think we have the following
+> nested iterations in the code:
+> 
+> 20 targets (parallel)
+>   15 repeats (parallel)
+>     100 Annealing_cycles (serial)
+>        6 repeats (serial)
+>          1000 to 10000 annealing_repeats (parallel)   # NOTE (1/29 mw): should this be called "reruns"?
+>            evolve()
+> 
+> The main question I have at this point is regarding the strategy for
+> increasing the innermost annealing repeats (currently 1,000 divided among 24
+> workers; desired to increase to 10,000).
+> 
+> The outermost loops in my Swift tests are done in parallel. Thus we can have a
+> 300 optimizations going in parallel and 24 annealings in parallel for a total
+> of 7,200 parallel tasks.
+> 
+> The question is: if you will always have a sizeable number of parallel
+> iterations in the outer loops, we dont need to change anything in the inner
+> loop to get more parallelism. In other words, we already have more parallelism
+> than we have CPUs available.
+> 
+> 7200 CPUs is about 42% of the overall Beagle system.  It will be very rare
+> that we we could get that many cores all at once.  But  think we can regularly
+> get say 500 to 2000 cores on a daily basis.
+> 
+> On the other hand, if you expect to regularly run tests of *single* annealing
+> cycles and want to speed those up, then indeed it may be worth changing the
+> code structure.
+> 
+> When me meet I'll try to give you an idea of whats involved. Basically we need
+> to change the structure of the annealing loop to create a function
+> "multi_loss_setup" as a separate executable which defines the annealing
+> parameters and writes them to a file; make multi_loss a separate executable;
+> create another executable "multi_loss_summarize" which reduces the results.
+> We can probably combine multi_loss_summarize into multi_loss_setup.
+> 
+> This is not very hard to do, but still sounds to me like a week of programming
+> to get all all restructured and tested.  Before investing that effort, we
+> should discuss if it will give you any additional performance gains over just
+> running many optimizations in parallel.
+> 
+> I need to run timings on the annealing cycles to see how that change across
+> the parameter space, to see if we can just increase the repeats to 10,000 with
+> no changes to the code. I think the feasibility of doing this the "easy way"
+> is based on how long the longest annealings take at the high end of the
+> parameter space.
+> 
+> Regards,
+> 
+> - Mike
+
+-----
+
+----- Forwarded Message -----
+From: "Andrey Rzhetsky" <arzhetsk at medicine.bsd.uchicago.edu>
+To: "Michael Wilde" <wilde at mcs.anl.gov>
+Sent: Monday, October 17, 2011 8:40:17 AM
+Subject: Re: Meet today to discuss optimizer?
+
+Hi Mike,
+
+The 6 (or more) annealing repeats can be run in parallel too.
+
+Unfortunately, around 10:15 I have to rush to Evanston to CBC meeting for
+the rest of the day (we can chat before, if you have a minute, I am in my
+office).
+
+I don't have Beagle login, unfortunately.
+
+Typically, I will have a sizeable outer loop, so, probably, the current
+24-worker setup is fine.
+
+Thank you very much for helping me out!
+
+All the best,
+
+Andrey
+
+
+---------------------------------------------------------------------------------------
+
+On 10/18/11 1:52 PM, "Michael Wilde" <wilde at mcs.anl.gov> wrote:
+
+> Hi Andrey,
+> 
+> Here's a quick update:
+> 
+> - I am now running the optimizer on Beagle compute nodes under Swift.
+> 
+> I attach a few tar files of sample runs at reduced parameter values (to shrink
+> the run time for debugging and learning the code's behavior);
+> 
+> Now Im trying to run some subset of the full-length parameters you gave me in
+> the python file.  Ive got 3 Beagle compute nodes allocated at the moment (72
+> cores total) and Im seeing these times from multi_loss with N=1000 repeats:
+> 
+> sandbox$ grep multi_  ./jobs/*/*/output/*.out
+> ./jobs/0/optimizer-01p7lhhk/output/T408.R1.out:multi_loss(N=1000) elapsed
+> time: 122.742 seconds 2.04571 minutes
+> ./jobs/0/optimizer-01p7lhhk/output/T408.R1.out:multi_loss(N=1000) elapsed
+> time: 123.979 seconds 2.06631 minutes
+> ./jobs/0/optimizer-01p7lhhk/output/T408.R1.out:multi_loss(N=1000) elapsed
+> time: 123.624 seconds 2.0604 minutes
+> ./jobs/t/optimizer-t0p7lhhk/output/T958.R1.out:multi_loss(N=1000) elapsed
+> time: 1431.09 seconds 23.8514 minutes
+> ./jobs/x/optimizer-x0p7lhhk/output/T708.R1.out:multi_loss(N=1000) elapsed
+> time: 627.074 seconds 10.4512 minutes
+> ./jobs/x/optimizer-x0p7lhhk/output/T708.R1.out:multi_loss(N=1000) elapsed
+> time: 790.652 seconds 13.1775 minutes
+> 
+> 
+> Each run of optimizer is going to a file name T(target).R(repeat).out
+> 
+> So we're seeing 23.8 mins for 1000 repeats at target=958 and 10-13 mins at
+> target=708. The 1000 repeats are spread over 24 cores each.
+> 
+> Whats your time availability later in the week to discuss this further, and to
+> see if either (a) I can show you how to run this version or (b) we can get a
+> set of production run descriptions from you and you can run them yourself?
+> 
+> In the compressed tar file at http://www.ci.uchicago.edu/~wilde/AR.snap.01.tgz
+> you will find:
+> 
+> - the swift script that I use instead of the Python driver to run the
+> optimizer in parallel (along beagle.xml that specifies scheduler parameters
+> for Beagle like time, cores, queue name and project ID)
+> 
+> - the slightly modified version of optimizer (changes in multi_loss() to
+> correct the loops, changes to use OpenMP instead of Grand Central Dispatch,
+> and a few changes in output logging).
+> 
+> - a few run directories of runs with shortened parameter settings.
+> 
+> If we continue working together on this, we should set up a way to share code
+> using a repository like Subversion (svn).  Thats pretty easy once you master a
+> few basic commands.
+> 
+> Regards,
+> 
+> - Mike
+> 
+> 
+
+-----
+
+----- Forwarded Message -----
+From: "Andrey Rzhetsky" <arzhetsk at medicine.bsd.uchicago.edu>
+To: "Michael Wilde" <wilde at mcs.anl.gov>
+Sent: Tuesday, October 18, 2011 3:57:36 PM
+Subject: Re: Meet today to discuss optimizer?
+
+Mike,
+
+Thank you!  Are you around now?  I would be also happy to carve some time
+tomorrow, if this works for you.
+
+With kind regards,
+
+Andrey
+
+
+---------------------------------------------------------------------------------------
+
+
+On 10/19/11 12:10 PM, "Michael Wilde" <wilde at mcs.anl.gov> wrote:
+
+> Hi Andrey,
+> 
+> Im in meetings today till about 3PM. Are you available at say 3:30 or later?
+> 
+> I did a larger run last night. Only one smaller optimizer run *fully*
+> finished, but many others made significant progress.  The results are at:
+> 
+>   http://www.ci.uchicago.edu/~wilde/AR.optimizer.out.2010.1018.tgz
+> 
+> If you have time, could you take a look at that run and see if the
+> optimizations look like they have been running as expected? Ive made only a
+> few cosmetic changes to your debug output.
+> 
+> I submitted the run at 21:20; it started running at about 21:27; by about
+> 23:10 it had acquired 12 nodes (x 24 cores each). It ended about 23:18 when
+> the first job exceeded its time limit of 5 hours. Im still trying to calibrate
+> how much time each optimizer invocation needs, and whether some of the
+> internal iterations can be further spread out.  Also how to organize the run
+> so that optimizations that time out  can be re-run with the smallest
+> reasonable failure unit.
+> 
+>
+
+---------------------------------------------------------------------------------------
+
+
+----- Forwarded Message -----
+From: "andrey rzhetsky" <arzhetsk at medicine.bsd.uchicago.edu>
+To: "Michael Wilde" <wilde at mcs.anl.gov>
+Sent: Wednesday, October 26, 2011 8:40:21 PM
+Subject: Re: Question on inner annealing loop
+
+Mike,
+
+
+> Im confused on 3 points here:
+> 
+> - the inner loop would always be done between 1 and 5 times, right?
+
+Correct.
+
+> - could each of those times really be done in parallel? (I'll try to determine
+> this by inspection).
+
+Not really -- the acceptance of parameter changes depends on the loss in
+between.
+
+> - when we last met in your office, I *thought* you indicated that this inner
+> loop could be done just *once*.  Was that what you meant?  And if so, for
+> which of the 5 vars?
+
+Nope, has to be repeated over and over.
+
+All the very best,
+
+Andrey
+
+
+---------------------------------------------------------------------------------------
+
+
+On 10/26/11 10:42 PM, "Michael Wilde" <wilde at mcs.anl.gov> wrote:
+
+> OK, all that makes sense, Andrey. But then do you recall what you suggested
+> when we met?
+> 
+> Lets label the loops as follows:
+> 
+> a) 20 targets (parallel)
+> b)   15 repeats (parallel)
+> c)     100 Annealing_cycles (serial)
+> d)        6 repeats (serial)
+> e)         1000 to 10000 annealing_repeats (parallel)
+> f)            evolve()
+> 
+> What I recalled from our last discussion was that I should reduce loop (c)
+> from 100 to 50 or 25, and loop (d) to 1.  But since reducing loop (d) doesn't
+> make sense, do you recall suggesting any other reduction?
+> 
+> If not, no problem, I think I know how to proceed.
+> 
+> Thanks,
+> 
+> - Mike
+> 
+> 
+-----
+
+----- Forwarded Message -----
+From: "andrey rzhetsky" <arzhetsk at medicine.bsd.uchicago.edu>
+To: "Michael Wilde" <wilde at mcs.anl.gov>
+Sent: Thursday, October 27, 2011 2:54:06 AM
+Subject: Re: Question on inner annealing loop
+
+Hi Mike,
+
+I suggested reducing (b) to 1.
+
+With kind regards,
+
+Andrey
+
+
+

Copied: SwiftApps/SciColSim/docs/TimingEstimation.txt (from rev 5634, SwiftApps/SciColSim/TimingEstimation.txt)
===================================================================
--- SwiftApps/SciColSim/docs/TimingEstimation.txt	                        (rev 0)
+++ SwiftApps/SciColSim/docs/TimingEstimation.txt	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,95 @@
+SciColSim on Beagle
+===================
+
+Loop Structure
+--------------
+Following is the loop structure for the SciColSim application at 'full-scale' values:
+
+~20 target innovation values
+ ~15 repeats
+  100 annealing cycles (serial)
+     3 non-fixed vars    (serial)
+       1,000 reruns (=>evolve_reruns/reruns_per_app))
+         call evolve (1 to 50 seconds per rerun)
+
+
+Total number of jobs are given by the following expression:
+
+Number_of_jobs = target_innovation_values x repeats x fixed_reps x annealing_cycles x evolve_reruns/reruns_per_app x num_workers
+
+               = ceil((max_range - min_range)/range_inc) x repeats x fixed_reps x annealing_cycles x evolve_reruns/reruns_per_app x num_workers
+               
+               = ceil((1009 - 58)/50) x 15 x 3 x 100 x 960/192 x 24
+
+               = 20 x 15 x 3 x 100 x 5 x 24
+
+               = 10,800,000
+
+               = 20 x 15 x 3 x 100 x 1000 x 10 = total core-seconds
+               
+               = 900M core seconds
+               
+               = 900M / 1000 = 900,000 seconds on 1000 cores
+
+               = 250 hours = 10 days
+
+
+
+Estimated Runtime on a small scale (2 laptops or 24 cores)
+
+         =10,800,000/24 x (1 to 50 sec)
+
+         =450,000 to 22,500,000 seconds
+
+         =5.2 hours to 260 days
+
+Estimated Runtime on medium scale (40 Beagle nodes or 960 cores) 
+
+         =10,800,000/960 x (1 to 50 sec)
+        
+         =11,250 to 562,500 seconds
+        
+         =3.12 hours to 6.5 days
+
+
+Atomic job times of application
+multi_loss(N=1, target=58) elapsed time: 0.116707 seconds 0.00194512 minutes
+
+multi_loss(N=1, target=108) elapsed time: 0.26613 seconds 0.0044355 minutes
+
+multi_loss(N=1, target=158) elapsed time: 0.379991 seconds 0.00633318 minutes
+
+multi_loss(N=1, target=208) elapsed time: 0.576198 seconds 0.0096033 minutes
+
+multi_loss(N=1, target=258) elapsed time: 1.50121 seconds 0.0250203 minutes
+
+multi_loss(N=1, target=308) elapsed time: 1.09665 seconds 0.0182775 minutes
+
+multi_loss(N=1, target=358) elapsed time: 1.49523 seconds 0.0249205 minutes
+
+multi_loss(N=1, target=408) elapsed time: 2.3056 seconds 0.0384267 minutes
+
+multi_loss(N=1, target=458) elapsed time: 3.54418 seconds 0.0590697 minutes
+
+multi_loss(N=1, target=508) elapsed time: 5.10912 seconds 0.085152 minutes
+
+multi_loss(N=1, target=558) elapsed time: 6.88611 seconds 0.114768 minutes
+
+multi_loss(N=1, target=608) elapsed time: 8.7683 seconds 0.146138 minutes
+
+multi_loss(N=1, target=658) elapsed time: 10.0665 seconds 0.167775 minutes
+
+multi_loss(N=1, target=708) elapsed time: 12.7259 seconds 0.212098 minutes
+
+multi_loss(N=1, target=758) elapsed time: 14.8005 seconds 0.246675 minutes
+
+multi_loss(N=1, target=808) elapsed time: 16.8803 seconds 0.281338 minutes
+
+multi_loss(N=1, target=858) elapsed time: 19.7864 seconds 0.329774 minutes
+
+multi_loss(N=1, target=908) elapsed time: 23.4506 seconds 0.390843 minutes
+
+multi_loss(N=1, target=958) elapsed time: 28.7667 seconds 0.479445 minutes
+
+multi_loss(N=1, target=1008) elapsed time: 49.0841 seconds 0.818069 minutes
+

Copied: SwiftApps/SciColSim/docs/atomic_times.png (from rev 5634, SwiftApps/SciColSim/atomic_times.png)
===================================================================
(Binary files differ)

Copied: SwiftApps/SciColSim/docs/atomic_times.txt (from rev 5634, SwiftApps/SciColSim/atomic_times.txt)
===================================================================
--- SwiftApps/SciColSim/docs/atomic_times.txt	                        (rev 0)
+++ SwiftApps/SciColSim/docs/atomic_times.txt	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,21 @@
+ 58  0.116707 
+108  0.26613 
+158  0.379991
+208  0.576198
+258  1.50121
+308  1.09665
+358  1.49523
+408  2.3056
+458  3.54418
+508  5.10912
+558  6.88611
+608  8.7683
+658 10.0665
+708 12.7259
+758 14.8005
+808 16.8803
+858 19.7864
+908 23.4506
+958 28.7667
+1008 49.0841
+

Copied: SwiftApps/SciColSim/docs/plot_active.txt (from rev 5634, SwiftApps/SciColSim/plot_active.txt)
===================================================================
--- SwiftApps/SciColSim/docs/plot_active.txt	                        (rev 0)
+++ SwiftApps/SciColSim/docs/plot_active.txt	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,7767 @@
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Copied: SwiftApps/SciColSim/docs/plot_cumulative.txt (from rev 5634, SwiftApps/SciColSim/plot_cumulative.txt)
===================================================================
--- SwiftApps/SciColSim/docs/plot_cumulative.txt	                        (rev 0)
+++ SwiftApps/SciColSim/docs/plot_cumulative.txt	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,7989 @@
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Copied: SwiftApps/SciColSim/docs/plot_ready_jobs.txt (from rev 5634, SwiftApps/SciColSim/plot_ready_jobs.txt)
===================================================================
--- SwiftApps/SciColSim/docs/plot_ready_jobs.txt	                        (rev 0)
+++ SwiftApps/SciColSim/docs/plot_ready_jobs.txt	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,6239 @@
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Copied: SwiftApps/SciColSim/docs/plotit (from rev 5634, SwiftApps/SciColSim/plotit)
===================================================================
--- SwiftApps/SciColSim/docs/plotit	                        (rev 0)
+++ SwiftApps/SciColSim/docs/plotit	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,42 @@
+set terminal png enhanced 
+#set term postscript eps enhanced 
+#set terminal svg enhanced size 1000 1000
+#set style line 1 linecolor rgb "blue"
+set output "activeplot.png"
+set nokey
+set xlabel "Time in sec"
+set ylabel "number of active jobs"
+set title "Active jobs"
+plot "plot_active.txt" using 1:2 with line
+
+set output "cumulativeplot-openmp.png"
+set xlabel "Time in seconds"
+set ylabel "number of completed jobs"
+set title "Cumulative SciColSim-openMP jobs"
+plot "plot_cumulative.txt" using 1:($2*24) with lines
+
+set output "cumulativeplot.png"
+set xlabel "Time in seconds"
+set ylabel "number of completed jobs"
+set title "Cumulative jobs"
+plot "plot_cumulative.txt" using 1:2 with lines
+
+set output "scs.png"
+set xlabel "Evolution"
+set ylabel "Value of T"
+set title "SciColSim evolution Results"
+plot "T.data" using 1 with lines 
+
+set output "scs_loss.png"
+set title "SciColSim evolution loss Results"
+set xlabel "Evolution"
+set ylabel "Value of loss(AR)"
+plot "anneal.data" using 1 with lines 
+
+set output "multiloss.png"
+set title "SciColSim evolution loss Results"
+set key auto
+set yrange [0:200]
+set xlabel "Evolution"
+set ylabel "loss"
+plot "multiloss.txt" using 3 with lines title "multiloss mean val",  "multiloss.txt" using ($3+$4) with lines title "+stddev", "multiloss.txt" using ($3-$4) with lines title "-stddev"

Copied: SwiftApps/SciColSim/docs/sample.swift.output (from rev 5634, SwiftApps/SciColSim/sample.swift.output)
===================================================================
--- SwiftApps/SciColSim/docs/sample.swift.output	                        (rev 0)
+++ SwiftApps/SciColSim/docs/sample.swift.output	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,528 @@
+Swift 0.93 swift-r5483 cog-r3339
+
+RunID: 20120130-1217-63kot916
+Progress:  time: Mon, 30 Jan 2012 12:17:12 -0600
+multi_loss appCalls=1
+multi_loss: entered: ci=0 cj=0 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,4.0,50.0,-1.0]
+multi_annealing: AR: i=1 ....T = 2.0
+multi_loss: i=1 calling evolve, args=[0,0,4,50,-1,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:17:16 -0600  Checking status:1
+multi_annealing: AR: initial: 124.861101 +- 3.21017
+multi_loss: returning: ci=0 cj=0 r.loss=124.861101 r.sdev=3.21017
+multi_loss appCalls=1
+multi_annealing: AR: 4.017889129124014 2
+multi_loss: entered: ci=1 cj=2 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,4.017889129124014,50.0,-1.0]
+multi_loss: i=1 calling evolve, args=[0,0,4.017889129124014,50,-1,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:17:19 -0600  Checking status:1  Finished successfully:2
+multi_annealing: AR: 124.117401 +- 3.306
+multi_loss: returning: ci=1 cj=2 r.loss=124.117401 r.sdev=3.306
+multi_annealing: AF: best_opt_some.txt: 58.0,124.117401,0.0,0.0,4.017889129124014,50.0,-1.0,3.306
+multi_annealing: AR: 0.3916097778073887 vs 1.0
+math/min: result=1.0
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: Accepting try_x[j], i=1 j=2
+multi_annealing: Accepting try_x[j], i=1 j=2 try_x[j]=4.017889129124014
+multi_annealing: AR: [1][2] Rejection counts: 0.0 0.0 0.0 0.0 0.0
+
+multi_annealing: AR: 1 ***** Did accept! 0.0 0.0 4.017889129124014 50.0 -1.0
+
+multi_loss appCalls=1
+multi_annealing: AR: 48.711529653164106 3
+multi_loss: entered: ci=1 cj=3 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,4.017889129124014,48.711529653164106,-1.0]
+multi_loss: i=1 calling evolve, args=[0,0,4.017889129124014,48.711529653164106,-1,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:17:22 -0600  Checking status:1  Finished successfully:4
+multi_annealing: AR: 125.18271 +- 3.599503
+multi_loss: returning: ci=1 cj=3 r.loss=125.18271 r.sdev=3.599503
+multi_annealing: AF: best_opt_some.txt: 58.0,125.18271,0.0,0.0,4.017889129124014,48.711529653164106,-1.0,3.599503
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: AR: 0.29338527075939624 vs 0.5870445897168992
+math/min: result=0.5870445897168992
+multi_annealing: Accepting try_x[j], i=1 j=3 try_x[j]=48.711529653164106
+multi_annealing: Accepting try_x[j], i=1 j=3
+multi_annealing: AR: 1 ***** Did accept! 0.0 0.0 4.017889129124014 48.711529653164106 -1.0
+
+multi_annealing: AR: [1][3] Rejection counts: 0.0 0.0 0.0 0.0 0.0
+
+multi_loss appCalls=1
+multi_annealing: AR: -0.0033614536445253362 4
+multi_loss: entered: ci=1 cj=4 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,4.017889129124014,48.711529653164106,-0.0033614536445253362]
+multi_loss: i=1 calling evolve, args=[0,0,4.017889129124014,48.711529653164106,-0.0033614536445253362,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:17:26 -0600  Checking status:1  Finished successfully:6
+multi_annealing: AR: 125.933179 +- 3.36805
+multi_loss: returning: ci=1 cj=4 r.loss=125.933179 r.sdev=3.36805
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: AF: best_opt_some.txt: 58.0,125.933179,0.0,0.0,4.017889129124014,48.711529653164106,-0.0033614536445253362,3.36805
+multi_annealing: AR: 0.6647548795660121 vs 0.6871281283507249
+math/min: result=0.6871281283507249
+multi_annealing: Accepting try_x[j], i=1 j=4
+multi_annealing: Accepting try_x[j], i=1 j=4 try_x[j]=-0.0033614536445253362
+multi_annealing: AR: 1 ***** Did accept! 0.0 0.0 4.017889129124014 48.711529653164106 -0.0033614536445253362
+
+multi_annealing: AR: [1][4] Rejection counts: 0.0 0.0 0.0 0.0 0.0
+
+multi_annealing: AR: i=2 ....T = 1.6762121943865207
+multi_loss appCalls=1
+multi_annealing: AR: 5.284753614883805 2
+multi_loss: entered: ci=2 cj=2 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,48.711529653164106,-0.0033614536445253362]
+multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,48.711529653164106,-0.0033614536445253362,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:17:29 -0600  Checking status:1  Finished successfully:8
+multi_annealing: AR: 126.145367 +- 3.119133
+multi_loss: returning: ci=2 cj=2 r.loss=126.145367 r.sdev=3.119133
+multi_annealing: AF: best_opt_some.txt: 58.0,126.145367,0.0,0.0,5.284753614883805,48.711529653164106,-0.0033614536445253362,3.119133
+multi_annealing: AR: 0.8343453829181197 vs 0.8810967932193764
+math/min: result=0.8810967932193764
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: Accepting try_x[j], i=2 j=2 try_x[j]=5.284753614883805
+multi_annealing: Accepting try_x[j], i=2 j=2
+multi_annealing: AR: 2 ***** Did accept! 0.0 0.0 5.284753614883805 48.711529653164106 -0.0033614536445253362
+
+multi_annealing: AR: [2][2] Rejection counts: 0.0 0.0 0.0 0.0 0.0
+
+multi_loss appCalls=1
+multi_annealing: AR: 47.49006278600924 3
+multi_loss: entered: ci=2 cj=3 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,47.49006278600924,-0.0033614536445253362]
+multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,47.49006278600924,-0.0033614536445253362,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:17:33 -0600  Checking status:1  Finished successfully:10
+multi_annealing: AR: 125.831434 +- 3.344003
+multi_loss: returning: ci=2 cj=3 r.loss=125.831434 r.sdev=3.344003
+multi_annealing: AF: best_opt_some.txt: 58.0,125.831434,0.0,0.0,5.284753614883805,47.49006278600924,-0.0033614536445253362,3.344003
+multi_annealing: AR: 0.7961447191161577 vs 1.0
+math/min: result=1.0
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: Accepting try_x[j], i=2 j=3
+multi_annealing: Accepting try_x[j], i=2 j=3 try_x[j]=47.49006278600924
+multi_annealing: AR: 2 ***** Did accept! 0.0 0.0 5.284753614883805 47.49006278600924 -0.0033614536445253362
+
+multi_annealing: AR: [2][3] Rejection counts: 0.0 0.0 0.0 0.0 0.0
+
+multi_loss appCalls=1
+multi_annealing: AR: 0.2830038287653472 4
+multi_loss: entered: ci=2 cj=4 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,47.49006278600924,0.2830038287653472]
+multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,47.49006278600924,0.2830038287653472,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:17:36 -0600  Checking status:1  Finished successfully:12
+multi_annealing: AR: 123.989674 +- 3.372759
+multi_loss: returning: ci=2 cj=4 r.loss=123.989674 r.sdev=3.372759
+multi_annealing: AF: best_opt_some.txt: 58.0,123.989674,0.0,0.0,5.284753614883805,47.49006278600924,0.2830038287653472,3.372759
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: AR: 0.19911890147442235 vs 1.0
+math/min: result=1.0
+multi_annealing: Accepting try_x[j], i=2 j=4
+multi_annealing: Accepting try_x[j], i=2 j=4 try_x[j]=0.2830038287653472
+multi_annealing: AR: [2][4] Rejection counts: 0.0 0.0 0.0 0.0 0.0
+
+multi_annealing: AR: 2 ***** Did accept! 0.0 0.0 5.284753614883805 47.49006278600924 0.2830038287653472
+
+multi_annealing: AR: i=3 ....T = 1.4048436603050374
+multi_loss appCalls=1
+multi_annealing: AR: 6.751519052449282 2
+multi_loss: entered: ci=3 cj=2 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,6.751519052449282,47.49006278600924,0.2830038287653472]
+multi_loss: i=1 calling evolve, args=[0,0,6.751519052449282,47.49006278600924,0.2830038287653472,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:17:39 -0600  Checking status:1  Finished successfully:14
+multi_loss: returning: ci=3 cj=2 r.loss=125.83918 r.sdev=3.276288
+multi_annealing: AR: 125.83918 +- 3.276288
+multi_annealing: AF: best_opt_some.txt: 58.0,125.83918,0.0,0.0,6.751519052449282,47.49006278600924,0.2830038287653472,3.276288
+math/min: result=0.2680663186298088
+multi_annealing: AR: 3,2 3 Did not accept: 6.751519052449282 (2)
+multi_annealing: AR: 0.6852396383450929 vs 0.2680663186298088
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: AR: 0.0 0.0 6.751519052449282 47.49006278600924 0.2830038287653472
+multi_loss appCalls=1
+multi_annealing: AR: 49.01109219848105 3
+multi_loss: entered: ci=3 cj=3 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,49.01109219848105,0.2830038287653472]
+multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,49.01109219848105,0.2830038287653472,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:17:42 -0600  Checking status:1  Finished successfully:16
+multi_loss: returning: ci=3 cj=3 r.loss=126.260409 r.sdev=3.474799
+multi_annealing: AF: best_opt_some.txt: 58.0,126.260409,0.0,0.0,5.284753614883805,49.01109219848105,0.2830038287653472,3.474799
+multi_annealing: AR: 126.260409 +- 3.474799
+math/min: result=0.1986200941793068
+multi_annealing: AR: 0.6989515725257741 vs 0.1986200941793068
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: AR: 3,3 3 Did not accept: 49.01109219848105 (3)
+multi_annealing: AR: 0.0 0.0 5.284753614883805 49.01109219848105 0.2830038287653472
+multi_loss appCalls=1
+multi_annealing: AR: 0.7184988677898592 4
+multi_loss: entered: ci=3 cj=4 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,47.49006278600924,0.7184988677898592]
+multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,47.49006278600924,0.7184988677898592,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:17:45 -0600  Checking status:1  Finished successfully:18
+multi_loss: returning: ci=3 cj=4 r.loss=123.593465 r.sdev=3.286319
+multi_annealing: AR: 123.593465 +- 3.286319
+multi_annealing: AF: best_opt_some.txt: 58.0,123.593465,0.0,0.0,5.284753614883805,47.49006278600924,0.7184988677898592,3.286319
+multi_annealing: AR: 0.10970841947076293 vs 1.0
+multi_annealing: AF: max_dist.txt - tbd
+math/min: result=1.0
+multi_annealing: Accepting try_x[j], i=3 j=4
+multi_annealing: Accepting try_x[j], i=3 j=4 try_x[j]=0.7184988677898592
+multi_annealing: AR: 3 ***** Did accept! 0.0 0.0 5.284753614883805 47.49006278600924 0.7184988677898592
+
+multi_annealing: AR: [3][4] Rejection counts: 0.0 0.0 1.0 1.0 0.0
+
+multi_annealing: AR: i=4 ....T = 1.1774080373049494
+multi_loss appCalls=1
+multi_annealing: AR: 4.4255186274220115 2
+multi_loss: entered: ci=4 cj=2 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,4.4255186274220115,47.49006278600924,0.7184988677898592]
+multi_loss: i=1 calling evolve, args=[0,0,4.4255186274220115,47.49006278600924,0.7184988677898592,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:17:49 -0600  Checking status:1  Finished successfully:20
+multi_loss: returning: ci=4 cj=2 r.loss=126.444622 r.sdev=3.25147
+multi_annealing: AR: 126.444622 +- 3.25147
+multi_annealing: AF: best_opt_some.txt: 58.0,126.444622,0.0,0.0,4.4255186274220115,47.49006278600924,0.7184988677898592,3.25147
+multi_annealing: AR: 0.9535428038358379 vs 0.08878355131476454
+math/min: result=0.08878355131476454
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: AR: 4,2 4 Did not accept: 4.4255186274220115 (2)
+multi_annealing: AR: 0.0 0.0 4.4255186274220115 47.49006278600924 0.7184988677898592
+multi_loss appCalls=1
+multi_annealing: AR: 46.92686166375319 3
+multi_loss: entered: ci=4 cj=3 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,46.92686166375319,0.7184988677898592]
+multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,46.92686166375319,0.7184988677898592,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:17:52 -0600  Checking status:1  Finished successfully:22
+multi_annealing: AR: 124.963375 +- 3.41926
+multi_loss: returning: ci=4 cj=3 r.loss=124.963375 r.sdev=3.41926
+multi_annealing: AF: best_opt_some.txt: 58.0,124.963375,0.0,0.0,5.284753614883805,46.92686166375319,0.7184988677898592,3.41926
+multi_annealing: AR: 0.4500207377949753 vs 0.312392025102438
+math/min: result=0.312392025102438
+multi_annealing: AR: 4,3 4 Did not accept: 46.92686166375319 (3)
+multi_annealing: AR: 0.0 0.0 5.284753614883805 46.92686166375319 0.7184988677898592
+multi_annealing: AF: max_dist.txt - tbd
+multi_loss appCalls=1
+multi_annealing: AR: -0.3320316742658884 4
+multi_loss: entered: ci=4 cj=4 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,47.49006278600924,-0.3320316742658884]
+multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,47.49006278600924,-0.3320316742658884,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:17:55 -0600  Checking status:1  Finished successfully:24
+multi_annealing: AR: 125.407828 +- 3.018498
+multi_loss: returning: ci=4 cj=4 r.loss=125.407828 r.sdev=3.018498
+multi_annealing: AF: best_opt_some.txt: 58.0,125.407828,0.0,0.0,5.284753614883805,47.49006278600924,-0.3320316742658884,3.018498
+multi_annealing: AR: 0.8555641933973892 vs 0.21417097212905944
+math/min: result=0.21417097212905944
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: AR: 0.0 0.0 5.284753614883805 47.49006278600924 -0.3320316742658884
+multi_annealing: AR: 4,4 4 Did not accept: -0.3320316742658884 (4)
+multi_annealing: AR: i=5 ....T = 0.9867928549496278
+multi_loss appCalls=1
+multi_annealing: AR: 6.833327620921641 2
+multi_loss: entered: ci=5 cj=2 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,6.833327620921641,47.49006278600924,0.7184988677898592]
+multi_loss: i=1 calling evolve, args=[0,0,6.833327620921641,47.49006278600924,0.7184988677898592,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:17:58 -0600  Checking status:1  Finished successfully:26
+multi_annealing: AR: 125.250487 +- 3.078819
+multi_loss: returning: ci=5 cj=2 r.loss=125.250487 r.sdev=3.078819
+multi_annealing: AF: best_opt_some.txt: 58.0,125.250487,0.0,0.0,6.833327620921641,47.49006278600924,0.7184988677898592,3.078819
+multi_annealing: AR: 0.44288167684075663 vs 0.18652324111077032
+math/min: result=0.18652324111077032
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: AR: 5,2 5 Did not accept: 6.833327620921641 (2)
+multi_annealing: AR: 0.0 0.0 6.833327620921641 47.49006278600924 0.7184988677898592
+multi_loss appCalls=1
+multi_annealing: AR: 46.49027442733761 3
+multi_loss: entered: ci=5 cj=3 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,46.49027442733761,0.7184988677898592]
+multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,46.49027442733761,0.7184988677898592,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:18:01 -0600  Checking status:1  Finished successfully:28
+multi_annealing: AR: 124.663101 +- 3.485893
+multi_loss: returning: ci=5 cj=3 r.loss=124.663101 r.sdev=3.485893
+multi_annealing: AF: best_opt_some.txt: 58.0,124.663101,0.0,0.0,5.284753614883805,46.49027442733761,0.7184988677898592,3.485893
+multi_annealing: AR: 0.42913823795768913 vs 0.33825612299835406
+math/min: result=0.33825612299835406
+multi_annealing: AR: 0.0 0.0 5.284753614883805 46.49027442733761 0.7184988677898592
+multi_annealing: AR: 5,3 5 Did not accept: 46.49027442733761 (3)
+multi_annealing: AF: max_dist.txt - tbd
+multi_loss appCalls=1
+multi_annealing: AR: 2.2333217492423234 4
+multi_loss: entered: ci=5 cj=4 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,47.49006278600924,2.2333217492423234]
+multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,47.49006278600924,2.2333217492423234,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:18:05 -0600  Checking status:1  Finished successfully:30
+multi_annealing: AR: 125.856136 +- 3.415745
+multi_loss: returning: ci=5 cj=4 r.loss=125.856136 r.sdev=3.415745
+multi_annealing: AF: best_opt_some.txt: 58.0,125.856136,0.0,0.0,5.284753614883805,47.49006278600924,2.2333217492423234,3.415745
+multi_annealing: AR: 0.5684553851797784 vs 0.10096772364295076
+math/min: result=0.10096772364295076
+multi_annealing: AR: 5,4 5 Did not accept: 2.2333217492423234 (4)
+multi_annealing: AR: 0.0 0.0 5.284753614883805 47.49006278600924 2.2333217492423234
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: AR: i=6 ....T = 0.8270371084000275
+multi_loss appCalls=1
+multi_annealing: AR: 3.928981105935937 2
+multi_loss: entered: ci=6 cj=2 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,3.928981105935937,47.49006278600924,0.7184988677898592]
+multi_loss: i=1 calling evolve, args=[0,0,3.928981105935937,47.49006278600924,0.7184988677898592,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:18:08 -0600  Checking status:1  Finished successfully:32
+multi_annealing: AR: 125.944813 +- 3.343539
+multi_loss: returning: ci=6 cj=2 r.loss=125.944813 r.sdev=3.343539
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: AR: 0.0737231873586417 vs 0.05824491156028037
+multi_annealing: AF: best_opt_some.txt: 58.0,125.944813,0.0,0.0,3.928981105935937,47.49006278600924,0.7184988677898592,3.343539
+multi_annealing: AR: 6,2 6 Did not accept: 3.928981105935937 (2)
+math/min: result=0.05824491156028037
+multi_annealing: AR: 0.0 0.0 3.928981105935937 47.49006278600924 0.7184988677898592
+multi_loss appCalls=1
+multi_annealing: AR: 47.456980868107266 3
+multi_loss: entered: ci=6 cj=3 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,47.456980868107266,0.7184988677898592]
+multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,47.456980868107266,0.7184988677898592,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:18:11 -0600  Checking status:1  Finished successfully:34
+multi_annealing: AR: 126.604462 +- 3.183016
+multi_loss: returning: ci=6 cj=3 r.loss=126.604462 r.sdev=3.183016
+multi_annealing: AR: 0.5487345599809303 vs 0.02623387861162342
+math/min: result=0.02623387861162342
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: AF: best_opt_some.txt: 58.0,126.604462,0.0,0.0,5.284753614883805,47.456980868107266,0.7184988677898592,3.183016
+multi_annealing: AR: 6,3 6 Did not accept: 47.456980868107266 (3)
+multi_annealing: AR: 0.0 0.0 5.284753614883805 47.456980868107266 0.7184988677898592
+multi_loss appCalls=1
+multi_loss: entered: ci=6 cj=4 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,47.49006278600924,-1.5302000464747076]
+multi_annealing: AR: -1.5302000464747076 4
+multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,47.49006278600924,-1.5302000464747076,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:18:12 -0600  Active:1  Finished successfully:36
+Progress:  time: Mon, 30 Jan 2012 12:18:14 -0600  Checking status:1  Finished successfully:36
+multi_loss: returning: ci=6 cj=4 r.loss=125.062511 r.sdev=3.22833
+multi_annealing: AR: 125.062511 +- 3.22833
+multi_annealing: AF: best_opt_some.txt: 58.0,125.062511,0.0,0.0,5.284753614883805,47.49006278600924,-1.5302000464747076,3.22833
+multi_annealing: AR: 0.4295446121391554 vs 0.16926736066405584
+multi_annealing: AF: max_dist.txt - tbd
+math/min: result=0.16926736066405584
+multi_annealing: AR: 6,4 6 Did not accept: -1.5302000464747076 (4)
+multi_annealing: AR: 0.0 0.0 5.284753614883805 47.49006278600924 -1.5302000464747076
+multi_annealing: AR: i=7 ....T = 0.6931448431551464
+multi_loss appCalls=1
+multi_annealing: AR: 5.698890855198198 2
+multi_loss: entered: ci=7 cj=2 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.698890855198198,47.49006278600924,0.7184988677898592]
+multi_loss: i=1 calling evolve, args=[0,0,5.698890855198198,47.49006278600924,0.7184988677898592,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:18:17 -0600  Checking status:1  Finished successfully:38
+multi_annealing: AR: 124.92233 +- 3.519439
+multi_loss: returning: ci=7 cj=2 r.loss=124.92233 r.sdev=3.519439
+multi_annealing: AF: best_opt_some.txt: 58.0,124.92233,0.0,0.0,5.698890855198198,47.49006278600924,0.7184988677898592,3.519439
+multi_annealing: AR: 0.26061833782383614 vs 0.14702488652213752
+multi_annealing: AF: max_dist.txt - tbd
+math/min: result=0.14702488652213752
+multi_annealing: AR: 7,2 7 Did not accept: 5.698890855198198 (2)
+multi_annealing: AR: 0.0 0.0 5.698890855198198 47.49006278600924 0.7184988677898592
+multi_loss appCalls=1
+multi_annealing: AR: 48.00451051760781 3
+multi_loss: entered: ci=7 cj=3 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,48.00451051760781,0.7184988677898592]
+multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,48.00451051760781,0.7184988677898592,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:18:21 -0600  Checking status:1  Finished successfully:40
+multi_annealing: AR: 123.700997 +- 3.061641
+multi_loss: returning: ci=7 cj=3 r.loss=123.700997 r.sdev=3.061641
+multi_annealing: AF: best_opt_some.txt: 58.0,123.700997,0.0,0.0,5.284753614883805,48.00451051760781,0.7184988677898592,3.061641
+multi_annealing: AR: 0.04383216320955852 vs 0.8562983650405412
+math/min: result=0.8562983650405412
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: Accepting try_x[j], i=7 j=3
+multi_annealing: Accepting try_x[j], i=7 j=3 try_x[j]=48.00451051760781
+multi_annealing: AR: 7 ***** Did accept! 0.0 0.0 5.284753614883805 48.00451051760781 0.7184988677898592
+
+multi_annealing: AR: [7][3] Rejection counts: 0.0 0.0 5.0 4.0 3.0
+
+multi_loss appCalls=1
+multi_annealing: AR: -0.41452399887346414 4
+multi_loss: entered: ci=7 cj=4 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,48.00451051760781,-0.41452399887346414]
+multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,48.00451051760781,-0.41452399887346414,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:18:24 -0600  Checking status:1  Finished successfully:42
+multi_annealing: AR: 122.427501 +- 3.484433
+multi_loss: returning: ci=7 cj=4 r.loss=122.427501 r.sdev=3.484433
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: AR: 0.6794911655574871 vs 1.0
+math/min: result=1.0
+multi_annealing: AF: best_opt_some.txt: 58.0,122.427501,0.0,0.0,5.284753614883805,48.00451051760781,-0.41452399887346414,3.484433
+multi_annealing: Accepting try_x[j], i=7 j=4
+multi_annealing: Accepting try_x[j], i=7 j=4 try_x[j]=-0.41452399887346414
+multi_annealing: AR: [7][4] Rejection counts: 0.0 0.0 5.0 4.0 3.0
+
+multi_annealing: AR: 7 ***** Did accept! 0.0 0.0 5.284753614883805 48.00451051760781 -0.41452399887346414
+
+multi_annealing: AR: i=8 ....T = 0.5809289192863943
+multi_loss appCalls=1
+multi_annealing: AR: 4.228373615739721 2
+multi_loss: entered: ci=8 cj=2 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,4.228373615739721,48.00451051760781,-0.41452399887346414]
+multi_loss: i=1 calling evolve, args=[0,0,4.228373615739721,48.00451051760781,-0.41452399887346414,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:18:27 -0600  Checking status:1  Finished successfully:44
+multi_loss: returning: ci=8 cj=2 r.loss=125.825434 r.sdev=3.06081
+multi_annealing: AR: 125.825434 +- 3.06081
+multi_annealing: AF: best_opt_some.txt: 58.0,125.825434,0.0,0.0,4.228373615739721,48.00451051760781,-0.41452399887346414,3.06081
+multi_annealing: AR: 0.43307949429152304 vs 0.0028823847709179276
+math/min: result=0.0028823847709179276
+multi_annealing: AR: 0.0 0.0 4.228373615739721 48.00451051760781 -0.41452399887346414
+multi_annealing: AR: 8,2 8 Did not accept: 4.228373615739721 (2)
+multi_annealing: AF: max_dist.txt - tbd
+multi_loss appCalls=1
+multi_annealing: AR: 46.894163840589215 3
+multi_loss: entered: ci=8 cj=3 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,46.894163840589215,-0.41452399887346414]
+multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,46.894163840589215,-0.41452399887346414,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:18:30 -0600  Checking status:1  Finished successfully:46
+multi_annealing: AR: 124.871719 +- 3.122439
+multi_loss: returning: ci=8 cj=3 r.loss=124.871719 r.sdev=3.122439
+multi_annealing: AF: best_opt_some.txt: 58.0,124.871719,0.0,0.0,5.284753614883805,46.894163840589215,-0.41452399887346414,3.122439
+multi_annealing: AR: 0.34644774801935974 vs 0.014884566500466245
+math/min: result=0.014884566500466245
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: AR: 0.0 0.0 5.284753614883805 46.894163840589215 -0.41452399887346414
+multi_annealing: AR: 8,3 8 Did not accept: 46.894163840589215 (3)
+multi_loss appCalls=1
+multi_annealing: AR: -2.2286308752011443 4
+multi_loss: entered: ci=8 cj=4 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,48.00451051760781,-2.2286308752011443]
+multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,48.00451051760781,-2.2286308752011443,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:18:33 -0600  Checking status:1  Finished successfully:48
+multi_annealing: AR: 125.200576 +- 3.137099
+multi_loss: returning: ci=8 cj=4 r.loss=125.200576 r.sdev=3.137099
+multi_annealing: AF: best_opt_some.txt: 58.0,125.200576,0.0,0.0,5.284753614883805,48.00451051760781,-2.2286308752011443,3.137099
+multi_annealing: AR: 0.47163119315783886 vs 0.008450593550370632
+math/min: result=0.008450593550370632
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: AR: 8,4 8 Did not accept: -2.2286308752011443 (4)
+multi_annealing: AR: 0.0 0.0 5.284753614883805 48.00451051760781 -2.2286308752011443
+multi_annealing: AR: i=9 ....T = 0.48688006928981853
+multi_loss appCalls=1
+multi_annealing: AR: 5.695013169826046 2
+multi_loss: entered: ci=9 cj=2 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.695013169826046,48.00451051760781,-0.41452399887346414]
+multi_loss: i=1 calling evolve, args=[0,0,5.695013169826046,48.00451051760781,-0.41452399887346414,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:18:37 -0600  Checking status:1  Finished successfully:50
+multi_annealing: AR: 125.017227 +- 3.237815
+multi_loss: returning: ci=9 cj=2 r.loss=125.017227 r.sdev=3.237815
+multi_annealing: AF: best_opt_some.txt: 58.0,125.017227,0.0,0.0,5.695013169826046,48.00451051760781,-0.41452399887346414,3.237815
+multi_annealing: AR: 0.17076495981302242 vs 0.004897539166396153
+math/min: result=0.004897539166396153
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: AR: 9,2 9 Did not accept: 5.695013169826046 (2)
+multi_annealing: AR: 0.0 0.0 5.695013169826046 48.00451051760781 -0.41452399887346414
+multi_loss appCalls=1
+multi_annealing: AR: 48.00679484464189 3
+multi_loss: entered: ci=9 cj=3 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,48.00679484464189,-0.41452399887346414]
+multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,48.00679484464189,-0.41452399887346414,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:18:40 -0600  Checking status:1  Finished successfully:52
+multi_annealing: AR: 125.213924 +- 3.333561
+multi_loss: returning: ci=9 cj=3 r.loss=125.213924 r.sdev=3.333561
+multi_annealing: AF: best_opt_some.txt: 58.0,125.213924,0.0,0.0,5.284753614883805,48.00679484464189,-0.41452399887346414,3.333561
+multi_annealing: AR: 0.15928070563724706 vs 0.003269830345111401
+math/min: result=0.003269830345111401
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: AR: 0.0 0.0 5.284753614883805 48.00679484464189 -0.41452399887346414
+multi_annealing: AR: 9,3 9 Did not accept: 48.00679484464189 (3)
+multi_loss appCalls=1
+multi_annealing: AR: -0.7507963173758503 4
+multi_loss: entered: ci=9 cj=4 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,48.00451051760781,-0.7507963173758503]
+multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,48.00451051760781,-0.7507963173758503,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:18:42 -0600  Active:1  Finished successfully:54
+multi_loss: returning: ci=9 cj=4 r.loss=125.856406 r.sdev=3.764608
+multi_annealing: AR: 125.856406 +- 3.764608
+multi_annealing: AF: best_opt_some.txt: 58.0,125.856406,0.0,0.0,5.284753614883805,48.00451051760781,-0.7507963173758503,3.764608
+math/min: result=8.738454465360378E-4
+multi_annealing: AR: 0.061628429129563234 vs 8.738454465360378E-4
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: AR: 9,4 9 Did not accept: -0.7507963173758503 (4)
+multi_annealing: AR: 0.0 0.0 5.284753614883805 48.00451051760781 -0.7507963173758503
+multi_annealing: AR: i=10 ....T = 0.408057154673674
+multi_loss appCalls=1
+multi_annealing: AR: 3.29267604969456 2
+multi_loss: entered: ci=10 cj=2 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,3.29267604969456,48.00451051760781,-0.41452399887346414]
+multi_loss: i=1 calling evolve, args=[0,0,3.29267604969456,48.00451051760781,-0.41452399887346414,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:18:46 -0600  Checking status:1  Finished successfully:56
+multi_annealing: AR: 125.360364 +- 3.317013
+multi_loss: returning: ci=10 cj=2 r.loss=125.360364 r.sdev=3.317013
+multi_annealing: AF: best_opt_some.txt: 58.0,125.360364,0.0,0.0,3.29267604969456,48.00451051760781,-0.41452399887346414,3.317013
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: AR: 0.27027335203696023 vs 7.56065260977875E-4
+math/min: result=7.56065260977875E-4
+multi_annealing: AR: 0.0 0.0 3.29267604969456 48.00451051760781 -0.41452399887346414
+multi_annealing: AR: 10,2 10 Did not accept: 3.29267604969456 (2)
+multi_loss appCalls=1
+multi_annealing: AR: 46.275556353155956 3
+multi_loss: entered: ci=10 cj=3 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,46.275556353155956,-0.41452399887346414]
+multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,46.275556353155956,-0.41452399887346414,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:18:49 -0600  Checking status:1  Finished successfully:58
+multi_annealing: AR: 126.434478 +- 3.246686
+multi_loss: returning: ci=10 cj=3 r.loss=126.434478 r.sdev=3.246686
+multi_annealing: AF: best_opt_some.txt: 58.0,126.434478,0.0,0.0,5.284753614883805,46.275556353155956,-0.41452399887346414,3.246686
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: AR: 0.14920802306232583 vs 5.4372803009239825E-5
+math/min: result=5.4372803009239825E-5
+multi_annealing: AR: 10,3 10 Did not accept: 46.275556353155956 (3)
+multi_annealing: AR: 0.0 0.0 5.284753614883805 46.275556353155956 -0.41452399887346414
+multi_loss appCalls=1
+multi_annealing: AR: 1.5046793086101826 4
+multi_loss: entered: ci=10 cj=4 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,48.00451051760781,1.5046793086101826]
+multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,48.00451051760781,1.5046793086101826,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:18:53 -0600  Checking status:1  Finished successfully:60
+multi_loss: returning: ci=10 cj=4 r.loss=125.785376 r.sdev=3.346594
+multi_annealing: AR: 125.785376 +- 3.346594
+multi_annealing: AF: best_opt_some.txt: 58.0,125.785376,0.0,0.0,5.284753614883805,48.00451051760781,1.5046793086101826,3.346594
+multi_annealing: AR: 0.6250655950503741 vs 2.668208671281028E-4
+math/min: result=2.668208671281028E-4
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: AR: 0.0 0.0 5.284753614883805 48.00451051760781 1.5046793086101826
+multi_annealing: AR: 10,4 10 Did not accept: 1.5046793086101826 (4)
+multi_annealing: new cycle at i=11
+multi_annealing: AR: New cycle at 11: prev dx[0-4]=[2.3 2.3 2.3 2.3 2.3]
+multi_annealing: AR: i=11 ....T = 0.34199518933533946
+multi_annealing: AR: New cycle at 11: dx[0-4]=[4.6 4.6 0.8624999999999998 0.9857142857142855 1.15]
+multi_loss appCalls=1
+multi_annealing: AR: 3.791705563626845 2
+multi_loss: entered: ci=11 cj=2 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,3.791705563626845,48.00451051760781,-0.41452399887346414]
+multi_loss: i=1 calling evolve, args=[0,0,3.791705563626845,48.00451051760781,-0.41452399887346414,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:18:56 -0600  Checking status:1  Finished successfully:62
+multi_annealing: AF: max_dist.txt - tbd
+multi_loss: returning: ci=11 cj=2 r.loss=124.596526 r.sdev=3.176561
+multi_annealing: AR: 124.596526 +- 3.176561
+multi_annealing: AF: best_opt_some.txt: 58.0,124.596526,0.0,0.0,3.791705563626845,48.00451051760781,-0.41452399887346414,3.176561
+multi_annealing: AR: 0.9715442940965933 vs 0.001760306082349071
+math/min: result=0.001760306082349071
+multi_annealing: AR: 0.0 0.0 3.791705563626845 48.00451051760781 -0.41452399887346414
+multi_annealing: AR: 11,2 11 Did not accept: 3.791705563626845 (2)
+multi_loss appCalls=1
+multi_annealing: AR: 49.92607616034099 3
+multi_loss: entered: ci=11 cj=3 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,49.92607616034099,-0.41452399887346414]
+multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,49.92607616034099,-0.41452399887346414,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:18:59 -0600  Checking status:1  Finished successfully:64
+multi_annealing: AR: 124.00716 +- 3.111738
+multi_loss: returning: ci=11 cj=3 r.loss=124.00716 r.sdev=3.111738
+multi_annealing: AF: best_opt_some.txt: 58.0,124.00716,0.0,0.0,5.284753614883805,49.92607616034099,-0.41452399887346414,3.111738
+multi_annealing: AR: 0.38818246796613753 vs 0.009863137695174688
+math/min: result=0.009863137695174688
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: AR: 11,3 11 Did not accept: 49.92607616034099 (3)
+multi_annealing: AR: 0.0 0.0 5.284753614883805 49.92607616034099 -0.41452399887346414
+multi_loss appCalls=1
+multi_annealing: AR: -0.7771568808805009 4
+multi_loss: entered: ci=11 cj=4 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,48.00451051760781,-0.7771568808805009]
+multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,48.00451051760781,-0.7771568808805009,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
+Progress:  time: Mon, 30 Jan 2012 12:19:02 -0600  Checking status:1  Finished successfully:66
+multi_annealing: AR: 123.567412 +- 3.66859
+multi_loss: returning: ci=11 cj=4 r.loss=123.567412 r.sdev=3.66859
+multi_annealing: AF: best_opt_some.txt: 58.0,123.567412,0.0,0.0,5.284753614883805,48.00451051760781,-0.7771568808805009,3.66859
+multi_annealing: AR: 0.5338319788181374 vs 0.03568160518248377
+math/min: result=0.03568160518248377
+multi_annealing: AF: max_dist.txt - tbd
+multi_annealing: AR: 11,4 11 Did not accept: -0.7771568808805009 (4)
+multi_annealing: AR: 0.0 0.0 5.284753614883805 48.00451051760781 -0.7771568808805009
+Progress:  time: Mon, 30 Jan 2012 12:19:12 -0600  Finished successfully:68
+No events in 10s.
+
+Registered futures:
+string[] args  Closed, 24 elements, no listeners
+string[] args  Closed, 24 elements, no listeners
+string[] args  Closed, 24 elements, no listeners
+string[] args  Closed, 24 elements, no listeners
+----
+
+Waiting threads:
+----
+
+No events in 10s.
+
+Registered futures:
+string[] args  Closed, 24 elements, no listeners
+string[] args  Closed, 24 elements, no listeners
+string[] args  Closed, 24 elements, no listeners
+string[] args  Closed, 24 elements, no listeners
+----
+
+Waiting threads:
+----
+
+No events in 10s.
+
+Registered futures:
+string[] args  Closed, 24 elements, no listeners
+string[] args  Closed, 24 elements, no listeners
+string[] args  Closed, 24 elements, no listeners
+string[] args  Closed, 24 elements, no listeners
+----
+
+Waiting threads:
+----
+
+Progress:  time: Mon, 30 Jan 2012 12:19:42 -0600  Finished successfully:68

Copied: SwiftApps/SciColSim/docs/sample.testopt.py.output (from rev 5634, SwiftApps/SciColSim/sample.testopt.py.output)
===================================================================
--- SwiftApps/SciColSim/docs/sample.testopt.py.output	                        (rev 0)
+++ SwiftApps/SciColSim/docs/sample.testopt.py.output	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,243 @@
+
+**** Calling optimizer: OMP_NUM_THREADS=4 ./openmp-optimizer 0 0 4 50 -1 58 40000 20 100 2 1 2. 0.01 5 0.3 2.3 1 1 0 0 0 n 4 1234567
+
+alpha_i: 0
+alpha_m: 0
+beta: 4
+gamma: 50
+delta: -1
+target: 58
+n_epochs: 40000
+n_steps: 20
+n_reruns: 100
+range: 2
+verbose level: 1
+T_start: 2
+T_end: 0.01
+Annealing_steps: 5
+Target_rejection: 0.3
+Starting_jump: 2.3
+FREEZE_alpha_i: 1
+FREEZE_alpha_m: 1
+FREEZE_beta: 0
+FREEZE_gamma: 0
+FREEZE_delta: 0
+Operation: n
+Nworkers: 4
+initSeed: 1234567
+0 | 1 (fixed) 
+1 | 1 (fixed) 
+2 | 0 (fixed) 
+3 | 0 (fixed) 
+4 | 0 (fixed) 
+0.742788 0.631704 0.118309 0.922271 0.141282 0.80831 0.961468 0.363704 0.665483 0.683465 0.771216 0.267925 0.224677 0.153439 0.23455 0.816502 0.718347 0.371612 0.948727 0.404154 0.600908 0.766305 0.493219 0.82445 0.100233 0.672304 0.157299 0.53804 0.6794 0.57498 0.758357 0.422188 0.206684 0.876666 0.344459 0.347966 0.684976 0.305927 0.71167 0.350459 0.989392 0.482885 0.618384 0.214069 0.636325 0.852934 0.0305713 0.354672 0.224546 0.979299 0.758825 0.825454 0.745604 0.252044 0.649904 0.845837 0.924348 0.807203 0.383877 0.603748 0.382183 0.142234 0.0259353 0.588867 0.0189005 0.370394 0.936833 0.703877 0.676321 0.648502 0.0543356 0.665713 0.131387 0.672719 0.879782 0.767712 0.525653 0.910353 0.122384 0.750199 0.889652 0.881209 0.575653 0.635256 0.133253 0.225557 0.481092 0.0576008 0.0327595 0.864969 0.661348 0.414942 0.00720323 0.687284 0.00380882 0.0261037 0.0576777 0.940641 0.72998 0.733998 
+multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
+multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
+multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
+multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
+multi_loss(N=100, target=58) elapsed time: 2.9134 seconds 0.0485567 minutes
+
+126.43 +- 3.11244
+
+....T = 2
+
+3.77342 2
+
+multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
+multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
+multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
+multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
+multi_loss(N=100, target=58) elapsed time: 2.90873 seconds 0.0484788 minutes
+
+126.523 +- 3.32417
+0.82704 vs 0.954153
+1 Rejection counts: 0 0 0 0 0 
+
+ 1,2 ***** Did accept! 0 0 3.77342 50 -1 
+
+52.2335 3
+
+multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
+multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
+multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
+multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
+multi_loss(N=100, target=58) elapsed time: 2.93802 seconds 0.0489671 minutes
+
+130.173 +- 3.67201
+0.188468 vs 0.161259
+ 1,3 1 Did not accept 52.2335(3)
+0 0 3.77342 52.2335 -1 
+-2.00667 4
+
+multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
+multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
+multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
+multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
+multi_loss(N=100, target=58) elapsed time: 2.95626 seconds 0.049271 minutes
+
+129.554 +- 3.1612
+0.88704 vs 0.219698
+ 1,4 1 Did not accept -2.00667(4)
+0 0 3.77342 50 -2.00667 
+
+....T = 0.693145
+
+4.07035 2
+
+multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
+multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
+multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
+multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
+multi_loss(N=100, target=58) elapsed time: 2.99097 seconds 0.0498495 minutes
+
+130.603 +- 3.25174
+0.0501227 vs 0.00277796
+ 2,2 2 Did not accept 4.07035(2)
+0 0 4.07035 50 -1 
+48.5261 3
+
+multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
+multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
+multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
+multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
+multi_loss(N=100, target=58) elapsed time: 2.94022 seconds 0.0490036 minutes
+
+128.508 +- 3.34913
+0.685387 vs 0.0571204
+ 2,3 2 Did not accept 48.5261(3)
+0 0 3.77342 48.5261 -1 
+-2.2438 4
+
+multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
+multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
+multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
+multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
+multi_loss(N=100, target=58) elapsed time: 2.95557 seconds 0.0492594 minutes
+
+128.768 +- 3.41509
+0.579097 vs 0.0392132
+ 2,4 2 Did not accept -2.2438(4)
+0 0 3.77342 50 -2.2438 
+
+....T = 0.240225
+
+2.54662 2
+
+multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
+multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
+multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
+multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
+multi_loss(N=100, target=58) elapsed time: 3.02841 seconds 0.0504735 minutes
+
+127.262 +- 3.06273
+0.274094 vs 0.0462924
+ 3,2 3 Did not accept 2.54662(2)
+0 0 2.54662 50 -1 
+51.7319 3
+
+multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
+multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
+multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
+multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
+multi_loss(N=100, target=58) elapsed time: 3.00121 seconds 0.0500201 minutes
+
+128.927 +- 3.75415
+0.00641465 vs 4.52253e-05
+ 3,3 3 Did not accept 51.7319(3)
+0 0 3.77342 51.7319 -1 
+0.232896 4
+
+multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
+multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
+multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
+multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
+multi_loss(N=100, target=58) elapsed time: 2.98499 seconds 0.0497499 minutes
+
+128.55 +- 3.56206
+0.941162 vs 0.000216463
+ 3,4 3 Did not accept 0.232896(4)
+0 0 3.77342 50 0.232896 
+
+....T = 0.0832553
+
+4.37082 2
+
+multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
+multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
+multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
+multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
+multi_loss(N=100, target=58) elapsed time: 2.95775 seconds 0.0492958 minutes
+
+128.816 +- 3.45615
+0.803355 vs 1.09972e-12
+ 4,2 4 Did not accept 4.37082(2)
+0 0 4.37082 50 -1 
+49.3468 3
+
+multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
+multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
+multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
+multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
+multi_loss(N=100, target=58) elapsed time: 2.9872 seconds 0.0497866 minutes
+
+128.915 +- 3.17434
+0.126163 vs 3.35758e-13
+ 4,3 4 Did not accept 49.3468(3)
+0 0 3.77342 49.3468 -1 
+-0.0551126 4
+
+multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
+multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
+multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
+multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
+multi_loss(N=100, target=58) elapsed time: 2.93849 seconds 0.0489748 minutes
+
+125.1 +- 3.35383
+0.17234 vs 1
+4 Rejection counts: 0 0 3 4 3 
+
+ 4,4 ***** Did accept! 0 0 3.77342 50 -0.0551126 
+
+
+....T = 0.028854
+
+5.90757 2
+
+multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
+multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
+multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
+multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
+multi_loss(N=100, target=58) elapsed time: 3.04963 seconds 0.0508272 minutes
+
+131.481 +- 3.49648
+0.318332 vs 9.27549e-97
+ 5,2 5 Did not accept 5.90757(2)
+0 0 5.90757 50 -0.0551126 
+49.5818 3
+
+multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
+multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
+multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
+multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
+multi_loss(N=100, target=58) elapsed time: 3.0051 seconds 0.050085 minutes
+
+131.054 +- 3.3899
+0.854774 vs 2.47481e-90
+ 5,3 5 Did not accept 49.5818(3)
+0 0 3.77342 49.5818 -0.0551126 
+-1.93165 4
+
+multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
+multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
+multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
+multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
+multi_loss(N=100, target=58) elapsed time: 2.95204 seconds 0.0492006 minutes
+
+127.676 +- 3.04749
+0.356781 vs 1.74605e-39
+ 5,4 5 Did not accept -1.93165(4)
+0 0 3.77342 50 -1.93165 
+
+*** optimizer completed, elapsed time=47.6158 seconds 0.793596 minutes)
+
+./mw.py Done!

Deleted: SwiftApps/SciColSim/extract4plots
===================================================================
--- SwiftApps/SciColSim/extract4plots	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/extract4plots	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,42 +0,0 @@
-#!/bin/bash
-
-#usage: ./swiftoutput2plot <swift.outfile>
-
-SWIFTOUTFILE=$1
-
-#extract start time
-TMPDATE=`grep -i progress $SWIFTOUTFILE | head -n 1 | cut -f4-9 -d ' '`
-START_TIME=`date +%s -d "$TMPDATE"`
-
-#extract end time
-TMPDATE=`grep -i progress $SWIFTOUTFILE | tail -n 1 | cut -f4-9 -d ' '`
-END_TIME=`date +%s -d "$TMPDATE"`
-
-#duration
-DIFFTIME=$((END_TIME - START_TIME))
-
-#extract active runs in a file
-grep -o -i "Active:[0-9]*" $SWIFTOUTFILE | awk -F: '{print $2}' > active.txt
-
-#extract successful completions in a file
-grep -o -i "Successfully:[0-9]*" $SWIFTOUTFILE | awk -F: '{print $2}' > cumulative.txt
-
-#prepare tics
-activelines=`wc -l active.txt | awk '{print $1}'`
-cumulines=`wc -l cumulative.txt | awk '{print $1}'`
-
-activelinespertic=`echo "scale=5 ; $DIFFTIME / $activelines" | bc`
-seq 0 $activelinespertic $DIFFTIME > activetics.txt
-
-cumulinespertic=`echo "scale=5 ; $DIFFTIME / $cumulines" | bc`
-seq 0 $cumulinespertic $DIFFTIME > cumultics.txt
-
-#final plot data
-paste activetics.txt active.txt > plot_active.txt
-paste cumultics.txt cumulative.txt > plot_cumulative.txt
-
-grep  "T =" $SWIFTOUTFILE | awk '{print $6}' | cut -c8- | sed 's/....$//' > T.data
-
-grep multi_annealing $SWIFTOUTFILE | grep "1;30" | awk '{print $3}' | cut -c11- | sed 's/....$//' > anneal.data
-
-grep returning $SWIFTOUTFILE | awk '{print $3, $4, $5, $6}' | sed -e 's/'ci='//' -e 's/'cj='//' -e 's/'r.loss='//' -e 's/'r.sdev='//' | sort -t' ' -k 1,2n > multiloss.txt

Deleted: SwiftApps/SciColSim/getallparams.sh
===================================================================
--- SwiftApps/SciColSim/getallparams.sh	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/getallparams.sh	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,10 +0,0 @@
-#! /bin/sh
-
-# targetinno=${1:-58}
-
-grep 'calling evolve' swift.out | # grep ,$targetinno, | 
-  sed -e 's/^.*\[//' \
-      -e 's/\]$//' \
-      -e 's/,/ /g' \
-      -e 's/\(\......\)[0-9]* /\1 /g' |
-  awk '{print $3, $4, $5}' | uniq 

Deleted: SwiftApps/SciColSim/getparamtrace.sh
===================================================================
--- SwiftApps/SciColSim/getparamtrace.sh	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/getparamtrace.sh	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,9 +0,0 @@
-#! /bin/sh
-
-targetinno=${1:-58}
-grep 'calling evolve' swift.out | grep ,$targetinno, | 
-  sed -e 's/^.*\[//' \
-      -e 's/\]$//' \
-      -e 's/,/ /g' \
-      -e 's/\(\......\)[0-9]* /\1 /g' |
-  awk '{print $3, $4, $5}' | uniq 

Deleted: SwiftApps/SciColSim/local.xml
===================================================================
--- SwiftApps/SciColSim/local.xml	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/local.xml	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,9 +0,0 @@
-<config>
-  <pool handle="localhost" >
-    <execution provider="local" url="none" />
-    <profile namespace="karajan" key="jobThrottle">0.05</profile>
-    <profile namespace="karajan" key="initialScore">10000</profile>
-    <filesystem provider="local"/>
-    <workdirectory>/gpfs/pads/swift/jonmon/Swift/work/pads</workdirectory>
-  </pool>
-</config>

Deleted: SwiftApps/SciColSim/mathtest.swift
===================================================================
--- SwiftApps/SciColSim/mathtest.swift	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/mathtest.swift	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,18 +0,0 @@
-import "math";
-
-float a = 0.5;
-tracef("random(): %f\n", random());
-tracef("sin(%f): %f\n", a, sin(a));
-tracef("jlog(%f): %f\n", a, jlog(a));
-tracef("log10(%f): %f\n", a, log10(a));
-tracef("exp(%f): %f\n", a, exp(a));
-tracef("ceil(%f): %f\n", 1.23, ceil(1.23));
-tracef("floor(%f): %f\n", 1.23, floor(1.23));
-tracef("pow(%f,%f): %f\n", 2.0, 4.0, pow(2.0,4.0));
-tracef("min(%f,%f): %f\n", 2.0, 4.0, min(2.0,4.0));
-tracef("min(%f,%f): %f\n", 2.0, 1.5, min(2.0,1.5));
-
-
-
-
-

Copied: SwiftApps/SciColSim/old/RunSwift.sh (from rev 5634, SwiftApps/SciColSim/RunSwift.sh)
===================================================================
--- SwiftApps/SciColSim/old/RunSwift.sh	                        (rev 0)
+++ SwiftApps/SciColSim/old/RunSwift.sh	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,49 @@
+#!/bin/bash
+
+# ./Runswift local to run on sandbox
+# ./Runswift clustersmall to run on beagle pbs cluster at small scale
+# ./Runswift clusterquick to run on beagle pbs cluster at large scale
+
+escapecode=$(echo -n -e '\033')
+
+count=$(head -1 counter.txt);
+expr $count + 1 > counter.txt
+mkdir run$count
+cp /home/ketan/SciColSim/*.swift run$count/
+cp /home/ketan/SciColSim/sites.beagle.xml run$count/
+cp /home/ketan/SciColSim/sites.beagle.quick.xml run$count/
+cp local.xml run$count/
+cp /home/ketan/SciColSim/tc run$count/
+cp /home/ketan/SciColSim/movie_graph.txt run$count/
+cp /home/ketan/SciColSim/cf run$count/
+cd run$count
+
+if [ $1 = "local" ]
+then
+ #SWIFT_HEAP_MAX=7000M swift -tc.file tc -sites.file local.xml -config cf annealing.swift -e33="$escapecode" -nworkers=36 >& swift.out
+ #Total jobs = 6 * 1 * 120/20 * 3 * 100 = 10,800
+ SWIFT_HEAP_MAX=7000M swift -tc.file tc -sites.file local.xml -config cf annealing.swift -e33="$escapecode" -nworkers=6 -minrange=58 -maxrange=64 -rangeinc=1 -evoreruns=120 -nreps=1 -alphai=0 -alpham=0 -beta=4.0 -gamma=50.0 -delta=-1 -annealingcycles=100 -rerunsperapp=20  >& swift.out
+
+elif [ $1 = "clusterbig" ]
+then
+ SWIFT_HEAP_MAX=7000M swift -tc.file tc -sites.file sites.beagle.xml -config cf annealing.swift -e33="$escapecode" -nworkers=24 -rangeinc=50 -evoreruns=960 -startingjump=2.3 -alphai=0 -alpham=0 -beta=4.0 -gamma=50.0 -delta=-1 -annealingcycles=100 -rerunsperapp=192  >& swift.out
+
+elif [ $1 = "clustersmall" ]
+then
+ SWIFT_HEAP_MAX=7000M swift -tc.file tc -sites.file sites.beagle.xml -config cf annealing.swift \-e33="$escapecode" \
+      >& swift.out
+
+elif [ $1 = "clusterquick" ]
+then
+#target_innovation=(1009-58)/50=~20
+#repeats=nreps=1
+# 3 repeats constant (serial)
+#annealing_cycles=100 (serial)
+#rerunsperapp=192
+#evoreruns=960
+#J=evoreruns/rerunsperapp=960/192=5
+
+#Total parallel jobs = (maxrange-minrange)/rangeinc * nreps * (evoreruns/rerunsperapp) = (1009-58)/50 * 1 * 960/192 = 20*5 = 100 Jobs = 2400 openmp jobs in parallel
+ SWIFT_HEAP_MAX=7000M swift -tc.file tc -sites.file sites.beagle.quick.xml -config cf annealing.swift -e33="$escapecode" -nworkers=24 -minrange=58 -maxrange=1009 -rangeinc=50 -evoreruns=960 -nreps=1 -alphai=0 -alpham=0 -beta=4.0 -gamma=50.0 -delta=-1 -annealingcycles=100 -rerunsperapp=192  >& swift.out
+fi
+

Copied: SwiftApps/SciColSim/old/annealing.open-issues.swift (from rev 5634, SwiftApps/SciColSim/annealing.open-issues.swift)
===================================================================
--- SwiftApps/SciColSim/old/annealing.open-issues.swift	                        (rev 0)
+++ SwiftApps/SciColSim/old/annealing.open-issues.swift	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,379 @@
+import "math";
+import "colortext";
+
+type file;
+
+type Res
+{
+    float loss;
+    float sdev;
+}
+
+global boolean FIX_VARIABLES = true;
+global int var_fixed[] = [1,1,0,0,0];
+global int Nworkers = @toint(@arg("nworkers","4"));
+global int rerunsPerApp;
+
+(float nx) newx(float x, float dx)
+{
+    float r = (random()) ; // / (pow(2.0,31.0)-1.0);
+    if (r > 0.5)
+    {
+        nx = x + (random())*dx; // /(pow(2.0,31.0)-1.0);
+    }
+    else
+    {
+        nx = x - (random())*dx; // /(pow(2.0,31.0)-1.0);
+    }
+    // tracef("newx(%f,%f)=%f\n",x,dx,nx);
+}
+
+app (file outfile, file loss) evolve (string args[], file graph)
+{
+    evolve @loss args stdout=@outfile ;  // graph is passed implicitly
+}
+
+app (file x) sumloss(file loss[])
+{
+    sumloss @filenames(loss) stdout=@x;
+}
+
+/*
+
+  Program structure:
+
+  main
+  optimizer_sweep() - formerly, python script
+  multi_annealing()
+  multi_loss()
+  evolve()
+  sumloss()
+*/
+
+(file bestfile, file maxfile) multi_annealing (
+    float T_start,
+    float T_end,
+    float Target_rejection,
+    int evolve_reruns,
+    float starting_jump,
+    float params0[],
+    float target_innov,
+    int annealing_cycles)
+{
+    int cycle=10;     // const
+    int NEVOPARAMS=5; // const - 5 params, alpha 1,m through delta, does not include target_innovation
+
+    float rejection[][];  // [i][j] where i is cycle and j is evolve-parameter (alpha_i, alpha_m, beta, gamma, delta)
+
+    float x[][], dx[][], curr_loss[], curr_sdev[];
+
+    Res mlres[][];
+    mlres[0][0] = multi_loss( 0, 0, params0, target_innov, evolve_reruns ); // FIXME: serves for all evolve-params ???
+    tracef("multi_annealing: AR: initial: %f +- %f\n",mlres[0][0].loss,mlres[0][0].sdev);
+
+    foreach j in [0:NEVOPARAMS-1]
+    {
+        x[0][j]=params0[j];
+        dx[0][j] = starting_jump;
+        rejection[0][j] = 0.0;
+        curr_loss[j] = mlres[0][0].loss;
+        curr_sdev[j] = mlres[0][0].sdev;
+    }
+
+    iterate iter_i
+    {    // foreach i in [1:annealing_cycles]
+        int i = iter_i + 1;
+
+        // set new temperature, rejection threshold, and dx values for this cycle
+
+        float temperature = T_start*exp( @tofloat(i-1)*(jlog(T_end)-jlog(T_start))/@tofloat(annealing_cycles));
+
+        tracef(@strcat("multi_annealing: AR: i=%i ....T = ",color(3,"%f"),"\n"), i, temperature);
+
+        // On each new "major" cycle within the annealing_cycles (other than the first) set new rejection and dx values
+
+        if ( i %% cycle == 1 && i > 1 )
+        {
+            tracef("multi_annealing: new cycle at i=%i\n",i);
+            tracef(color(Pink, "multi_annealing: AR: New cycle at %i: prev dx[0-4]=[%f %f %f %f %f]\n"),i,dx[i-1][0],dx[i-1][1],dx[i-1][2],dx[i-1][3],dx[i-1][4]);
+            foreach  k in [0:NEVOPARAMS-1]
+            {
+                float newrejection = rejection[i-1][k] / @tofloat(cycle);
+                if (newrejection > 0.0)
+                {
+                    dx[i][k] = dx[i-1][k] / (newrejection / Target_rejection);
+                    // FIXME: re-enable: rejection[i][k]=0.0;
+                }
+                else
+                {
+                    dx[i][k] = dx[i-1][k] * 2.0;
+                    // FIXME: re-enable: rejection[i][k]=rejection[i-1][k];
+                }
+                // FIXME: HANGS? : tracef(color(Red,"Recomputed rejection: i=%d k=%d dx[i][k]=%f\n"), i, k, dx[i][k]);
+            }
+            tracef(color(Blue, "multi_annealing: AR: New cycle at %i: dx[0-4]=[%f %f %f %f %f]\n"),i,dx[i][0],dx[i][1],dx[i][2],dx[i][3],dx[i][4]);
+        }
+        else
+        { // If not new cycle, set dx[i][*] from previous dx ([i-1]). rejection[i]j] is set later.
+            foreach  k in [0:NEVOPARAMS-1]
+            {
+                dx[i][k] = dx[i-1][k];
+            }
+        }
+        //foreach j in [0:NEVOPARAMS-1] { // Try a new value for each non-fixed param; then write results and accept or reject
+        iterate j
+        {  // Try a new value for each non-fixed param; then write results and accept or reject
+            // float try_x[];
+            int curr = (i * NEVOPARAMS) + j;
+            int prev = curr-1;
+            // tracef("in multi_annealing: i=%i j=%i curr=%i prev=%i\n", i, j, curr, prev);
+            if ( /*(!FIX_VARIABLES) || */ (var_fixed[j]==0) ) {  // Adjustable vars
+                // fixed=1,1,0,0,0: FIXME: FIX_VARIABLES flag has faulty logic but OK when TRUE
+                float try_x[];
+                foreach k in [0:NEVOPARAMS-1]
+                { // Select the evolve params to try
+                    if ( k < j )
+                    {
+                        try_x[k] = x[i][k]; // already set x[i][k]
+                    }
+                    else
+                    {
+                        if ( k == j )
+                        {
+                            try_x[k] = newx(x[i-1][j],dx[i-1][j]); // permute x[i-1][j]
+                        }
+                        else
+                        { // k > j
+                            try_x[k] = x[i-1][k]; // use x[i-1][k] (from prior cycle)
+                        }
+                    }
+                }
+                tracef(@strcat("multi_annealing: AR: ", color(10,"%f"), " ", color(9,"%i"),"\n"), try_x[j],j);
+                // Up to here, x[] and dx[] are only set for previous i
+                mlres[i][j] = multi_loss(i,j,try_x, target_innov, evolve_reruns); // do the N evolve()'s, N=evolve_reruns
+                tracef("multi_annealing: AR: %f +- %f\n", mlres[i][j].loss, mlres[i][j].sdev);
+                // Beyond this point, x[] and dx[] are being set for this i,j
+
+                float ALOT=100000000000.0; // 100,000,000,000. = 10^11
+                if (mlres[i][j].loss < ALOT)
+                {
+                    tracef("multi_annealing: AF: best_opt_some.txt: %f,%f,%f,%f,%f,%f,%f,%f\n",
+                           target_innov,mlres[i][j].loss,try_x[0],try_x[1],try_x[2],try_x[3],try_x[4],mlres[i][j].sdev);
+                    tracef(color(Red,"multi_annealing: AF: max_dist.txt - tbd\n")); // FIXME: max_dist is global set in evolve()
+                }
+                else
+                {  // does this ever occur? if so did we want to still do the ratio computation above???
+                    tracef("multi_annealing: Loss %f > ALOT at [i][j] = [%d][%d]\n", mlres[i][j].loss, i ,j);
+                }
+                float ratio = min(1.0, exp( -(mlres[i][j].loss-curr_loss[prev]) / temperature));
+                float r = (random()) ; // / (pow(2.0,31.0)-1.0);  // FIXME: AR: why all the 2^31's ???
+                tracef("multi_annealing: AR: %f vs %f\n", r, ratio);
+                if (r > ratio)
+                {  // Reject new parameter
+                    x[i][j] = x[i-1][j];
+                    rejection[i][j] = rejection[i-1][j] + 1.0;  // FIXME: AR: Is this correct? incr rejection?
+                    curr_loss[curr] = curr_loss[prev];
+                    curr_sdev[curr] = curr_sdev[prev];
+                    // FIXME: AR: the following prints seem to replicate values in the .cpp version - please clarify.
+                    tracef("multi_annealing: AR: %i,%i %i Did not accept: %f (%i)\n", i, j, i, try_x[j], j);
+                    tracef("multi_annealing: AR: %f %f %f %f %f\n", try_x[0],try_x[1],try_x[2],try_x[3],try_x[4]);
+                }
+                else
+                {           // Accept new parameter
+                    tracef("multi_annealing: Accepting try_x[j], i=%i j=%i\n",i,j);
+                    x[i][j] = try_x[j];
+                    rejection[i][j] = rejection[i-1][j];  // FIXME: AR: Is this correct? no incr of rejection?
+                    tracef("multi_annealing: Accepting try_x[j], i=%i j=%i try_x[j]=%f\n",i,j,try_x[j]);
+                    curr_loss[curr] = mlres[i][j].loss;
+                    curr_sdev[curr] = mlres[i][j].sdev;
+                    float rj[];
+                    foreach k in [0:NEVOPARAMS-1]
+                    {  // FIXME!!!
+                        if (k <= j)
+                        {
+                            rj[k] = rejection[i][k]; // Was either set from previous j or just set for this j
+                        }
+                        else
+                        {
+                            rj[k] = rejection[i-1][k]; // Not yet set, use previous
+                        }
+                    }
+                    tracef(@strcat("multi_annealing: AR: [%i][%i] ", color(8,"Rejection counts: "),
+                                   color(1,"%f"), " ", color(7,"%f"), " ", color(5,"%f"), " ", color(9,"%f"), " ", color(6,"%f"), "\n\n"),
+                           i, j, rj[0], rj[1], rj[2], rj[3], rj[4]);
+                    tracef(@strcat("multi_annealing: AR: %i ", color(8,"***** Did accept! "),
+                                   color(1,"%f"), " ", color(7,"%f"), " ", color(5,"%f"), " ", color(9,"%f"), " ", color(6,"%f"), "\n\n"),
+                           i, try_x[0], try_x[1], try_x[2], try_x[3], try_x[4]);
+                }
+            }
+            else
+            { // Fixed Vars
+                x[i][j] = x[i-1][j];
+                rejection[i][j] = rejection[i-1][j];
+                curr_loss[curr] = curr_loss[prev];
+                curr_sdev[curr] = curr_sdev[prev];
+                // dx[i][j] not set for fixed vars
+            }
+        } until(j == NEVOPARAMS-1);
+    } until(iter_i == (annealing_cycles-1));
+}
+
+(Res r) multi_loss( int ci, int cj, float x[], float target_innov, int evolve_reruns )
+// (Res r, Stats s) multi_loss( int ci, int cj, float x[], float target_innov, int evolve_reruns ) FIXME: To obtain stats
+{
+    file rfile[];
+    file ofile[]; // FIXME: to obtain timings and otehr stats
+    tracef("multi_loss: entered: ci=%i cj=%i target_innov=%f evolve_reruns=%i x=%q\n",ci, cj, target_innov,evolve_reruns,x);
+
+    int appCalls = @toint(@tofloat(evolve_reruns) / @tofloat(rerunsPerApp));  // FIXME: handle fractional issues and rounding etc. For now must divide evenly
+
+    tracef("multi_loss appCalls=%i\n", appCalls);
+    foreach i in [1:appCalls] {  // repeats of the evolove() - same as n_reruns
+        file outfile; // FIXME: map and save in future
+        string args[] = [ // FIXME: move this to a setargs() function
+            //    alpha_i        alpha_m        beta           gamma          delta          target_innov
+            @strcat(x[0]), @strcat(x[1]), @strcat(x[2]), @strcat(x[3]), @strcat(x[4]), @strcat(target_innov),
+
+            //     n_epochs n_steps evolve_reruns           range
+            //    "40000",  "20",   @strcat(evolve_reruns), "2",
+            "40000",  "20",   @strcat(rerunsPerApp),  "2",
+
+            //    verbose_level
+            "1",
+
+            //    T_start T_end   Annealing_steps Target_rejection Starting_jump
+            "2.",   "0.01", "2",            "0.3",           "2.3",
+
+            //    FREEZE: alpha_i alpha_m beta gamma delta
+            "1",    "1",    "0", "0",  "0",
+
+            //   operation-code:(m,a) Nworkers           seed
+            "m",                 @strcat(Nworkers), "1234567" ];
+
+        file graph <"movie_graph.txt">;
+        (outfile, rfile[i]) = evolve(args,graph);
+        // (ofile[i], rfile[i]) = evolve(args,graph);
+        tracef("multi_loss: i=%i calling evolve, args=%q\n", i, args);
+        // tracef("multi_loss: after evolve: i=%i %k %k\n", i, outfile, rfile[i]);
+    }
+    file sumfile = sumloss(rfile);
+    r = readData(sumfile);
+    tracef("multi_loss: returning: ci=%i cj=%i r.loss=%f r.sdev=%f\n",ci,cj,r.loss,r.sdev);
+    // file statfile = sumstats(ofile);  FIXME: to obtain timings and otehr stats
+    // s = readStat(statsfile);          FIXME: to obtain timings and otehr stats
+}
+
+optimizer_sweep() // Implements logic of python driver script
+{
+    int minrange=58;
+    int maxrange=59;
+    int rangeinc=50;
+
+    //int maxrange=1009;
+    //int maxrange=209;
+
+    // FIXME: add provision for random priming and random param values when x[i] == -100 (see optimizer.cpp main())
+
+    int nreps=1; // 15
+
+//    file bestfile <single_file_mapper; file=@strcat("output/T",target,".R",rep,".best_opt_some")>;
+//    file maxfile <single_file_mapper; file=@strcat("output/T",target,".R",rep,".max_dist")>;
+
+    foreach target_innov in [minrange:maxrange:rangeinc]
+    {
+        foreach rep in [1:nreps]
+        {
+            file outfile;  // <single_file_mapper; file=@strcat("output/T",target_innov,".R",rep,".out")>;
+            file lossfile; // <single_file_mapper; file=@strcat("output/T",target_innov,".R",rep,".loss_data")>;
+/*
+  (outfile,lossfile) = multi_annealing(
+  T_start          = 2.0,
+  T_end            = 0.01,
+  Target_rejection = 0.3,
+  evolve_reruns    = 10,
+  starting_jump    = 2.3,
+  params0[]        = [0.0, 0.0, 4.0, 50.0, -1.0],
+  @tofloat(target_innov),
+  annealing_cycles = 2);
+*/
+            (outfile,lossfile) = multi_annealing(
+                2.0,
+                0.01,
+                0.3,
+                100,
+                2.3,
+                [0.0, 0.0, 4.0, 50.0, -1.0],
+                @tofloat(target_innov),
+                30);
+        }
+    }
+}
+
+rerunsPerApp = 100;
+
+main()
+{
+    optimizer_sweep();
+}
+
+main();
+
+/*
+
+  Program structure:
+
+  main
+  optimizer_sweep()
+  multi_annealing()
+  multi_loss()
+  evolve()
+  sumloss()
+
+  Example parameter sets:
+
+  for target in range(58,59,50):
+  for i in range(1):
+  args="./toptimizer 0 0 4 50 -1 "+target+" 40000 20 75    2 1 2. 0.01 2 0.3 2.3 1 1 1 0 0 m // > out.T"+str(target)+".i"+str(i)
+  os.system(args);
+
+  string fastargs1[] = [
+  "0", "0", "4", "50", "-1", @strcat(target),
+  "40000", "20", "1000", "2",
+  "1",
+  "2.", "0.01", "100", "0.3", "2.3",
+  "1", "1", "0", "0", "0"];
+  string fastargs2[] = [
+  "0", "0", "4", "50", "-1", @strcat(target),
+  "40000", "20", "1000", "2",
+  "1",
+  "2.", "0.01",  "5", "0.3", "2.3",
+  "1", "1", "0", "0", "0", "m"];
+  string fastargs3[] = [
+  "0", "0", "4", "50", "-1", @strcat(target),
+  "40000", "20", @strcat(repeats), "2",
+  "1",
+  "2.", "0.01",  "2", "0.3", "2.3",
+  "1", "1", "0", "0", "0", "m"];
+*/
+
+(string args[]) setargs()
+{
+    // string longargs[] = @strcat("0 0 4 50 -1 ",target," 40000 20 1000 2 1 2. 0.01 100 0.3 2.3 1 1 0 0 0 m");
+
+    //  [alpha_i alpha_m beta gamma delta target_innov
+    //  [n_epochs n_steps n_reruns] [range]
+    //  [verbose_level]
+    //  [T_start T_end Annealing_steps Target_rejection Starting_jump]
+    //  [FREEZE_alpha_i FREEZE_alpha_m FREEZE_beta FREEZE_gamma FREEZE_delta] [operation-code:(m,a) Nworkers]
+}
+
+////////////////// HOLD JUNK
+
+//                    tracef(@strcat("multi_annealing: AR: %i ", color(8,"Rejection counts: "),
+//                                   color( /* 2 */ 1," %f"), "\n\n"),
+//                                   i, rejection[i][j] ); // , rejection[i][1], rejection[i][2], rejection[i][3], rejection[i][4]);
+// FIXME: determine correct rejection[] values to avoid hanging:
+//                    tracef(@strcat("multi_annealing: AR: %i ", color(8,"Rejection counts: "),
+//                                   color( /* 2 */ 1," %f"), color(7," %f"), color(5," %f"), color(9," %f"), color(6," %f"), "\n\n"),
+//                                   rejection[i][0], rejection[i][1], rejection[i][2], rejection[i][3], rejection[i][4]);
+// END FIXME

Copied: SwiftApps/SciColSim/old/beagle.xml (from rev 5634, SwiftApps/SciColSim/beagle.xml)
===================================================================
--- SwiftApps/SciColSim/old/beagle.xml	                        (rev 0)
+++ SwiftApps/SciColSim/old/beagle.xml	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,60 @@
+<config>
+
+<!-- From Glen: used w/ "Swift svn swift-r4813 (swift modified locally) cog-r3175" -->
+
+  <pool handle="beagle">
+
+    <execution provider="coaster" jobmanager="local:pbs" url="none"/>
+    <profile namespace="globus" key="providerAttributes">pbs.aprun;pbs.mpp;depth=24</profile>
+    <profile key="jobsPerNode" namespace="globus">1</profile>
+
+    <profile namespace="env" key="OMP_NUM_THREADS">24</profile>
+    <profile namespace="globus" key="maxwalltime">02:00:00</profile>
+    <profile namespace="globus" key="maxTime">14400</profile>
+    <profile namespace="globus" key="slots">20</profile>
+    <profile namespace="globus" key="nodeGranularity">1</profile>
+    <profile namespace="globus" key="maxNodes">1</profile>
+    <profile namespace="globus" key="lowOverAllocation">100</profile>
+    <profile namespace="globus" key="highOverAllocation">100</profile>
+    <profile namespace="karajan" key="jobThrottle">.15</profile>
+    <profile namespace="karajan" key="initialScore">10000</profile>
+
+    <profile namespace="globus" key="project">CI-MCB000119</profile>
+    <profile namespace="globus" key="queue">route</profile>
+
+    <filesystem provider="local"/>
+    <workdirectory >/lustre/beagle/wilde/swiftwork</workdirectory>
+  </pool>
+
+<!-- From Justin's swift-devel page: 
+
+<import file="sys.xml"/>
+<set name="wdir" value="/lustre/beagle/{user.name}/work"/>
+<echo message="setting workDirectory to: {wdir}"/>
+
+
+<pool handle="beagle-pbs">
+  <execution jobmanager="local:pbs" provider="coaster" url="none"/>
+  <profile namespace="globus" key="maxWallTime">1</profile>
+  <profile namespace="globus" key="maxTime">7200</profile>
+
+  <profile namespace="globus" key="providerAttributes">
+    pbs.aprun;pbs.mpp;depth=24
+  </profile>
+  <profile key="jobsPerNode" namespace="globus">24</profile>
+  <profile key="slots" namespace="globus">1</profile>
+  <profile key="nodeGranularity" namespace="globus">1</profile>
+  <profile key="maxNodes" namespace="globus">1</profile>
+  <profile key="queue" namespace="globus">batch</profile>
+  <profile key="jobThrottle" namespace="karajan">5.99</profile>
+  <profile key="initialScore" namespace="karajan">10000</profile>
+  <profile namespace="globus" key="project">_PROJECT_</profile>
+  <profile namespace="globus" key="project">_QUEUE_</profile>
+
+  <filesystem provider="local" url="none" />
+  <workdirectory>{wdir}</workdirectory>
+</pool>
+
+-->
+
+</config>

Copied: SwiftApps/SciColSim/old/mathtest.swift (from rev 5634, SwiftApps/SciColSim/mathtest.swift)
===================================================================
--- SwiftApps/SciColSim/old/mathtest.swift	                        (rev 0)
+++ SwiftApps/SciColSim/old/mathtest.swift	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,18 @@
+import "math";
+
+float a = 0.5;
+tracef("random(): %f\n", random());
+tracef("sin(%f): %f\n", a, sin(a));
+tracef("jlog(%f): %f\n", a, jlog(a));
+tracef("log10(%f): %f\n", a, log10(a));
+tracef("exp(%f): %f\n", a, exp(a));
+tracef("ceil(%f): %f\n", 1.23, ceil(1.23));
+tracef("floor(%f): %f\n", 1.23, floor(1.23));
+tracef("pow(%f,%f): %f\n", 2.0, 4.0, pow(2.0,4.0));
+tracef("min(%f,%f): %f\n", 2.0, 4.0, min(2.0,4.0));
+tracef("min(%f,%f): %f\n", 2.0, 1.5, min(2.0,1.5));
+
+
+
+
+

Copied: SwiftApps/SciColSim/old/optimizer.protomods.cpp (from rev 5634, SwiftApps/SciColSim/optimizer.protomods.cpp)
===================================================================
--- SwiftApps/SciColSim/old/optimizer.protomods.cpp	                        (rev 0)
+++ SwiftApps/SciColSim/old/optimizer.protomods.cpp	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,1818 @@
+//
+//  main.cpp
+//  optimizer
+//
+//  Created by Andrey Rzhetsky on 4/11/11.
+//  Copyright 2011 University of Chicago. All rights reserved.
+//
+
+#define MAXNworkers 24
+int Nworkers=MAXNworkers;
+
+// Add operation code to enable existing code to be used at lower level from Swift scripts:
+
+char operation = 'n'; // n: normal; m: do one multi_loss (with n_reruns).
+                      // Not used: a: analyze and generate next annealing parameter set. g: tbd
+
+#include <fstream>
+#include <sstream>
+#include <iostream>
+#include <stdio.h>
+#include <time.h>
+#include <ctime>    
+#include <algorithm>
+#include <string>
+
+#include <stdio.h>
+#include <sys/param.h>
+#include <sys/time.h>
+#include <sys/types.h>
+
+// #include <dispatch/dispatch.h>
+#include <fstream>
+
+
+#include <stdlib.h>
+#include <boost/numeric/ublas/io.hpp>
+#include <boost/graph/graph_traits.hpp>
+#include <boost/graph/dijkstra_shortest_paths.hpp>
+#include <boost/graph/loop_erased_random_walk.hpp>
+#include <boost/graph/random.hpp>
+#include <boost/property_map/property_map.hpp>
+#include <boost/graph/graph_concepts.hpp>
+#include <boost/graph/properties.hpp>
+
+#include <boost/graph/graph_traits.hpp>
+#include <boost/graph/adjacency_list.hpp>
+#include <boost/graph/adjacency_matrix.hpp>
+
+#define BOOST_MATH_OVERFLOW_ERROR_POLICY ignore_error
+#define BOOST_MATH_DISCRETE_QUANTILE_POLICY real
+#include <boost/graph/random.hpp>
+#include <boost/random/geometric_distribution.hpp>
+#include <boost/random/uniform_01.hpp>
+#include <boost/random.hpp>
+#include <boost/random/linear_congruential.hpp>
+#include <boost/random/uniform_int.hpp>
+#include <boost/random/uniform_real.hpp>
+#include <boost/random/variate_generator.hpp>
+#include <boost/generator_iterator.hpp>
+#include <boost/lexical_cast.hpp>
+
+#define INT_INFINITY 2147483647
+#define NEVOPARAMS 5
+
+#define FIX_VARIABLES 1
+
+using namespace boost;
+using namespace std;
+using namespace boost::numeric::ublas;
+
+static int max_dist=0;
+
+typedef boost::adjacency_matrix<boost::directedS> Graph;
+typedef std::pair<int,int> Edge;
+typedef boost::graph_traits<Graph> GraphTraits;
+typedef boost::numeric::ublas::triangular_matrix<double, boost::numeric::ublas::strict_upper> prob;
+typedef boost::numeric::ublas::triangular_matrix<double, boost::numeric::ublas::strict_upper> pathlength;
+typedef boost::graph_traits<Graph>::vertex_descriptor vertex_descriptor;
+
+namespace std {
+	using ::time;
+}
+
+static int var_fixed[NEVOPARAMS] = {1, 0, 1, 1, 0};
+
+typedef boost::minstd_rand base_generator_type;
+typedef adjacency_list < listS, vecS, directedS,
+no_property, property < edge_weight_t, int > > graph_t;
+typedef graph_traits < graph_t >::vertex_descriptor vertex_descriptor;
+typedef graph_traits < graph_t >::edge_descriptor edge_descriptor;
+
+
+//================================================
+string strDouble(double number)
+{
+    stringstream ss;//create a stringstream
+    ss << number;//add number to the stream
+    return ss.str();//return a string with the contents of the stream
+}
+
+//================================================
+
+double gaussian(double sigma)
+{
+    double GaussNum = 0.0;
+    int NumInSum = 10;
+    for(int i = 0; i < NumInSum; i++)
+    {
+        GaussNum += ((double)rand()/(double)RAND_MAX - 0.5);
+    }
+    GaussNum = GaussNum*sqrt((double)12/(double)NumInSum);
+    
+    
+    return GaussNum;
+    
+}
+
+
+
+//=================================================
+double diffclock(clock_t clock1,clock_t clock2)
+{
+	double diffticks=clock1-clock2;
+	double diffms=(diffticks)/CLOCKS_PER_SEC;
+	return diffms;
+}
+
+//================================================
+//================================================================
+double get_new_x(double x, double dx){
+    
+    double new_x;
+    // boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
+    double r = rand()/(double)(pow(2.,31)-1.);
+    
+    if (r > 0.5){            
+        new_x = x + rand()*dx/(double)(pow(2.,31)-1.);
+    } else {            
+        new_x = x - rand()*dx/(double)(pow(2.,31)-1.);
+    }
+    
+    return new_x;
+    
+}
+
+
+//===============================================   
+string string_wrap(string ins, int mode){
+    
+    std::ostringstream s;
+    
+    switch(mode){
+        case 0:
+            s << "\033[1;29m" << ins << "\033[0m";
+            break;
+        case 1:
+            s << "\033[1;34m" << ins << "\033[0m";
+            break;
+        case 2:
+            s << "\033[1;44m" << ins << "\033[0m";
+            break;
+        case 3:
+            s << "\033[1;35m" << ins << "\033[0m";
+            break;
+        case 4:
+            s << "\033[1;33;44m" << ins << "\033[0m";
+            break;
+        case 5:
+            s << "\033[1;47;34m" << ins << "\033[0m";
+            break;
+        case 6:
+            s << "\033[1;1;31m" << ins << "\033[0m";
+            break;
+        case 7:
+            s << "\033[1;1;33m" << ins << "\033[0m";
+            break;
+        case 8:
+            s << "\033[1;1;43;34m" << ins << "\033[0m";
+            break;
+        case 9:
+            s << "\033[1;1;37m" << ins << "\033[0m";
+            break;
+        case 10:
+            s << "\033[1;30;47m" << ins << "\033[0m";
+            break;
+        default:
+            s << ins;
+    }
+    
+    return s.str();
+}
+
+
+//===============================================
+string wrap_double(double val, int mode){
+    
+    std::ostringstream s;
+    s << string_wrap(strDouble(val),mode);
+    
+    return s.str();
+}
+
+
+
+//===============================================
+const     
+string i2string(int i){
+    
+    std::ostringstream s;
+    s << "worker" 
+    << lexical_cast<std::string>(i);
+    
+    return s.str();
+    
+}
+
+//===============================================
+char* i2char(int i){
+    
+    std::ostringstream s;
+    s << "worker" 
+    << lexical_cast<std::string>(i);
+    
+    char* a=new char[s.str().size()+1];
+    memcpy(a,s.str().c_str(), s.str().size());
+    
+    return a;
+}
+
+
+template <class T>
+bool from_string(T& t, 
+                 const std::string& s, 
+                 std::ios_base& (*f)(std::ios_base&))
+{
+  std::istringstream iss(s);
+  return !(iss >> f >> t).fail();
+}
+
+//================================================
+class Universe {
+	
+private:
+	
+	double alpha_i;
+	double alpha_m;
+	double beta;
+	double gamma;
+	double delta;
+	
+    double TargetNovelty;
+    double CumulativeRelativeLoss;
+    double CRLsquare;
+    string id;
+    
+    
+	int N_nodes;
+	int M_edges;
+	
+	int N_epochs;
+	int N_steps;
+	int N_repeats;
+	
+	int current_epoch;
+	double current_loss;
+	int current_repeat;
+    double current_novelty;
+	
+	int mode_identify_failed;
+    int verbose_level; // 0 is silent, higher is more
+	
+	double k_max;
+	
+	graph_t Full_g;
+	
+	double **Prob;
+	double **Tried;
+	double **Dist;
+	double **Final;
+    double **EdgeIndex;
+	double *Rank;
+	
+    base_generator_type generator;	
+    boost::uniform_real<> uni_dist;
+    boost::geometric_distribution<double> geo;
+    
+public:
+	
+    
+    
+	//======  Constructor ======
+	Universe(const std::string FileToOpen, int Epochs, int Steps, int Repeats, int identify_failed, double target, const std::string idd)
+	{
+		//typedef array_type2::index index2;
+		
+		
+		std::ifstream inFile;
+        //string line;
+        
+        //-------------------------------
+        
+        base_generator_type gene(42u);
+        generator = gene;
+        generator.seed(static_cast<unsigned int>(std::time(0)));
+        boost::uniform_real<> uni_d(0,1);
+        uni_dist = uni_d;
+        
+        //--------------------------------
+        
+		int i, k;
+        int x, y;
+		Edge* edge_array_mine;
+		int num_arcs_mine, num_nodes_mine;
+		int* weights_mine;
+		
+        TargetNovelty = target;
+        CumulativeRelativeLoss = 0.;
+        CRLsquare = 0.;
+        
+		
+		N_epochs  = Epochs;
+		N_steps   = Steps;
+		N_repeats = Repeats;
+		
+		current_epoch = 0;
+		current_loss = 0.;
+		current_repeat = 0;
+        
+        id = idd;
+        
+        verbose_level = 1;
+		
+		mode_identify_failed = identify_failed;
+        
+		
+		//-------------------------------
+		// The first pass though file with the graph
+		inFile.open(FileToOpen.c_str());
+		if (inFile.fail()) {
+			cout << "Unable to open file";
+			exit(1); // terminate with error
+		}else {
+            
+            if (verbose_level > 2){
+                std::cout <<  " Opened <" << FileToOpen << ">"<<std::endl;
+            }
+        }
+		
+		i=0;
+        std::string line;
+		//while (! inFile.eof() && ! inFile.fail()) {
+        while (1==1) {
+            
+            inFile >> x;
+            inFile >> y;
+            
+            if (verbose_level > 2){
+                std::cout << " x: " << x;
+                std::cout << " y: " << y << std::endl;
+            }
+            
+			if (i==0){
+				N_nodes=x;
+				M_edges=y;	
+                break;
+			}
+			i++;
+            
+			
+		}
+		inFile.close();
+        
+        if (verbose_level == 2){
+            std::cout << N_nodes <<  " nodes, " << M_edges << " edges"<<std::endl;
+        }
+		
+		// k_max is the longest distance possible
+		
+        //k_max = M_edges;
+		k_max = 70;
+        
+		//------------------------------------
+		// Get memory allocated for all class members
+		
+		Prob = allocate_2Dmatrix(N_nodes, N_nodes);
+		Tried = allocate_2Dmatrix(N_nodes, N_nodes);
+		Dist = allocate_2Dmatrix(N_nodes, N_nodes);
+		Final = allocate_2Dmatrix(N_nodes, N_nodes);
+        EdgeIndex = allocate_2Dmatrix(N_nodes, N_nodes);
+		Rank = allocate_1Dmatrix(N_nodes);
+		
+        //The second pass through file with the graph
+        
+		for(int i = 0; i < N_nodes; ++i) {
+			Rank[i]=0.;
+			for(int j = 0; j < N_nodes; ++j) {
+				Final[i][j] = 0.;
+				Prob[i][j]=0.;
+				Dist[i][j]=-1.;
+				Tried[i][j]=0.;
+                EdgeIndex[i][j]=-1;
+			}
+		}
+        
+		
+		// Fill in the final graph -- and we are ready to go!
+        
+	    inFile.open(FileToOpen.c_str());
+		if (!inFile) {
+            std::cout << "Unable to open file";
+			exit(1); // terminate with error
+		}
+		else {
+            
+            if (verbose_level > 2){
+                std::cout <<  " Opened <" << FileToOpen << ">"<<std::endl;
+            }
+        }
+        
+		i=0;  
+		while (inFile >> x && inFile >>y) {
+			if (i > 0) {
+				Final[x][y]=1.;
+				Final[y][x]=1.;
+                
+                
+                if (verbose_level == 2){
+                    std::cout << ".";
+                }
+			}
+			i++;
+			
+		}
+        if (verbose_level == 2){
+            std::cout << std::endl;
+        }
+		inFile.close(); 
+		
+        k=0;
+        for (int i=0; i<N_nodes-1; i++){
+            for (int j=i+1;j<N_nodes; j++){
+                if(Final[i][j] > 0.){
+                    EdgeIndex[i][j]=k;
+                    k++;
+                }
+            }
+        }
+        
+        
+		
+		//+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+		// create graph -- hopefully, we can keep it, just modifying edge weights
+		
+		
+		edge_array_mine = new Edge[2*M_edges];
+		num_arcs_mine = 2*M_edges;
+		num_nodes_mine = N_nodes;
+		weights_mine = new int[2*M_edges];
+		for (int i=0; i<2*M_edges; i++){ weights_mine[i]=1;}
+		
+		k=0;
+		for(int i=0; i<N_nodes-1; i++){
+			for( int j=i+1; j<N_nodes; j++){
+				if (Final[i][j]>0.){
+					edge_array_mine[2*k]  =Edge(i,j);
+					edge_array_mine[2*k+1]=Edge(j,i);
+					k++;
+				}
+			}
+		}
+		graph_t g(edge_array_mine, edge_array_mine + num_arcs_mine, weights_mine, num_nodes_mine);
+		
+		Full_g = g;
+		delete edge_array_mine;
+		delete weights_mine;
+		
+		//===========================================================================
+		std::vector<edge_descriptor> p(num_edges(Full_g));
+		std::vector<int> d(num_edges(Full_g));
+		edge_descriptor s;
+		boost::graph_traits<graph_t>::vertex_descriptor u, v;
+		
+		for (int i=0; i<N_nodes-1; i++){
+			for (int j=i+1; j<N_nodes; j++){
+				if (Final[i][j] > 0.){
+					u = vertex(i, Full_g);
+					v = vertex(j, Full_g);
+					remove_edge(u,v,Full_g);
+					remove_edge(v,u,Full_g);
+					
+				}
+			}
+		}
+		
+        
+    }
+	
+	
+	//=====================================================================
+	int sample_failed_number(double pfail){
+		
+		//boost::geometric_distribution<double> geo(pfail);
+		//boost::variate_generator<base_generator_type&, geometric_distribution<double> > geom(generator, geo);
+		
+		double r, u, g;
+        
+        r=0.;
+		for(int i=0; i<N_steps; i++){
+            
+            u=(double)rand();
+            u = 1.-u /(double)(pow(2.,31)-1.);
+            g=(int)(ceil(log(u) / log(pfail)));
+            
+			//r += geom();
+            
+            r+=g;
+		}
+        
+        if (verbose_level>=3){
+            std::cout << id << " failed " << r << std::endl;
+		}
+		return r;
+		
+	}
+    
+    //=============================================
+    double get_target(void){
+        return TargetNovelty;
+    }
+	
+    //=============================================
+    void set_target(double target){
+        TargetNovelty=target;
+    }
+	
+	//=============================================
+	int sample(){
+		
+        //boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
+        // double r = uni(), Summa = 0.;
+        
+        
+        
+        double r = rand(), Summa = 0.;
+        r /= (double)(pow(2.,31)-1.);
+		int result = 0;
+		int finished = 0;
+        
+        if (verbose_level==4){
+            std::cout << id << " sampled " << r << std::endl;
+        }
+		
+		for(int i=0; i<N_nodes-1 && finished==0; i++){			
+			for( int j=i+1; j<N_nodes && finished==0; j++){
+				
+				Summa += Prob[i][j];
+				
+				if (Summa > r){
+					
+					Tried[i][j]+=1.;
+					
+					if (Final[i][j] > 0.){
+						result = 1;
+					}
+					finished = 1;										
+				}
+			}
+		}
+		
+		return result;
+		
+	}
+	
+	//===============================
+	void update_current_graph(void){
+		
+		std::vector<edge_descriptor> p(num_edges(Full_g));
+		std::vector<int> d(num_edges(Full_g));
+		edge_descriptor s;
+		boost::graph_traits<graph_t>::vertex_descriptor u, v;
+		
+		//property_map<graph_t, edge_weight_t>::type weightmap = get(edge_weight, Full_g);
+		for (int i=0; i<N_nodes-1; i++){
+			for (int j=i+1; j<N_nodes; j++){
+				if (Final[i][j] > 0. && Tried[i][j]>0){
+					//s = edge(i, j, Full_g);	
+					boost::graph_traits<graph_t>::edge_descriptor e1,e2;
+					bool found1, found2;
+					u = vertex(i, Full_g);
+					v = vertex(j, Full_g);
+					tie(e1, found1) = edge(u, v, Full_g);
+					tie(e2, found2) = edge(v, u, Full_g);
+					if (!found1 && !found2){
+						add_edge(u,v,1,Full_g);
+					    add_edge(v,u,1,Full_g);
+					}
+					
+				}
+			}
+			
+		}
+	}
+	
+	//===============================
+	void update_distances(void){
+		// put shortest paths to the *Dist[][]
+		std::vector<vertex_descriptor> p(num_vertices(Full_g));
+		std::vector<int> d(num_vertices(Full_g));
+		vertex_descriptor s;
+		
+		
+		// put shortest paths to the *Dist[][]
+		for (int j=0; j<num_vertices(Full_g); j++){
+			
+			if(Rank[j] > 0.){
+				s = vertex(j, Full_g);	 
+				dijkstra_shortest_paths(Full_g, s, predecessor_map(&p[0]).distance_map(&d[0]));
+				
+				//std::cout <<" Vertex "<< j << std::endl;
+				graph_traits < graph_t >::vertex_iterator vi, vend;
+				
+				for (boost::tie(vi, vend) = vertices(Full_g); vi != vend; ++vi) {
+					
+					if (p[*vi]!=*vi){
+						Dist[*vi][j]=d[*vi];
+						Dist[j][*vi]=d[*vi];
+                        
+                        if (Dist[*vi][j]>max_dist){
+                            max_dist=Dist[*vi][j];
+                        }
+                        
+                        
+					} else {
+						Dist[*vi][j]=-1.;
+						Dist[j][*vi]=-1.;
+					}
+				}
+			}
+			
+		}
+		
+		
+	}
+	
+	//======================================================
+	void update_ranks(void){
+		
+		for(int i=0; i<N_nodes; i++){
+			Rank[i]=0.;
+		}
+		
+		for(int i=0; i<N_nodes-1; i++){
+			for( int j=i+1; j<N_nodes; j++){
+				if (Tried[i][j]>0. && Final[i][j] >0.){
+					Rank[i]++;
+					Rank[j]++;
+				}
+			}
+		}
+		
+	}
+	
+	//====================================================================
+	void set_world(double a_i, double a_m, double b, double g, double d){
+		
+		alpha_i=a_i;
+		alpha_m=a_m;
+		gamma=g;
+		beta=b;
+		delta=d;
+		
+	}
+	
+	//====================================================================
+	void reset_world(){
+		
+        //====================================================
+		std::vector<edge_descriptor> p(num_edges(Full_g));
+		std::vector<int> d(num_edges(Full_g));
+		edge_descriptor s;
+		boost::graph_traits<graph_t>::vertex_descriptor u, v;
+        
+		
+		for (int i=0; i<N_nodes-1; i++){
+			for (int j=i+1; j<N_nodes; j++){
+				if (Final[i][j] > 0. && Tried[i][j] > 0){
+					u = vertex(i, Full_g);
+					v = vertex(j, Full_g);
+					remove_edge(u,v,Full_g);
+					remove_edge(v,u,Full_g);
+					
+				}
+			}
+		}
+        
+        //==================================================
+        
+		current_loss=0;
+		current_epoch=0;
+		current_repeat++;
+        current_novelty=0;
+		
+		for(int i = 0; i < N_nodes; ++i) {
+			Rank[i]=0.;
+			for(int j = 0; j < N_nodes; ++j) {
+				Prob[i][j]=0.;
+				Dist[i][j]=-1.;
+				Tried[i][j]=0.;
+			}
+		}
+	}
+	
+	
+    //==============================================
+    void show_parameters(void){
+        
+        std::cout << "Parameters: " 
+        << alpha_i << " "
+        << alpha_m << " | "
+        << beta << " "
+        << gamma << " | "
+        << delta << std::endl;
+        
+    }
+    
+    
+    
+    //===============================================
+    string file_name(){
+        
+        std::ostringstream s;
+        s << "world_" 
+        << lexical_cast<std::string>(alpha_i) << "_" 
+        << lexical_cast<std::string>(alpha_m) << "_"
+        << lexical_cast<std::string>(beta) << "_"
+        << lexical_cast<std::string>(gamma) << "_"
+        << lexical_cast<std::string>(delta) << "_"
+        << lexical_cast<std::string>(N_epochs) << "_"
+        << lexical_cast<std::string>(N_steps) << "_"
+        << lexical_cast<std::string>(N_repeats) << ".txt";
+        
+        return s.str();
+        
+    }
+    
+    
+    
+    
+    //=================================================
+    void set_verbose(int verbose){
+        
+        verbose_level = verbose;
+    }
+    
+    
+    //=============================================================
+    void update_probabilities(void){
+        
+        
+        //=========================
+		// Compute sampling probabilities
+		// first pass: \xi_i,j
+		for(int i=0; i<N_nodes-1; i++){
+			for( int j=i+1; j<N_nodes; j++){
+				
+				double bg = 0.;
+				
+				Prob[i][j] = alpha_i*log(min(Rank[i]+1.,Rank[j]+1.)) + 
+                alpha_m*log(max(Rank[i]+1.,Rank[j]+1.));
+				
+                if (Dist[i][j] > 0.){
+                    
+                    double k = Dist[i][j];
+                    if (k >= k_max){
+                        k = k_max-1;
+                    }
+					
+                    bg = beta * log(k/k_max) + gamma * log(1. - k/k_max);
+					
+                } else {
+                    bg = delta;
+                }
+				
+				Prob[i][j] = exp(Prob[i][j] + bg);
+			}
+		}
+        
+		
+		// second pass: sum
+		double Summa = 0.;
+		
+		for(int i=0; i<N_nodes-1; i++){
+			for( int j=i+1; j<N_nodes; j++){
+				Summa += Prob[i][j];
+			}
+		}
+		
+		// third pass: normalize
+		for(int i=0; i<N_nodes-1; i++){
+			for( int j=i+1; j<N_nodes; j++){
+				Prob[i][j] /= Summa;
+			}
+		}
+        
+    }
+    
+	// Now we are ready for simulations
+	//==============================================
+	void update_world(){
+		
+		int failed = 0;
+        
+		// Given current universe compute shortest paths
+		//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+		
+		update_current_graph();
+		update_ranks();				
+		update_distances();
+		update_probabilities();
+		
+		//===============================
+		// sampling
+		int result;
+		double cost=0., novel=0.;
+		int publishable = 0;
+		
+		
+		//^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+		if (mode_identify_failed == 1){
+			
+			while(publishable < N_steps){
+				
+		    	result = sample();
+			    publishable += result;
+			    failed += (1-result);
+				
+			}
+			
+			for(int i=0; i<N_nodes-1; i++){
+				for( int j=i+1; j<N_nodes; j++){
+					
+					cost+=Tried[i][j];
+					
+					if (Tried[i][j]>0. && Final[i][j]>0.){
+						novel+=1.;
+					}
+				}
+			}
+			
+		}
+		//^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+		else {
+			
+			double pfail=0.;
+			int n_failed;
+			//, n_check = 0;
+			
+			for(int i=0; i<N_nodes-1; i++){
+				for( int j=i+1; j<N_nodes; j++){
+					if (Final[i][j] == 0.){
+						pfail += Prob[i][j];
+						Prob[i][j] = 0.;
+					}
+					
+				}
+			}
+			
+			for(int i=0; i<N_nodes-1; i++){
+				for( int j=i+1; j<N_nodes; j++){
+					Prob[i][j] /= (1.-pfail);
+				}
+				//std::cout << std::endl;
+			}			
+			
+			n_failed = sample_failed_number(pfail);
+			while(publishable < N_steps){
+				
+		    	result = sample();
+			    publishable += result;					
+			}
+            
+            
+			current_loss += (n_failed + N_steps);
+			cost = current_loss;
+			
+			for(int i=0; i<N_nodes-1; i++){
+				for( int j=i+1; j<N_nodes; j++){
+					
+					if (Tried[i][j]>0. && Final[i][j]>0.){
+						novel+=1.;
+					}
+				}
+			}
+		}
+        
+        current_novelty = novel;
+        
+        
+		//^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+		if (verbose_level == 2){
+            std::cout << (current_repeat+1) << "  epoch=" << (current_epoch+1) 
+            
+		    << "  cost=" << cost 
+		    << " novel=" << novel 
+		    << " rel_loss=" << cost/novel
+		    << std::endl;
+        }
+        
+		current_epoch++;
+	}
+	
+	
+	//======  Destructor ======
+	~Universe(){
+		
+		delete_2Dmatrix(Final, N_nodes);
+		delete_2Dmatrix(Dist, N_nodes);
+		delete_2Dmatrix(Tried, N_nodes);
+		delete_2Dmatrix(Prob, N_nodes);
+        delete_2Dmatrix(EdgeIndex, N_nodes);
+		delete_1Dmatrix(Rank);		
+	}
+    
+    //================================================
+    // Allocate memory
+    double** allocate_2Dmatrix(int N, int M)
+    {
+        double **pointer;
+        
+        if (verbose_level == 2){
+            std::cout<< "["<<N<<"|"<<M<<"]"<<std::endl;
+        }
+        pointer = new double*[N];
+        for (int i = 0; i < N; ++i)
+            pointer[i] = new double[M];	
+        
+        return pointer;
+    }
+    //===================
+    double* allocate_1Dmatrix(int N)
+    {
+        double *pointer;
+        
+        if(N > 0){
+            
+            pointer = new double[N];
+            
+        }else {
+            
+            pointer = NULL;
+        }
+        
+        return pointer;
+        
+    }
+    
+    //==============================================
+    // De-Allocate memory to prevent memory leak
+    void delete_2Dmatrix(double **pointer, int N){
+        
+        if (pointer != NULL){
+            
+            for (int i = 0; i < N; ++i){
+                delete [] pointer[i];
+            }
+            delete [] pointer;
+        }
+    }
+    //====================
+    void delete_1Dmatrix(double *pointer){
+        
+        delete [] pointer;
+    }
+    
+    //===========================================
+    double get_rel_loss(){
+        
+        return CumulativeRelativeLoss ;
+    }
+    
+    //===========================================
+    double get_rel_loss_err(){
+        
+        return CRLsquare ;
+    }
+    
+    
+    
+    //==================================================================================
+    void evolve_to_target_and_save(int istart, int iend, double* storage, int* counters){
+        
+        double ALOT=100000000000.;
+        
+	// std::cout<<" evolve_to_target_and_save: istart=" << istart << "iend=" << iend << "\n";
+
+        reset_world();
+        
+        for (int k = istart; k < iend; k++){
+
+	  // std::cout<<" evolve: k=" << k << "\n";
+
+            
+            
+            for(int i=0; i< N_epochs &&  current_novelty < TargetNovelty; i++){
+	      // std::cout<<" evolve: k=" << k << " i=" << i << " cur=" << current_novelty << " Target=" << TargetNovelty << "\n";
+                update_world();
+            }
+            
+            storage[k]=current_loss/current_novelty;
+            counters[k]=1;
+            
+            
+            reset_world();
+        }
+        
+    }
+    //==============================================
+    int get_reruns(void){
+        return N_repeats;
+    }
+    
+    //==============================================
+    double get_parameter(int i){
+        
+        switch(i){
+            case 0:
+                return alpha_i;
+            case 1:
+                return alpha_m;
+            case 2:
+                return beta;
+            case 3:
+                return gamma;
+            case 4:
+                return delta;
+            default:
+                
+                std::cout << "Erroneous parameter id!!!!\n\n\n";
+                return 0.;
+        }
+    }
+    
+    
+    //==============================================
+    void evolve_to_target(){
+        
+        reset_world();
+        if (beta < -1. || gamma < -1.){
+            CumulativeRelativeLoss = 100000000000.;
+            CRLsquare = 0.;
+            return;
+        }
+        
+        
+        for (int k=0; k< N_repeats; k++){
+            
+            
+            for(int i=0; i<N_epochs &&  current_novelty < TargetNovelty; i++){
+                update_world();
+            }
+            
+            CumulativeRelativeLoss += current_loss/current_novelty;
+            CRLsquare += (current_loss/current_novelty)*(current_loss/current_novelty);
+            if(verbose_level==3){
+                std::cout <<  CumulativeRelativeLoss << " | " << CRLsquare << std::endl;
+            }
+            
+            if(verbose_level==1){
+                std::cout <<  "." ;
+                
+            }
+            else if(verbose_level==2){
+                std::cout <<  "**" << (k+1) <<  "**  curr loss " << current_loss << "; curr novelty " << current_novelty << std::endl;
+            }
+            
+            
+            reset_world();
+        }
+        
+        CumulativeRelativeLoss /= double(N_repeats);
+        CRLsquare /= double(N_repeats);
+        
+        if(verbose_level==1){
+            std::cout << std::endl;
+        }
+        
+        if(verbose_level==2){
+            std::cout <<  CumulativeRelativeLoss << " || " << CRLsquare << std::endl;
+        }
+        
+        CRLsquare = 2*sqrt((CRLsquare - CumulativeRelativeLoss*CumulativeRelativeLoss)/double(N_repeats));
+        
+    }
+    
+    
+    //================================================================    
+    int set_parameter(double value, int position){
+        
+        if (position < 0 || position > 4) {return 0;}
+        
+        else {
+            
+            switch(position){
+                case 0:
+                    alpha_i=value;
+                    return 1;
+                case 1:
+                    alpha_m=value;
+                    return 1;
+                case 2:
+                    beta=value;
+                    return 1;
+                case 3:
+                    gamma=value;
+                    return 1;
+                case 4:
+                    delta=value;
+                    return 1;
+            }
+            
+        }
+        
+        return 0;
+    }
+
+#ifdef notdef
+    //=================================================================
+    void try_annealing(double starting_jump, int iterations, 
+                       double temp_start, double temp_end, double target_rejection){
+        
+        double dx[5]={0.,0.,0.,0.,0};
+        double x[5]={0.,0.,0.,0.,0};
+        double rejection[5]={0., 0., 0., 0., 0.};
+        double curr_x, curr_err, x_tmp;
+        double temperature;
+        double ratio, r;
+        int cycle=10;
+        boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
+        
+        // set up parameter for annealing
+        
+        x[0]=alpha_i;
+        x[1]=alpha_m;
+        x[2]=beta;
+        x[3]=gamma;
+        x[4]=delta;
+        
+        for(int i=0;i<5;i++){
+            dx[i] = starting_jump;
+        }
+        
+        // establish the current value
+        
+        //..........................................
+        evolve_to_target();        
+        std::cout << CumulativeRelativeLoss << " +- " << CRLsquare << std::endl;
+        
+        curr_x   = CumulativeRelativeLoss;
+        curr_err = CRLsquare;
+        CumulativeRelativeLoss = 0;
+        CRLsquare = 0;
+        //...........................................
+        
+        // optimization cycle
+        for(int i=0; i<iterations; i++){
+            
+            temperature = temp_start*exp( i*(log(temp_end)-log(temp_start))/(double)iterations);
+            std::cout  << std::endl << "....T = " << wrap_double(temperature,3) << std::endl << std::endl;
+            
+            if (i % cycle == 0 && i > 0){
+                
+                for (int k=0; k<5; k++){
+                    
+                    rejection[k]/=(double)cycle;
+                    if (rejection[k] > 0){
+                        dx[k] = dx[k]/(rejection[k]/target_rejection);
+                        rejection[k]=0.;
+                    }
+                    else{
+                        dx[k]*=2.;
+                    }
+                    std::cout  << dx[k] << " ";
+                }
+                std::cout  << std::endl;
+            }
+            
+            
+            for (int j=0; j<5; j++){
+                
+                // get new value of x[j]
+                x_tmp = get_new_x(x[j],dx[j]);
+                
+                
+                
+                //.............................................
+                set_parameter(x_tmp, j);
+                
+                
+                evolve_to_target(); 
+                
+                std::cout  << std::endl << "......... " << std::endl;
+                std::cout << "Trying... " << CumulativeRelativeLoss << " +- " << CRLsquare << std::endl;
+                
+                ratio = min(1.,exp(-(CumulativeRelativeLoss-curr_x)/temperature));
+                r = uni();
+                std::cout << r << " vs " << ratio << std::endl;
+                
+                if (r > ratio){
+                    
+                    std::cout << string_wrap(id, 4) <<" "<< (i+1) << ","<< (j) 
+                    <<" "<< (i+1) << " Did not accept " 
+                    << x_tmp << "(" << j << ")" << std::endl;
+                    std::cout << alpha_i << " "<< alpha_m << " "
+                    << beta << " " << gamma << " " 
+                    << delta << " " << std::endl;
+                    set_parameter(x[j], j);
+                    CumulativeRelativeLoss = 0;
+                    CRLsquare = 0;
+                    
+                    rejection[j]+=1.;
+                }
+                
+                else {
+                    
+                    curr_x   = CumulativeRelativeLoss;
+                    curr_err = CRLsquare;
+                    x[j] = x_tmp;
+                    CumulativeRelativeLoss = 0;
+                    CRLsquare = 0;
+                    std::cout << (i+1) << string_wrap((string) " Rejection counts: ", 8) 
+                    << wrap_double(rejection[0],2) 
+                    << " "<< wrap_double(rejection[1], 7) << " "
+                    << wrap_double(rejection[2],5) << " " << wrap_double(rejection[2],9) << " " 
+                    << wrap_double(rejection[4],6) << " " 
+                    << std::endl << std::endl;
+                    
+                    std::cout << string_wrap(id, 4) <<" "<< (i+1) <<","<< (j) 
+                    <<" "
+                    << string_wrap((string) "***** Did accept! ", 3) 
+                    << wrap_double(alpha_i,2) 
+                    << " "<< wrap_double(alpha_m, 7) << " "
+                    << wrap_double(beta,5) << " " 
+                    << wrap_double(gamma,9) << " " 
+                    << wrap_double(delta,6) << " " 
+                    << std::endl << std::endl;
+                    
+                }
+                //........................................................ 
+                
+            }
+            
+        }
+        
+    }
+    
+#endif // notdef
+
+	
+};
+
+
+//============================================================
+
+std::pair<double,double> multi_loss( // dispatch_group_t group, 
+                                    Universe* un[], 
+                                    // dispatch_queue_t* CustomQueues,
+                                    double* Results,
+                                    int*    Counters,
+                                    double* params){
+    
+    int N = un[0]->get_reruns();
+    int step = (int)(double)N/(double)(Nworkers);
+    int istart=0;
+    int iend = istart+step;
+    
+    double Loss=0., LossSquare=0.;
+    
+    timeval startTime, endTime;
+    double elapsedTime;
+    // start timer
+    gettimeofday(&startTime, NULL);
+
+    //err:    for(int i=0; i<Nworkers; i++){
+
+    std::cout << "Entry to multi_loss, params: ";
+    for(int i=0; i<Nworkers; i++){
+        for(int j=0; j<NEVOPARAMS; j++){
+	  std::cout << "[" << un[i]->get_parameter(j) << "," << params[j] << "] ";
+            un[i]->set_parameter(params[j],j);
+        }
+    }
+    std::cout << "\n";
+    int i;
+    #pragma omp parallel for private (i)
+    for(i=0; i<Nworkers; i++){
+
+      // dispatch_group_async(group, CustomQueues[i], ^{
+      std::cout<<"multi_loss: Calling evolve_to_target_and_save i=" << i << " N=" << N << " step=" << step << " istart=" << i*step << " iend=" << (i+1)*step << "\n";
+      //un[i]->evolve_to_target_and_save(istart, iend, Results, Counters);
+      un[i]->evolve_to_target_and_save(i*step, min((i+1)*step,N), Results, Counters);
+      //});
+
+      // std::cout<<"multi_loss: Returned from evolve_to_target_and_save " << i << "\n";
+      
+
+      //istart += step;
+      //iend = min(istart+step,N);
+            
+    }
+    // err    }
+    // dispatch_group_wait(group, DISPATCH_TIME_FOREVER);
+    //dispatch_release(group);
+    
+
+    for (int i=0; i<N; i++){
+        
+        Loss+=Results[i]/(double)N;
+        LossSquare+=Results[i]*Results[i]/(double)N;
+        
+        std::cout<<i<<":"<< Results[i] << " ";
+    }
+    
+    std::cout<<" \n\n\n";
+    double two_std = ((LossSquare - Loss*Loss)/(double)N);
+    
+    two_std = 2.*sqrt(two_std);
+    std::pair<double,double> Res;
+    Res.first=Loss;
+    Res.second=two_std;
+
+    gettimeofday(&endTime, NULL);
+    elapsedTime = (endTime.tv_sec - startTime.tv_sec) * 1000.0;      // sec to ms
+    elapsedTime += (endTime.tv_usec - startTime.tv_usec) / 1000.0;   // us to ms
+    elapsedTime /= 1000.;
+    cout << "multi_loss(N=" << N << ") elapsed time: " << elapsedTime << " seconds " << elapsedTime/60. << " minutes\n\n";
+    
+    return Res;
+}
+//============================================================
+
+
+//============================================================
+void multi_annealing( // dispatch_group_t group, 
+                     Universe* un[], 
+                     // dispatch_queue_t* CustomQueues, 
+                     double T_start, double T_end, 
+                     double Target_rejection, 
+                     int Annealing_repeats, 
+                     double starting_jump,
+                     double* Results,
+                     int*    Counters,
+                     double* params0,
+                     double annealing_cycles){
+    //.................................
+    // re-implement annealing
+    
+    double dx[NEVOPARAMS]={0.,0.,0.,0.,0};
+    double x[NEVOPARAMS]={0.,0.,0.,0.,0};
+    double rejection[NEVOPARAMS]={0., 0., 0., 0., 0.};
+    double curr_x, curr_err, x_tmp;
+    double temperature;
+    double ratio, r;
+    int cycle=10;
+    //boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
+    
+    // set up parameter for annealing
+
+    for(int i=0;i<NEVOPARAMS;i++){
+        x[i]=params0[i];
+        dx[i] = starting_jump;
+	for(int w=0; w<Nworkers; w++){
+	  un[w]->set_parameter(x[i], i);
+	}
+    }
+    
+    // establish the current value
+    std::pair<double,double>Res;
+    
+    if ( operation == 'm' ) {
+      // Nworkers = 1;
+    }
+    else if (operation == 'g') {
+      // generate params: not yet implemented - to be deprecated
+    }
+    else if (operation == 'a') {
+      // analyze multi_loss() results: not tested or used - to be deprecated
+
+      string line;
+      ifstream mlossdata ("multi_loss.data");
+      double d, Loss, LossSquare, two_std;
+      bool b;
+      int n=0;
+      if (mlossdata.is_open()) {
+	while ( getline (mlossdata,line) ) {
+	  b = from_string<double>(d, std::string(line), std::dec);
+	  cout << line << " d=" << d << endl;
+	  Loss += d;
+          LossSquare += (d*d);
+          n++;
+	}
+        Loss /= double(n);
+	LossSquare /= double(n);
+	two_std = ((LossSquare - Loss*Loss)/(double)n);
+	two_std = 2.*sqrt(two_std);
+	std::cout<<"n="<<n<<" Loss="<<Loss<<" LossSquare="<<LossSquare<<" two_std="<<two_std<<"\n\n\n";
+	mlossdata.close();
+	FILE *f=fopen("multi_loss_stats.txt","w");
+	fprintf(f,"%d n\n",n);
+	fprintf(f,"%.20e Loss\n",Loss);
+	fprintf(f,"%.20e LossSquare\n",LossSquare);
+	fprintf(f,"%.20e two_std\n",two_std);
+        fclose(f);
+	exit(0);
+      }
+      else {
+	cout << "Unable to open file multi_loss.data"; 
+	exit(1);
+      }
+    }
+
+    std::cout << "Calling initial multi_loss:\n";
+    Res = multi_loss( /* group,*/ un, /* CustomQueues,*/ Results, Counters, x);
+    std::cout << "Ret from initial multi_loss:\n";
+    std::cout << Res.first << " +- " << Res.second << std::endl;
+
+    if ( operation == 'm' ) {
+      FILE *f;
+      int N = un[0]->get_reruns();
+
+      f = fopen("multi_loss.data","w");
+      for(int i=0; i<N; i++) {
+	fprintf(f,"%.20e\n",Results[i]);
+      }
+      fclose(f);
+      exit(0);
+    }
+    
+    curr_x   = Res.first;
+    curr_err = Res.second;
+    
+    // optimization cycle
+    
+    for(int i=0; i<annealing_cycles; i++){
+        
+        temperature = T_start*exp( i*(log(T_end)-log(T_start))/(double)annealing_cycles);
+        std::cout  << std::endl << "....T = " << wrap_double(temperature,3) << std::endl << std::endl;
+        
+        if (i % cycle == 0 && i > 0){
+            
+            for (int k=0; k<NEVOPARAMS; k++){
+                rejection[k]/=(double)cycle;
+                
+                if (rejection[k] > 0){
+                    dx[k] = dx[k]/(rejection[k]/Target_rejection);
+                    rejection[k]=0.;
+                }
+                else{
+                    dx[k]*=2.;
+                }
+                std::cout  << dx[k] << " ";
+            }
+            std::cout  << std::endl;
+        }
+
+        for (int j=0; j<NEVOPARAMS; j++){
+            
+            ///////////////////////////////
+            if (FIX_VARIABLES==0 || var_fixed[j]==0){
+                
+                
+                
+                // get new value of x[j]
+                double x_hold=x[j];
+                x_tmp = get_new_x(x[j],dx[j]);
+                x[j]=x_tmp;
+                
+                std::cout << wrap_double(x_tmp,10) << " " << wrap_double(j,9) << "\n\n"; 
+                //=======================================
+                //.............................................
+                for(int w=0; w<Nworkers; w++){
+                    un[w]->set_parameter(x_tmp, j);
+                }
+                
+
+		// WRITE OUT PARAMS HERE; then exit.
+
+		std::cout << "Calling multi_loss: i=" << i << " j=" << j << "\n";
+                Res = multi_loss(/* group, */ un, /* CustomQueues, */ Results, Counters, x);
+		std::cout << "Ret from multi_loss: i=" << i << " j=" << j << "\n";
+                std::cout << Res.first << " +- " << Res.second << std::endl;
+                
+                ratio = min(1.,exp(-(Res.first-curr_x)/temperature));
+                r = rand()/(double)(pow(2.,31)-1.);
+                std::cout << r << " vs " << ratio << std::endl;
+                
+                double ALOT=100000000000.;
+                
+                if (Res.first < ALOT)
+                {
+                    ofstream filestr;
+                    
+                    filestr.open ("best_opt_some.txt", ofstream::app);
+                    
+                    // >> i/o operations here <<
+                    filestr << un[0]->get_target() << "," 
+                    << Res.first 
+                    << "," << un[0]->get_parameter(0) 
+                    << "," << un[0]->get_parameter(1) 
+                    << "," << un[0]->get_parameter(2) 
+                    << "," << un[0]->get_parameter(3) 
+                    << "," << un[0]->get_parameter(4) << "," << Res.second << ",\n";
+                    
+                    filestr.close();
+                    
+                    
+                    filestr.open ("max_dist.txt", ofstream::app);
+                    
+                    // >> i/o operations here <<
+                    filestr << max_dist << ",\n";
+                    
+                    filestr.close();
+                    
+                }
+                
+                
+                if (r > ratio){
+                    
+                    std::cout << " "<< (i+1) << ","<< (j) 
+                    <<" "<< (i+1) << " Did not accept " 
+                    << x_tmp << "(" << j << ")" << std::endl;
+                    std::cout << un[0]->get_parameter(0) 
+                    << " " << un[0]->get_parameter(1) 
+                    << " " << un[0]->get_parameter(2) 
+                    << " " << un[0]->get_parameter(3) 
+                    << " " << un[0]->get_parameter(4) << " " << std::endl;
+                    
+                    x[j]=x_hold;
+                    for(int w=0; w<Nworkers; w++){
+                        un[w]->set_parameter(x[j], j);
+                    }
+                    
+                    
+                    //set_parameter(x[j], j);     
+                    rejection[j]+=1.;
+                }
+                
+                else {
+                    
+                    curr_x   = Res.first;
+                    curr_err = Res.second;
+                    x[j] = x_tmp;
+                    
+                    for(int w=0; w<Nworkers; w++){
+                        un[w]->set_parameter(x[j], j);
+                    }
+                    
+                    std::cout << (i+1) << string_wrap((string) " Rejection counts: ", 8) 
+                    << wrap_double(rejection[0],2) << " " 
+                    << wrap_double(rejection[1],7) << " "
+                    << wrap_double(rejection[2],5) << " " 
+                    << wrap_double(rejection[3],9) << " " 
+                    << wrap_double(rejection[4],6) << " " 
+                    << std::endl << std::endl;
+                    
+                    std::cout << " "<< (i+1) <<","<< (j) 
+                    <<" "
+                    << string_wrap((string) "***** Did accept! ", 3) 
+                    << wrap_double(un[0]->get_parameter(0),2) << " "
+                    << wrap_double(un[0]->get_parameter(1),7) << " "
+                    << wrap_double(un[0]->get_parameter(2),5) << " " 
+                    << wrap_double(un[0]->get_parameter(3),9) << " " 
+                    << wrap_double(un[0]->get_parameter(4),6) << " " 
+                    << std::endl << std::endl;
+                    
+                    
+                    
+                }
+                //........................................................ 
+                
+            }
+        }
+        
+    }
+    
+}
+
+
+//================================================
+int
+main(int argc, char* argv[])
+{
+    
+    double params0[6] = {0., 0., 0., 0., 0., 0.2}, target=50., range;
+    string par_names0[6] = {"alpha_i", "alpha_m", "beta", "gamma", "delta", "target"};
+    string par_names1[4] = {"n_epochs", "n_steps", "n_reruns", "range"};
+    string par_names2[5] = {"T_start", "T_end", "Annealing_steps","Target_rejection","Starting_jump"};
+    string par_names3[5] = {"FREEZE_alpha_i", "FREEZE_alpha_m", "FREEZE_beta", "FREEZE_gamma", "FREEZE_delta"};    
+    string par_names4[2] = {"Operation", "Nworkers"};    
+    int params1[4] = {300, 50, 1000, 10};
+    int params3[5] = { 0, 0, 0, 0, 0};
+    
+    //          temperature_start,  temperature_end,  annealing_steps target_rejection  Starting_jump
+    double params2[5] = {1,             0.001,               100,              0.3,           1.5};
+    
+    int verbose_level = 2;
+    const std::string one="one", two="two";
+    static Universe* un[MAXNworkers];
+    // static dispatch_queue_t CustomQueues[MAXNworkers];
+    
+    static double* Results;
+    static int*    Counters;
+    
+    timeval t1, t2;
+    double elapsedTime;
+    // start timer
+    gettimeofday(&t1, NULL);
+    
+    
+    if (argc < 8) {
+        std::cout << "Usage: super_optimizer alpha_i alpha_m beta gamma delta target_innov [n_epochs n_steps n_reruns] [range] [verbose_level]\n"; 
+        std::cout << "         [T_start T_end Annealing_steps Target_rejection Starting_jump]\n"; 
+        std::cout << "         [FREEZE_alpha_i FREEZE_alpha_m FREEZE_beta FREEZE_gamma FREEZE_delta]\n"; 
+        
+        system("pwd");
+        
+        
+        return(1);
+    }
+    else {
+      std::cout << "argc=" << argc << std::endl;
+
+        for (int nArg=0; nArg < argc; nArg++){
+            //std::cout << nArg << " " << argv[nArg] << std::endl;
+            if (nArg > 0 && nArg < 7){
+                params0[nArg-1]= atof(argv[nArg]);
+                std::cout << par_names0[nArg-1] << ": " << params0[nArg-1] <<  std::endl;
+            }
+            if (nArg > 6 && nArg < 11){
+                params1[nArg-7]= atoi(argv[nArg]);
+                std::cout << par_names1[nArg-7] << ": " << params1[nArg-7] <<  std::endl;
+            }
+            if (nArg == 11){
+                verbose_level = atoi(argv[nArg]);
+                std::cout << "verbose level: " << verbose_level <<  std::endl;
+            }
+            if (nArg > 11 && nArg < 17){
+                params2[nArg-12]= atof(argv[nArg]);
+                std::cout << par_names2[nArg-12] << ": " << params2[nArg-12] <<  std::endl;
+            }
+            if (nArg > 16 && nArg < 22){
+                params3[nArg-17]= atof(argv[nArg]);
+                var_fixed[nArg-17]= atof(argv[nArg]);
+                std::cout << par_names3[nArg-17] << ": " << var_fixed[nArg-17] <<  std::endl;
+            }
+            if (nArg == 22 ){
+                operation = *argv[nArg];
+		std::cout << par_names4[0] << ": " << operation <<  std::endl;
+            }
+            if (nArg == 23 ){
+	        Nworkers = atoi(argv[nArg]);
+		std::cout << par_names4[1] << ": " << Nworkers <<  std::endl;
+            }
+        }
+    }
+    
+    /*
+    for target in range(58,1009,50):
+	s = ("%d" % target)
+	print s
+
+	for i in range(15):
+          # Param groups separated by "|" below for documentation. NOTE that | is not used on command line!
+	  os.system("./supe_duper_optimizer |0 0 4 50 -1 "+s+" | 40000 20 1000 2 | 1 | 2. 0.01 100 0.3 2.3 | 1 1 0 0 0")
+    */
+
+    /*   Parameters, re-iterated:
+
+                        "alpha_i", "alpha_m", "beta", "gamma", "delta", "target"};
+    double params0[6] = {0.,        0.,        0.,     0.,      0.,      0.2}, target=50., range;
+
+                    {"n_epochs", "n_steps", "n_reruns", "range"};
+    int params1[4] = {300,        50,       1000,        10};
+
+                       {"T_start",          "T_end",          "Annealing_steps","Target_rejection","Starting_jump"};
+                         temperature_start,  temperature_end,  annealing_steps target_rejection  Starting_jump
+    double params2[5] = {1,                  0.001,            100,            0.3,              1.5};
+
+                     {"FREEZE_alpha_i", "FREEZE_alpha_m", "FREEZE_beta", "FREEZE_gamma", "FREEZE_delta"};    
+    int params3[5] = { 0,                0,                0,             0,              0};
+
+
+    */
+
+    for (int j=0; j<NEVOPARAMS; j++){
+        
+        cout << j << " | " << var_fixed[j] << " (fixed) \n";
+    }
+	
+    target=params0[NEVOPARAMS];
+    range = (double)params1[3];
+	int identify_failed = 0;
+	char* filename= (char *)"movie_graph.txt";
+ 	int n_ep=params1[0], n_st=params1[1], n_rep=params1[2];
+    
+    //...............................
+    
+    for(int i=0; i<Nworkers; i++){
+        un[i] = new Universe((char *)filename,n_ep,n_st,
+                             (int)n_rep,
+                             identify_failed, target, i2string(i));
+        // CustomQueues[i] = dispatch_queue_create(i2char(i), NULL);
+    }
+    
+    //...............................
+    // mw: n_rep == n_reruns = # times evolve is done within multi_loss, spread across Nworkers.
+    if(n_rep > 0){
+        
+        Results = new double[n_rep];
+        Counters = new int[n_rep];
+        
+    }else {
+        
+        Results =  NULL;
+        Counters = NULL;
+        std::cout << " Number of reruns should be positive! " <<  std::endl;
+        return 0;
+        
+    }
+    //...............................
+
+    //srand(time(0));
+    //srandomdev();
+    {
+      timeval t; 
+      gettimeofday(&t, NULL);
+      srand(t.tv_usec);
+    }
+    
+    {
+        double r=0;
+        for (int j=0; j<100; j++){
+            
+            
+            
+            r = rand()/(double)(pow(2.,31)-1.);
+            std::cout << r << " ";
+        }
+        std::cout << "\n ";
+    }
+  	//random initiation of starting parameters
+    
+    if (range > 0.){
+        
+        for (int i=0; i < 5; i++){
+            
+            if (params0[i]==-100.){
+                
+                double r1 = (rand()/(double)(pow(2.,31)-1.));
+                double r2 = (rand()/(double)(pow(2.,31)-1.));
+                double sign = 1.;
+                
+                if(r1 > 0.5){
+                    sign=-1.;
+                }
+                
+                params0[i] = sign*r2*range;
+                
+                std::cout << par_names0[i] << ": " << params0[i] <<  std::endl;
+            }
+        }
+        
+    }
+    
+    
+    double T_start=params2[0], T_end=params2[1], Target_rejection=params2[3], starting_jump=params2[4];
+    int Annealing_repeats = (int) params2[2];
+    
+    
+    // dispatch_group_t group = dispatch_group_create();
+    
+    //.............................
+    multi_annealing( /* group, */ un, /* CustomQueues, */ T_start, T_end, Target_rejection, Annealing_repeats, 
+		     starting_jump, Results, Counters, params0, Annealing_repeats);
+    
+    //dispatch_group_wait(group, DISPATCH_TIME_FOREVER);
+    // dispatch_release(group);
+    //.............................
+    
+    
+    // stop timer
+    gettimeofday(&t2, NULL);
+    
+    // compute and print the elapsed time in millisec
+    elapsedTime = (t2.tv_sec - t1.tv_sec) * 1000.0;      // sec to ms
+    elapsedTime += (t2.tv_usec - t1.tv_usec) / 1000.0;   // us to ms
+    elapsedTime /= 1000.;
+    cout << elapsedTime << " seconds " << elapsedTime/60. << " minutes\n\n";
+    
+    //.....................
+    
+    for(int i=0; i<Nworkers; i++){
+        delete un[i];
+    }
+    
+    //....................
+    if(n_rep > 0){
+        
+        delete [] Results;
+        delete [] Counters;
+        
+    }
+    
+    return 0;
+	
+	
+	
+}
+

Copied: SwiftApps/SciColSim/old/optirun.swift (from rev 5634, SwiftApps/SciColSim/optirun.swift)
===================================================================
--- SwiftApps/SciColSim/old/optirun.swift	                        (rev 0)
+++ SwiftApps/SciColSim/old/optirun.swift	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,41 @@
+type file;
+
+app (file outfile, file best, file max) optimize ( string args[], file graph )
+{
+  optimizersh @best @max args stdout=@outfile ;
+}
+
+int minrange=58;
+int maxrange=1009;
+//int maxrange=209;
+int rangeinc=50;
+
+int nreps=2; # 15
+
+//      [alpha_i alpha_m beta gamma delta target_innov
+//      [n_epochs n_steps n_reruns] [range]
+//      [verbose_level]
+//      [T_start T_end Annealing_steps Target_rejection Starting_jump]
+//      [FREEZE_alpha_i FREEZE_alpha_m FREEZE_beta FREEZE_gamma FREEZE_delta]
+
+file graph <"movie_graph.txt">;
+
+foreach target in [minrange:maxrange:rangeinc] {
+  foreach rep in [1:nreps] {
+    file outfile <single_file_mapper; file=@strcat("output/T",target,".R",rep,".out")>;
+    // file errfile <single_file_mapper; file=@strcat("output/T",target,".R",rep,".err")>;
+    file bestfile <single_file_mapper; file=@strcat("output/T",target,".R",rep,".best_opt_some")>;
+    file maxfile <single_file_mapper; file=@strcat("output/T",target,".R",rep,".max_dist")>;
+
+    // string longargs[] = @strcat("0 0 4 50 -1 ",target," 40000 20 1000 2 1 2. 0.01 100 0.3 2.3 1 1 0 0 0");
+
+    string fastargs1[] = ["0", "0", "4", "50", "-1", @strcat(target), "40000", "20", "1000", "2", "1", "2.", "0.01", "100", "0.3", "2.3", "1", "1", "0", "0", "0"];
+    string fastargs2[] = [
+       "0", "0", "4", "50", "-1", @strcat(target),
+       "40000", "20", "1000", "2",
+       "1",
+       "2.", "0.01",  "5", "0.3", "2.3",
+       "1", "1", "0", "0", "0"];
+    (outfile, bestfile, maxfile) = optimize(fastargs2,graph);
+  }
+}

Copied: SwiftApps/SciColSim/old/sites.beagle.quick.xml (from rev 5634, SwiftApps/SciColSim/sites.beagle.quick.xml)
===================================================================
--- SwiftApps/SciColSim/old/sites.beagle.quick.xml	                        (rev 0)
+++ SwiftApps/SciColSim/old/sites.beagle.quick.xml	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,25 @@
+<config>
+  <pool handle="beagle">
+    <execution provider="coaster" jobmanager="local:pbs"/>
+    <profile namespace="globus" key="project">CI-MCB000119</profile>
+
+    <profile namespace="env" key="SWIFT_GEN_SCRIPTS">KEEP</profile>
+
+    <profile namespace="globus" key="jobsPerNode">1</profile>
+    <profile namespace="env" key="OMP_NUM_THREADS">24</profile>
+    <profile namespace="globus" key="workerLoggingLevel">DEBUG</profile>
+    <profile namespace="globus" key="lowOverAllocation">100</profile>
+    <profile namespace="globus" key="highOverAllocation">100</profile>
+    <profile namespace="globus" key="providerAttributes">pbs.aprun;pbs.mpp;depth=24</profile>
+    <profile namespace="globus" key="maxTime">10000</profile>
+    <profile namespace="globus" key="maxWallTime">01:30:00</profile>
+    <profile namespace="globus" key="slots">50</profile>
+    <profile namespace="globus" key="nodeGranularity">2</profile>
+    <profile namespace="globus" key="maxNodes">2</profile>
+    <profile namespace="globus" key="queue">route</profile>
+    <profile namespace="karajan" key="jobThrottle">9.59</profile>
+    <profile namespace="karajan" key="initialScore">10000</profile>
+    <filesystem provider="local"/>
+    <workdirectory >/lustre/beagle/ketan/labs/SciColSim-Bgl/swift.workdir</workdirectory>
+  </pool>
+</config>

Copied: SwiftApps/SciColSim/old/sites.beagle.xml (from rev 5634, SwiftApps/SciColSim/sites.beagle.xml)
===================================================================
--- SwiftApps/SciColSim/old/sites.beagle.xml	                        (rev 0)
+++ SwiftApps/SciColSim/old/sites.beagle.xml	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,24 @@
+<config>
+  <pool handle="beagle">
+    <execution provider="coaster" jobmanager="local:pbs"/>
+    <profile namespace="globus" key="project">CI-MCB000119</profile>
+
+    <profile namespace="env" key="SWIFT_GEN_SCRIPTS">KEEP</profile>
+    <!--<profile namespace="globus" key="jobsPerNode">24</profile>-->
+    <profile namespace="env" key="OMP_NUM_THREADS">24</profile>
+    <profile namespace="globus" key="workerLoggingLevel">DEBUG</profile>
+    <profile namespace="globus" key="jobsPerNode">1</profile>
+    <profile namespace="globus" key="providerAttributes">pbs.aprun;pbs.mpp;depth=24</profile>
+    <profile namespace="globus" key="maxTime">37000</profile>
+    <profile namespace="globus" key="maxWallTime">10:00:00</profile>
+    <profile namespace="globus" key="slots">20</profile>
+    <profile namespace="globus" key="lowOverAllocation">100</profile>
+    <profile namespace="globus" key="highOverAllocation">100</profile>
+    <profile namespace="globus" key="nodeGranularity">2</profile>
+    <profile namespace="globus" key="maxNodes">2</profile>
+    <profile namespace="karajan" key="jobThrottle">9.59</profile>
+    <profile namespace="karajan" key="initialScore">10000</profile>
+    <filesystem provider="local"/>
+    <workdirectory >/lustre/beagle/ketan/labs/SciColSim-Bgl/swift.workdir</workdirectory>
+  </pool>
+</config>

Copied: SwiftApps/SciColSim/old/t1.py (from rev 5634, SwiftApps/SciColSim/t1.py)
===================================================================
--- SwiftApps/SciColSim/old/t1.py	                        (rev 0)
+++ SwiftApps/SciColSim/old/t1.py	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,22 @@
+#! /usr/bin/env python
+import os
+
+# FULL for target in range(58,1009,50):
+# FAST for target in range(58,209,50):
+for target in range(58,59,50):
+	s = ("%d" % target)
+	print s
+
+# FULL	for i in range(15):
+# FAST	for i in range(2):
+	for i in range(1):
+		args="./toptimizer 0 0 4 50 -1 "+s+" 40000 20 1000 2 1 2. 0.01 100 0.3 2.3 1 1 0 0 0 # > out.T"+str(target)+".i"+str(i)
+		args="OMP_NUM_THREADS=24 ./toptimizer 0 0 4 50 -1 "+s+" 40000 20 100 2 1 2. 0.01 2 0.3 2.3 1 1 0 0 0 # > out.T"+str(target)+".i"+str(i)
+		args="OMP_NUM_THREADS=24 ./toptimizer 0 0 4 50 -1 "+s+" 40000 20 100 2 1 2. 0.01 2 0.3 2.3 1 1 1 0 0 # > out.T"+str(target)+".i"+str(i)
+		print("\n\n **** CALLING APP: "+args+"\n\n\n")
+	        os.system(args);
+#		print("\n\n **** CALLING APP: ./optimizer 0 0 4 50 -1 "+s+" 40000 20 10 2 1 2. 0.01 100 0.3 2.3 1 1 0 0 0\n\n\n")
+#		FAST: os.system("./optimizer 0 0 4 50 -1 "+s+" 40000 20 10 2 1 2. 0.01 2 0.3 2.3 1 1 0 0 0")
+
+
+print "Done!"

Copied: SwiftApps/SciColSim/old/t2.py (from rev 5634, SwiftApps/SciColSim/t2.py)
===================================================================
--- SwiftApps/SciColSim/old/t2.py	                        (rev 0)
+++ SwiftApps/SciColSim/old/t2.py	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,23 @@
+#! /usr/bin/env python
+import os
+
+# FULL for target in range(58,1009,50):
+# FAST for target in range(58,209,50):
+for target in range(58,59,50):
+	s = ("%d" % target)
+	print s
+
+# FULL	for i in range(15):
+# FAST	for i in range(2):
+	for i in range(1):
+		args="./toptimizer 0 0 4 50 -1 "+s+" 40000 20 1000 2 1 2. 0.01 100 0.3 2.3 1 1 0 0 0 # > out.T"+str(target)+".i"+str(i)
+		args="OMP_NUM_THREADS=24 ./toptimizer 0 0 4 50 -1 "+s+" 40000 20 100 2 1 2. 0.01 2 0.3 2.3 1 1 0 0 0 # > out.T"+str(target)+".i"+str(i)
+		args="./toptimizer 0 0 4 50 -1 "+s+" 40000 20 75    2 1 2. 0.01 2 0.3 2.3 1 1 1 0 0 m # > out.T"+str(target)+".i"+str(i)
+		args="OMP_NUM_THREADS=24 ./toptimizer 0 0 4 50 -1 "+s+" 40000 20 96    2 1 2. 0.01 2 0.3 2.3 1 1 0 0 0 m 24 # > out.T"+str(target)+".i"+str(i)
+		print("\n\n **** CALLING APP: "+args+"\n\n\n")
+	        os.system(args);
+#		print("\n\n **** CALLING APP: ./optimizer 0 0 4 50 -1 "+s+" 40000 20 10 2 1 2. 0.01 100 0.3 2.3 1 1 0 0 0\n\n\n")
+#		FAST: os.system("./optimizer 0 0 4 50 -1 "+s+" 40000 20 10 2 1 2. 0.01 2 0.3 2.3 1 1 0 0 0")
+
+
+print "Done!"

Copied: SwiftApps/SciColSim/old/t3.py (from rev 5634, SwiftApps/SciColSim/t3.py)
===================================================================
--- SwiftApps/SciColSim/old/t3.py	                        (rev 0)
+++ SwiftApps/SciColSim/old/t3.py	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,22 @@
+#! /usr/bin/env python
+import os
+
+# FULL for target in range(58,1009,50):
+# FAST for target in range(58,209,50):
+for target in range(58,59,50):
+	s = ("%d" % target)
+	print s
+
+# FULL	for i in range(15):
+# FAST	for i in range(2):
+	for i in range(1):
+		args="./toptimizer 0 0 4 50 -1 "+s+" 40000 20 1000 2 1 2. 0.01 100 0.3 2.3 1 1 0 0 0 # > out.T"+str(target)+".i"+str(i)
+		args="OMP_NUM_THREADS=24 ./toptimizer 0 0 4 50 -1 "+s+" 40000 20 100 2 1 2. 0.01 2 0.3 2.3 1 1 0 0 0 # > out.T"+str(target)+".i"+str(i)
+		args="./toptimizer 0 0 4 50 -1 "+s+" 40000 20 75    2 1 2. 0.01 2 0.3 2.3 1 1 1 0 0 a # > out.T"+str(target)+".i"+str(i)
+		print("\n\n **** CALLING APP: "+args+"\n\n\n")
+	        os.system(args);
+#		print("\n\n **** CALLING APP: ./optimizer 0 0 4 50 -1 "+s+" 40000 20 10 2 1 2. 0.01 100 0.3 2.3 1 1 0 0 0\n\n\n")
+#		FAST: os.system("./optimizer 0 0 4 50 -1 "+s+" 40000 20 10 2 1 2. 0.01 2 0.3 2.3 1 1 0 0 0")
+
+
+print "Done!"

Copied: SwiftApps/SciColSim/old/tc (from rev 5634, SwiftApps/SciColSim/tc)
===================================================================
--- SwiftApps/SciColSim/old/tc	                        (rev 0)
+++ SwiftApps/SciColSim/old/tc	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,15 @@
+localhost sh /bin/sh null null null
+localhost cat /bin/cat null null null
+
+pbs cat /bin/cat null null null
+mcs cat /bin/cat null null null
+localhost catnap /home/wilde/swift/lab/catnap.sh null null GLOBUS::maxwalltime="00:01:00"
+
+beagle optimizer /home/wilde/AndreysOptimizer/src/Optimizer null null null
+beagle optimizersh /home/wilde/AndreysOptimizer/src/optimizer.sh null null null
+
+beagle evolve /home/wilde/AndreysOptimizer/src/evolve.sh null null null
+localhost evolve /home/wilde/AndreysOptimizer/src/evolve.sh null null GLOBUS::maxwalltime="02:00:00"
+
+beagle sumloss /home/wilde/AndreysOptimizer/src/sumloss.sh null null null
+localhost sumloss /home/wilde/AndreysOptimizer/src/sumloss.sh null null GLOBUS::maxwalltime="02:00:00"

Copied: SwiftApps/SciColSim/old/test-orig.sh (from rev 5634, SwiftApps/SciColSim/test-orig.sh)
===================================================================
--- SwiftApps/SciColSim/old/test-orig.sh	                        (rev 0)
+++ SwiftApps/SciColSim/old/test-orig.sh	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,21 @@
+#! /bin/bash
+
+python <<END
+import os
+
+target = 58
+s = ("%d" % target)
+
+args="0 0 4 50 -1 "+s+" 40000 20 1000 2 1 2. 0.01 100 0.3 2.3 1 1 0 0 0 "
+args="0 0 4 50 -1 "+s+" 40000 20  100 2 1 2. 0.01   2 0.3 2.3 1 1 0 0 0 "
+
+print("\n\n **** CALLING APP: "+args+"\n\n\n")
+os.system("./optimizer "+args+" >out.o.T"+s);
+os.system("./Optimizer "+args+" >out.O.T"+s);
+
+#		print("\n\n **** CALLING APP: ./optimizer 0 0 4 50 -1 "+s+" 40000 20 10 2 1 2. 0.01 100 0.3 2.3 1 1 0 0 0\n\n\n")
+#		FAST: os.system("./optimizer 0 0 4 50 -1 "+s+" 40000 20 10 2 1 2. 0.01 2 0.3 2.3 1 1 0 0 0")
+
+print "Done!"
+END
+

Copied: SwiftApps/SciColSim/old/test-swift.sh (from rev 5634, SwiftApps/SciColSim/test-swift.sh)
===================================================================
--- SwiftApps/SciColSim/old/test-swift.sh	                        (rev 0)
+++ SwiftApps/SciColSim/old/test-swift.sh	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,7 @@
+escapecode=$(echo -n -e '\033')
+
+swift -config cf -tc.file tc -sites.file local.xml annealing.swift \
+  -e33="$escapecode" 
+
+#  -evolveReruns=20  \ 
+#  -annealingCycles=10


Property changes on: SwiftApps/SciColSim/optimizer.cpp
___________________________________________________________________
Deleted: svn:executable
   - *

Deleted: SwiftApps/SciColSim/optimizer.orig-mac.cpp
===================================================================
--- SwiftApps/SciColSim/optimizer.orig-mac.cpp	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/optimizer.orig-mac.cpp	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,1673 +0,0 @@
-//
-//  main.cpp
-//  optimizer
-//
-//  Created by Andrey Rzhetsky on 4/11/11.
-//  Copyright 2011 University of Chicago. All rights reserved.
-//
-
-#define Nworkers 2
-
-#include <fstream>
-#include <iostream>
-#include <stdio.h>
-#include <time.h>
-#include <ctime>    
-#include <algorithm>
-#include <string>
-
-#include <stdio.h>
-#include <sys/param.h>
-#include <sys/time.h>
-#include <sys/types.h>
-
-#include <dispatch/dispatch.h>
-#include <fstream>
-
-
-#include <stdlib.h>
-#include <boost/numeric/ublas/io.hpp>
-#include <boost/graph/graph_traits.hpp>
-#include <boost/graph/dijkstra_shortest_paths.hpp>
-#include <boost/graph/loop_erased_random_walk.hpp>
-#include <boost/graph/random.hpp>
-#include <boost/property_map/property_map.hpp>
-#include <boost/graph/graph_concepts.hpp>
-#include <boost/graph/properties.hpp>
-
-#include <boost/graph/graph_traits.hpp>
-#include <boost/graph/adjacency_list.hpp>
-#include <boost/graph/adjacency_matrix.hpp>
-
-#define BOOST_MATH_OVERFLOW_ERROR_POLICY ignore_error
-#define BOOST_MATH_DISCRETE_QUANTILE_POLICY real
-#include <boost/graph/random.hpp>
-#include <boost/random/geometric_distribution.hpp>
-#include <boost/random/uniform_01.hpp>
-#include <boost/random.hpp>
-#include <boost/random/linear_congruential.hpp>
-#include <boost/random/uniform_int.hpp>
-#include <boost/random/uniform_real.hpp>
-#include <boost/random/variate_generator.hpp>
-#include <boost/generator_iterator.hpp>
-#include <boost/lexical_cast.hpp>
-
-#define INT_INFINITY 2147483647
-
-#define FIX_VARIABLES 1
-
-using namespace boost;
-using namespace std;
-using namespace boost::numeric::ublas;
-
-static int max_dist=0;
-
-typedef boost::adjacency_matrix<boost::directedS> Graph;
-typedef std::pair<int,int> Edge;
-typedef boost::graph_traits<Graph> GraphTraits;
-typedef boost::numeric::ublas::triangular_matrix<double, boost::numeric::ublas::strict_upper> prob;
-typedef boost::numeric::ublas::triangular_matrix<double, boost::numeric::ublas::strict_upper> pathlength;
-typedef boost::graph_traits<Graph>::vertex_descriptor vertex_descriptor;
-
-namespace std {
-	using ::time;
-}
-
-static int var_fixed[5] = {1, 0, 1, 1, 0};
-
-typedef boost::minstd_rand base_generator_type;
-typedef adjacency_list < listS, vecS, directedS,
-no_property, property < edge_weight_t, int > > graph_t;
-typedef graph_traits < graph_t >::vertex_descriptor vertex_descriptor;
-typedef graph_traits < graph_t >::edge_descriptor edge_descriptor;
-
-
-//================================================
-string strDouble(double number)
-{
-    stringstream ss;//create a stringstream
-    ss << number;//add number to the stream
-    return ss.str();//return a string with the contents of the stream
-}
-
-//================================================
-
-double gaussian(double sigma)
-{
-    double GaussNum = 0.0;
-    int NumInSum = 10;
-    for(int i = 0; i < NumInSum; i++)
-    {
-        GaussNum += ((double)rand()/(double)RAND_MAX - 0.5);
-    }
-    GaussNum = GaussNum*sqrt((double)12/(double)NumInSum);
-    
-    
-    return GaussNum;
-    
-}
-
-
-
-//=================================================
-double diffclock(clock_t clock1,clock_t clock2)
-{
-	double diffticks=clock1-clock2;
-	double diffms=(diffticks)/CLOCKS_PER_SEC;
-	return diffms;
-}
-
-//================================================
-//================================================================
-double get_new_x(double x, double dx){
-    
-    double new_x;
-    // boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
-    double r = rand()/(double)(pow(2.,31)-1.);
-    
-    if (r > 0.5){            
-        new_x = x + rand()*dx/(double)(pow(2.,31)-1.);
-    } else {            
-        new_x = x - rand()*dx/(double)(pow(2.,31)-1.);
-    }
-    
-    return new_x;
-    
-}
-
-
-//===============================================   
-string string_wrap(string ins, int mode){
-    
-    std::ostringstream s;
-    
-    switch(mode){
-        case 0:
-            s << "\033[1;29m" << ins << "\033[0m";
-            break;
-        case 1:
-            s << "\033[1;34m" << ins << "\033[0m";
-            break;
-        case 2:
-            s << "\033[1;44m" << ins << "\033[0m";
-            break;
-        case 3:
-            s << "\033[1;35m" << ins << "\033[0m";
-            break;
-        case 4:
-            s << "\033[1;33;44m" << ins << "\033[0m";
-            break;
-        case 5:
-            s << "\033[1;47;34m" << ins << "\033[0m";
-            break;
-        case 6:
-            s << "\033[1;1;31m" << ins << "\033[0m";
-            break;
-        case 7:
-            s << "\033[1;1;33m" << ins << "\033[0m";
-            break;
-        case 8:
-            s << "\033[1;1;43;34m" << ins << "\033[0m";
-            break;
-        case 9:
-            s << "\033[1;1;37m" << ins << "\033[0m";
-            break;
-        case 10:
-            s << "\033[1;30;47m" << ins << "\033[0m";
-            break;
-        default:
-            s << ins;
-    }
-    
-    return s.str();
-}
-
-
-//===============================================
-string wrap_double(double val, int mode){
-    
-    std::ostringstream s;
-    s << string_wrap(strDouble(val),mode);
-    
-    return s.str();
-}
-
-
-
-//===============================================
-const     
-string i2string(int i){
-    
-    std::ostringstream s;
-    s << "worker" 
-    << lexical_cast<std::string>(i);
-    
-    return s.str();
-    
-}
-
-//===============================================
-char* i2char(int i){
-    
-    std::ostringstream s;
-    s << "worker" 
-    << lexical_cast<std::string>(i);
-    
-    char* a=new char[s.str().size()+1];
-    memcpy(a,s.str().c_str(), s.str().size());
-    
-    return a;
-}
-
-//================================================
-class Universe {
-	
-private:
-	
-	double alpha_i;
-	double alpha_m;
-	double beta;
-	double gamma;
-	double delta;
-	
-    double TargetNovelty;
-    double CumulativeRelativeLoss;
-    double CRLsquare;
-    string id;
-    
-    
-	int N_nodes;
-	int M_edges;
-	
-	int N_epochs;
-	int N_steps;
-	int N_repeats;
-	
-	int current_epoch;
-	double current_loss;
-	int current_repeat;
-    double current_novelty;
-	
-	int mode_identify_failed;
-    int verbose_level; // 0 is silent, higher is more
-	
-	double k_max;
-	
-	graph_t Full_g;
-	
-	double **Prob;
-	double **Tried;
-	double **Dist;
-	double **Final;
-    double **EdgeIndex;
-	double *Rank;
-	
-    base_generator_type generator;	
-    boost::uniform_real<> uni_dist;
-    boost::geometric_distribution<double> geo;
-    
-public:
-	
-    
-    
-	//======  Constructor ======
-	Universe(const std::string FileToOpen, int Epochs, int Steps, int Repeats, int identify_failed, double target, const std::string idd)
-	{
-		//typedef array_type2::index index2;
-		
-		
-		std::ifstream inFile;
-        //string line;
-        
-        //-------------------------------
-        
-        base_generator_type gene(42u);
-        generator = gene;
-        generator.seed(static_cast<unsigned int>(std::time(0)));
-        boost::uniform_real<> uni_d(0,1);
-        uni_dist = uni_d;
-        
-        //--------------------------------
-        
-		int i, k;
-        int x, y;
-		Edge* edge_array_mine;
-		int num_arcs_mine, num_nodes_mine;
-		int* weights_mine;
-		
-        TargetNovelty = target;
-        CumulativeRelativeLoss = 0.;
-        CRLsquare = 0.;
-        
-		
-		N_epochs  = Epochs;
-		N_steps   = Steps;
-		N_repeats = Repeats;
-		
-		current_epoch = 0;
-		current_loss = 0.;
-		current_repeat = 0;
-        
-        id = idd;
-        
-        verbose_level = 1;
-		
-		mode_identify_failed = identify_failed;
-        
-		
-		//-------------------------------
-		// The first pass though file with the graph
-		inFile.open(FileToOpen.c_str());
-		if (inFile.fail()) {
-			cout << "Unable to open file";
-			exit(1); // terminate with error
-		}else {
-            
-            if (verbose_level > 2){
-                std::cout <<  " Opened <" << FileToOpen << ">"<<std::endl;
-            }
-        }
-		
-		i=0;
-        std::string line;
-		//while (! inFile.eof() && ! inFile.fail()) {
-        while (1==1) {
-            
-            inFile >> x;
-            inFile >> y;
-            
-            if (verbose_level > 2){
-                std::cout << " x: " << x;
-                std::cout << " y: " << y << std::endl;
-            }
-            
-			if (i==0){
-				N_nodes=x;
-				M_edges=y;	
-                break;
-			}
-			i++;
-            
-			
-		}
-		inFile.close();
-        
-        if (verbose_level == 2){
-            std::cout << N_nodes <<  " nodes, " << M_edges << " edges"<<std::endl;
-        }
-		
-		// k_max is the longest distance possible
-		
-        //k_max = M_edges;
-		k_max = 70;
-        
-		//------------------------------------
-		// Get memory allocated for all class members
-		
-		Prob = allocate_2Dmatrix(N_nodes, N_nodes);
-		Tried = allocate_2Dmatrix(N_nodes, N_nodes);
-		Dist = allocate_2Dmatrix(N_nodes, N_nodes);
-		Final = allocate_2Dmatrix(N_nodes, N_nodes);
-        EdgeIndex = allocate_2Dmatrix(N_nodes, N_nodes);
-		Rank = allocate_1Dmatrix(N_nodes);
-		
-        //The second pass through file with the graph
-        
-		for(int i = 0; i < N_nodes; ++i) {
-			Rank[i]=0.;
-			for(int j = 0; j < N_nodes; ++j) {
-				Final[i][j] = 0.;
-				Prob[i][j]=0.;
-				Dist[i][j]=-1.;
-				Tried[i][j]=0.;
-                EdgeIndex[i][j]=-1;
-			}
-		}
-        
-		
-		// Fill in the final graph -- and we are ready to go!
-        
-	    inFile.open(FileToOpen.c_str());
-		if (!inFile) {
-            std::cout << "Unable to open file";
-			exit(1); // terminate with error
-		}
-		else {
-            
-            if (verbose_level > 2){
-                std::cout <<  " Opened <" << FileToOpen << ">"<<std::endl;
-            }
-        }
-        
-		i=0;  
-		while (inFile >> x && inFile >>y) {
-			if (i > 0) {
-				Final[x][y]=1.;
-				Final[y][x]=1.;
-                
-                
-                if (verbose_level == 2){
-                    std::cout << ".";
-                }
-			}
-			i++;
-			
-		}
-        if (verbose_level == 2){
-            std::cout << std::endl;
-        }
-		inFile.close(); 
-		
-        k=0;
-        for (int i=0; i<N_nodes-1; i++){
-            for (int j=i+1;j<N_nodes; j++){
-                if(Final[i][j] > 0.){
-                    EdgeIndex[i][j]=k;
-                    k++;
-                }
-            }
-        }
-        
-        
-		
-		//+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
-		// create graph -- hopefully, we can keep it, just modifying edge weights
-		
-		
-		edge_array_mine = new Edge[2*M_edges];
-		num_arcs_mine = 2*M_edges;
-		num_nodes_mine = N_nodes;
-		weights_mine = new int[2*M_edges];
-		for (int i=0; i<2*M_edges; i++){ weights_mine[i]=1;}
-		
-		k=0;
-		for(int i=0; i<N_nodes-1; i++){
-			for( int j=i+1; j<N_nodes; j++){
-				if (Final[i][j]>0.){
-					edge_array_mine[2*k]  =Edge(i,j);
-					edge_array_mine[2*k+1]=Edge(j,i);
-					k++;
-				}
-			}
-		}
-		graph_t g(edge_array_mine, edge_array_mine + num_arcs_mine, weights_mine, num_nodes_mine);
-		
-		Full_g = g;
-		delete edge_array_mine;
-		delete weights_mine;
-		
-		//===========================================================================
-		std::vector<edge_descriptor> p(num_edges(Full_g));
-		std::vector<int> d(num_edges(Full_g));
-		edge_descriptor s;
-		boost::graph_traits<graph_t>::vertex_descriptor u, v;
-		
-		for (int i=0; i<N_nodes-1; i++){
-			for (int j=i+1; j<N_nodes; j++){
-				if (Final[i][j] > 0.){
-					u = vertex(i, Full_g);
-					v = vertex(j, Full_g);
-					remove_edge(u,v,Full_g);
-					remove_edge(v,u,Full_g);
-					
-				}
-			}
-		}
-		
-        
-    }
-	
-	
-	//=====================================================================
-	int sample_failed_number(double pfail){
-		
-		//boost::geometric_distribution<double> geo(pfail);
-		//boost::variate_generator<base_generator_type&, geometric_distribution<double> > geom(generator, geo);
-		
-		double r, u, g;
-        
-        r=0.;
-		for(int i=0; i<N_steps; i++){
-            
-            u=(double)rand();
-            u = 1.-u /(double)(pow(2.,31)-1.);
-            g=(int)(ceil(log(u) / log(pfail)));
-            
-			//r += geom();
-            
-            r+=g;
-		}
-        
-        if (verbose_level>=3){
-            std::cout << id << " failed " << r << std::endl;
-		}
-		return r;
-		
-	}
-    
-    //=============================================
-    double get_target(void){
-        return TargetNovelty;
-    }
-	
-    //=============================================
-    void set_target(double target){
-        TargetNovelty=target;
-    }
-	
-	//=============================================
-	int sample(){
-		
-        //boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
-        // double r = uni(), Summa = 0.;
-        
-        
-        
-        double r = rand(), Summa = 0.;
-        r /= (double)(pow(2.,31)-1.);
-		int result = 0;
-		int finished = 0;
-        
-        if (verbose_level==4){
-            std::cout << id << " sampled " << r << std::endl;
-        }
-		
-		for(int i=0; i<N_nodes-1 && finished==0; i++){			
-			for( int j=i+1; j<N_nodes && finished==0; j++){
-				
-				Summa += Prob[i][j];
-				
-				if (Summa > r){
-					
-					Tried[i][j]+=1.;
-					
-					if (Final[i][j] > 0.){
-						result = 1;
-					}
-					finished = 1;										
-				}
-			}
-		}
-		
-		return result;
-		
-	}
-	
-	//===============================
-	void update_current_graph(void){
-		
-		std::vector<edge_descriptor> p(num_edges(Full_g));
-		std::vector<int> d(num_edges(Full_g));
-		edge_descriptor s;
-		boost::graph_traits<graph_t>::vertex_descriptor u, v;
-		
-		//property_map<graph_t, edge_weight_t>::type weightmap = get(edge_weight, Full_g);
-		for (int i=0; i<N_nodes-1; i++){
-			for (int j=i+1; j<N_nodes; j++){
-				if (Final[i][j] > 0. && Tried[i][j]>0){
-					//s = edge(i, j, Full_g);	
-					boost::graph_traits<graph_t>::edge_descriptor e1,e2;
-					bool found1, found2;
-					u = vertex(i, Full_g);
-					v = vertex(j, Full_g);
-					tie(e1, found1) = edge(u, v, Full_g);
-					tie(e2, found2) = edge(v, u, Full_g);
-					if (!found1 && !found2){
-						add_edge(u,v,1,Full_g);
-					    add_edge(v,u,1,Full_g);
-					}
-					
-				}
-			}
-			
-		}
-	}
-	
-	//===============================
-	void update_distances(void){
-		// put shortest paths to the *Dist[][]
-		std::vector<vertex_descriptor> p(num_vertices(Full_g));
-		std::vector<int> d(num_vertices(Full_g));
-		vertex_descriptor s;
-		
-		
-		// put shortest paths to the *Dist[][]
-		for (int j=0; j<num_vertices(Full_g); j++){
-			
-			if(Rank[j] > 0.){
-				s = vertex(j, Full_g);	 
-				dijkstra_shortest_paths(Full_g, s, predecessor_map(&p[0]).distance_map(&d[0]));
-				
-				//std::cout <<" Vertex "<< j << std::endl;
-				graph_traits < graph_t >::vertex_iterator vi, vend;
-				
-				for (boost::tie(vi, vend) = vertices(Full_g); vi != vend; ++vi) {
-					
-					if (p[*vi]!=*vi){
-						Dist[*vi][j]=d[*vi];
-						Dist[j][*vi]=d[*vi];
-                        
-                        if (Dist[*vi][j]>max_dist){
-                            max_dist=Dist[*vi][j];
-                        }
-                        
-                        
-					} else {
-						Dist[*vi][j]=-1.;
-						Dist[j][*vi]=-1.;
-					}
-				}
-			}
-			
-		}
-		
-		
-	}
-	
-	//======================================================
-	void update_ranks(void){
-		
-		for(int i=0; i<N_nodes; i++){
-			Rank[i]=0.;
-		}
-		
-		for(int i=0; i<N_nodes-1; i++){
-			for( int j=i+1; j<N_nodes; j++){
-				if (Tried[i][j]>0. && Final[i][j] >0.){
-					Rank[i]++;
-					Rank[j]++;
-				}
-			}
-		}
-		
-	}
-	
-	//====================================================================
-	void set_world(double a_i, double a_m, double b, double g, double d){
-		
-		alpha_i=a_i;
-		alpha_m=a_m;
-		gamma=g;
-		beta=b;
-		delta=d;
-		
-	}
-	
-	//====================================================================
-	void reset_world(){
-		
-        //====================================================
-		std::vector<edge_descriptor> p(num_edges(Full_g));
-		std::vector<int> d(num_edges(Full_g));
-		edge_descriptor s;
-		boost::graph_traits<graph_t>::vertex_descriptor u, v;
-        
-		
-		for (int i=0; i<N_nodes-1; i++){
-			for (int j=i+1; j<N_nodes; j++){
-				if (Final[i][j] > 0. && Tried[i][j] > 0){
-					u = vertex(i, Full_g);
-					v = vertex(j, Full_g);
-					remove_edge(u,v,Full_g);
-					remove_edge(v,u,Full_g);
-					
-				}
-			}
-		}
-        
-        //==================================================
-        
-		current_loss=0;
-		current_epoch=0;
-		current_repeat++;
-        current_novelty=0;
-		
-		for(int i = 0; i < N_nodes; ++i) {
-			Rank[i]=0.;
-			for(int j = 0; j < N_nodes; ++j) {
-				Prob[i][j]=0.;
-				Dist[i][j]=-1.;
-				Tried[i][j]=0.;
-			}
-		}
-	}
-	
-	
-    //==============================================
-    void show_parameters(void){
-        
-        std::cout << "Parameters: " 
-        << alpha_i << " "
-        << alpha_m << " | "
-        << beta << " "
-        << gamma << " | "
-        << delta << std::endl;
-        
-    }
-    
-    
-    
-    //===============================================
-    string file_name(){
-        
-        std::ostringstream s;
-        s << "world_" 
-        << lexical_cast<std::string>(alpha_i) << "_" 
-        << lexical_cast<std::string>(alpha_m) << "_"
-        << lexical_cast<std::string>(beta) << "_"
-        << lexical_cast<std::string>(gamma) << "_"
-        << lexical_cast<std::string>(delta) << "_"
-        << lexical_cast<std::string>(N_epochs) << "_"
-        << lexical_cast<std::string>(N_steps) << "_"
-        << lexical_cast<std::string>(N_repeats) << ".txt";
-        
-        return s.str();
-        
-    }
-    
-    
-    
-    
-    //=================================================
-    void set_verbose(int verbose){
-        
-        verbose_level = verbose;
-    }
-    
-    
-    //=============================================================
-    void update_probabilities(void){
-        
-        
-        //=========================
-		// Compute sampling probabilities
-		// first pass: \xi_i,j
-		for(int i=0; i<N_nodes-1; i++){
-			for( int j=i+1; j<N_nodes; j++){
-				
-				double bg = 0.;
-				
-				Prob[i][j] = alpha_i*log(min(Rank[i]+1.,Rank[j]+1.)) + 
-                alpha_m*log(max(Rank[i]+1.,Rank[j]+1.));
-				
-                if (Dist[i][j] > 0.){
-                    
-                    double k = Dist[i][j];
-                    if (k >= k_max){
-                        k = k_max-1;
-                    }
-					
-                    bg = beta * log(k/k_max) + gamma * log(1. - k/k_max);
-					
-                } else {
-                    bg = delta;
-                }
-				
-				Prob[i][j] = exp(Prob[i][j] + bg);
-			}
-		}
-        
-		
-		// second pass: sum
-		double Summa = 0.;
-		
-		for(int i=0; i<N_nodes-1; i++){
-			for( int j=i+1; j<N_nodes; j++){
-				Summa += Prob[i][j];
-			}
-		}
-		
-		// third pass: normalize
-		for(int i=0; i<N_nodes-1; i++){
-			for( int j=i+1; j<N_nodes; j++){
-				Prob[i][j] /= Summa;
-			}
-		}
-        
-    }
-    
-	// Now we are ready for simulations
-	//==============================================
-	void update_world(){
-		
-		int failed = 0;
-        
-		// Given current universe compute shortest paths
-		//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-		
-		update_current_graph();
-		update_ranks();				
-		update_distances();
-		update_probabilities();
-		
-		//===============================
-		// sampling
-		int result;
-		double cost=0., novel=0.;
-		int publishable = 0;
-		
-		
-		//^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-		if (mode_identify_failed == 1){
-			
-			while(publishable < N_steps){
-				
-		    	result = sample();
-			    publishable += result;
-			    failed += (1-result);
-				
-			}
-			
-			for(int i=0; i<N_nodes-1; i++){
-				for( int j=i+1; j<N_nodes; j++){
-					
-					cost+=Tried[i][j];
-					
-					if (Tried[i][j]>0. && Final[i][j]>0.){
-						novel+=1.;
-					}
-				}
-			}
-			
-		}
-		//^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-		else {
-			
-			double pfail=0.;
-			int n_failed;
-			//, n_check = 0;
-			
-			for(int i=0; i<N_nodes-1; i++){
-				for( int j=i+1; j<N_nodes; j++){
-					if (Final[i][j] == 0.){
-						pfail += Prob[i][j];
-						Prob[i][j] = 0.;
-					}
-					
-				}
-			}
-			
-			for(int i=0; i<N_nodes-1; i++){
-				for( int j=i+1; j<N_nodes; j++){
-					Prob[i][j] /= (1.-pfail);
-				}
-				//std::cout << std::endl;
-			}			
-			
-			n_failed = sample_failed_number(pfail);
-			while(publishable < N_steps){
-				
-		    	result = sample();
-			    publishable += result;					
-			}
-            
-            
-			current_loss += (n_failed + N_steps);
-			cost = current_loss;
-			
-			for(int i=0; i<N_nodes-1; i++){
-				for( int j=i+1; j<N_nodes; j++){
-					
-					if (Tried[i][j]>0. && Final[i][j]>0.){
-						novel+=1.;
-					}
-				}
-			}
-		}
-        
-        current_novelty = novel;
-        
-        
-		//^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-		if (verbose_level == 2){
-            std::cout << (current_repeat+1) << "  epoch=" << (current_epoch+1) 
-            
-		    << "  cost=" << cost 
-		    << " novel=" << novel 
-		    << " rel_loss=" << cost/novel
-		    << std::endl;
-        }
-        
-		current_epoch++;
-	}
-	
-	
-	//======  Destructor ======
-	~Universe(){
-		
-		delete_2Dmatrix(Final, N_nodes);
-		delete_2Dmatrix(Dist, N_nodes);
-		delete_2Dmatrix(Tried, N_nodes);
-		delete_2Dmatrix(Prob, N_nodes);
-        delete_2Dmatrix(EdgeIndex, N_nodes);
-		delete_1Dmatrix(Rank);		
-	}
-    
-    //================================================
-    // Allocate memory
-    double** allocate_2Dmatrix(int N, int M)
-    {
-        double **pointer;
-        
-        if (verbose_level == 2){
-            std::cout<< "["<<N<<"|"<<M<<"]"<<std::endl;
-        }
-        pointer = new double*[N];
-        for (int i = 0; i < N; ++i)
-            pointer[i] = new double[M];	
-        
-        return pointer;
-    }
-    //===================
-    double* allocate_1Dmatrix(int N)
-    {
-        double *pointer;
-        
-        if(N > 0){
-            
-            pointer = new double[N];
-            
-        }else {
-            
-            pointer = NULL;
-        }
-        
-        return pointer;
-        
-    }
-    
-    //==============================================
-    // De-Allocate memory to prevent memory leak
-    void delete_2Dmatrix(double **pointer, int N){
-        
-        if (pointer != NULL){
-            
-            for (int i = 0; i < N; ++i){
-                delete [] pointer[i];
-            }
-            delete [] pointer;
-        }
-    }
-    //====================
-    void delete_1Dmatrix(double *pointer){
-        
-        delete [] pointer;
-    }
-    
-    //===========================================
-    double get_rel_loss(){
-        
-        return CumulativeRelativeLoss ;
-    }
-    
-    //===========================================
-    double get_rel_loss_err(){
-        
-        return CRLsquare ;
-    }
-    
-    
-    
-    //==================================================================================
-    void evolve_to_target_and_save(int istart, int iend, double* storage, int* counters){
-        
-        double ALOT=100000000000.;
-        
-        reset_world();
-        
-        for (int k = istart; k < iend; k++){
-            
-            
-            for(int i=0; i< N_epochs &&  current_novelty < TargetNovelty; i++){
-                update_world();
-            }
-            
-            storage[k]=current_loss/current_novelty;
-            counters[k]=1;
-            
-            
-            reset_world();
-        }
-        
-    }
-    //==============================================
-    int get_reruns(void){
-        return N_repeats;
-    }
-    
-    //==============================================
-    double get_parameter(int i){
-        
-        switch(i){
-            case 0:
-                return alpha_i;
-            case 1:
-                return alpha_m;
-            case 2:
-                return beta;
-            case 3:
-                return gamma;
-            case 4:
-                return delta;
-            default:
-                
-                std::cout << "Erroneous parameter id!!!!\n\n\n";
-                return 0.;
-        }
-    }
-    
-    
-    //==============================================
-    void evolve_to_target(){
-        
-        reset_world();
-        if (beta < -1. || gamma < -1.){
-            CumulativeRelativeLoss = 100000000000.;
-            CRLsquare = 0.;
-            return;
-        }
-        
-        
-        for (int k=0; k< N_repeats; k++){
-            
-            
-            for(int i=0; i<N_epochs &&  current_novelty < TargetNovelty; i++){
-                update_world();
-            }
-            
-            CumulativeRelativeLoss += current_loss/current_novelty;
-            CRLsquare += (current_loss/current_novelty)*(current_loss/current_novelty);
-            if(verbose_level==3){
-                std::cout <<  CumulativeRelativeLoss << " | " << CRLsquare << std::endl;
-            }
-            
-            if(verbose_level==1){
-                std::cout <<  "." ;
-                
-            }
-            else if(verbose_level==2){
-                std::cout <<  "**" << (k+1) <<  "**  curr loss " << current_loss << "; curr novelty " << current_novelty << std::endl;
-            }
-            
-            
-            reset_world();
-        }
-        
-        CumulativeRelativeLoss /= double(N_repeats);
-        CRLsquare /= double(N_repeats);
-        
-        if(verbose_level==1){
-            std::cout << std::endl;
-        }
-        
-        if(verbose_level==2){
-            std::cout <<  CumulativeRelativeLoss << " || " << CRLsquare << std::endl;
-        }
-        
-        CRLsquare = 2*sqrt((CRLsquare - CumulativeRelativeLoss*CumulativeRelativeLoss)/double(N_repeats));
-        
-    }
-    
-    
-    //================================================================    
-    int set_parameter(double value, int position){
-        
-        if (position < 0 || position > 4) {return 0;}
-        
-        else {
-            
-            switch(position){
-                case 0:
-                    alpha_i=value;
-                    return 1;
-                case 1:
-                    alpha_m=value;
-                    return 1;
-                case 2:
-                    beta=value;
-                    return 1;
-                case 3:
-                    gamma=value;
-                    return 1;
-                case 4:
-                    delta=value;
-                    return 1;
-            }
-            
-        }
-        
-        return 0;
-    }
-    
-    
-    //=================================================================
-    void try_annealing(double starting_jump, int iterations, 
-                       double temp_start, double temp_end, double target_rejection){
-        
-        double dx[5]={0.,0.,0.,0.,0};
-        double x[5]={0.,0.,0.,0.,0};
-        double rejection[5]={0., 0., 0., 0., 0.};
-        double curr_x, curr_err, x_tmp;
-        double temperature;
-        double ratio, r;
-        int cycle=10;
-        boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
-        
-        // set up parameter for annealing
-        
-        x[0]=alpha_i;
-        x[1]=alpha_m;
-        x[2]=beta;
-        x[3]=gamma;
-        x[4]=delta;
-        
-        for(int i=0;i<5;i++){
-            dx[i] = starting_jump;
-        }
-        
-        // establish the current value
-        
-        //..........................................
-        evolve_to_target();        
-        std::cout << CumulativeRelativeLoss << " +- " << CRLsquare << std::endl;
-        
-        curr_x   = CumulativeRelativeLoss;
-        curr_err = CRLsquare;
-        CumulativeRelativeLoss = 0;
-        CRLsquare = 0;
-        //...........................................
-        
-        // optimization cycle
-        for(int i=0; i<iterations; i++){
-            
-            temperature = temp_start*exp( i*(log(temp_end)-log(temp_start))/(double)iterations);
-            std::cout  << std::endl << "....T = " << wrap_double(temperature,3) << std::endl << std::endl;
-            
-            if (i % cycle == 0 && i > 0){
-                
-                for (int k=0; k<5; k++){
-                    
-                    rejection[k]/=(double)cycle;
-                    if (rejection[k] > 0){
-                        dx[k] = dx[k]/(rejection[k]/target_rejection);
-                        rejection[k]=0.;
-                    }
-                    else{
-                        dx[k]*=2.;
-                    }
-                    std::cout  << dx[k] << " ";
-                }
-                std::cout  << std::endl;
-            }
-            
-            
-            for (int j=0; j<5; j++){
-                
-                // get new value of x[j]
-                x_tmp = get_new_x(x[j],dx[j]);
-                
-                
-                
-                //.............................................
-                set_parameter(x_tmp, j);
-                
-                
-                evolve_to_target(); 
-                
-                std::cout  << std::endl << "......... " << std::endl;
-                std::cout << "Trying... " << CumulativeRelativeLoss << " +- " << CRLsquare << std::endl;
-                
-                ratio = min(1.,exp(-(CumulativeRelativeLoss-curr_x)/temperature));
-                r = uni();
-                std::cout << r << " vs " << ratio << std::endl;
-                
-                if (r > ratio){
-                    
-                    std::cout << string_wrap(id, 4) <<" "<< (i+1) << ","<< (j) 
-                    <<" "<< (i+1) << " Did not accept " 
-                    << x_tmp << "(" << j << ")" << std::endl;
-                    std::cout << alpha_i << " "<< alpha_m << " "
-                    << beta << " " << gamma << " " 
-                    << delta << " " << std::endl;
-                    set_parameter(x[j], j);
-                    CumulativeRelativeLoss = 0;
-                    CRLsquare = 0;
-                    
-                    rejection[j]+=1.;
-                }
-                
-                else {
-                    
-                    curr_x   = CumulativeRelativeLoss;
-                    curr_err = CRLsquare;
-                    x[j] = x_tmp;
-                    CumulativeRelativeLoss = 0;
-                    CRLsquare = 0;
-                    std::cout << (i+1) << string_wrap((string) " Rejection counts: ", 8) 
-                    << wrap_double(rejection[0],2) 
-                    << " "<< wrap_double(rejection[1], 7) << " "
-                    << wrap_double(rejection[2],5) << " " << wrap_double(rejection[2],9) << " " 
-                    << wrap_double(rejection[4],6) << " " 
-                    << std::endl << std::endl;
-                    
-                    std::cout << string_wrap(id, 4) <<" "<< (i+1) <<","<< (j) 
-                    <<" "
-                    << string_wrap((string) "***** Did accept! ", 3) 
-                    << wrap_double(alpha_i,2) 
-                    << " "<< wrap_double(alpha_m, 7) << " "
-                    << wrap_double(beta,5) << " " 
-                    << wrap_double(gamma,9) << " " 
-                    << wrap_double(delta,6) << " " 
-                    << std::endl << std::endl;
-                    
-                }
-                //........................................................ 
-                
-            }
-            
-        }
-        
-    }
-    
-	
-};
-
-//============================================================
-
-std::pair<double,double> multi_loss(dispatch_group_t group, 
-                                    Universe* un[], 
-                                    dispatch_queue_t* CustomQueues,
-                                    double* Results,
-                                    int*    Counters,
-                                    double* params){
-    
-    int N = un[0]->get_reruns();
-    int step = (int)(double)N/(double)(Nworkers);
-    int istart=0;
-    int iend = istart+step;
-    
-    double Loss=0., LossSquare=0.;
-    
-    for(int i=0; i<Nworkers; i++){
-        for(int j=0; j<5; j++){
-            un[i]->set_parameter(params[j],j);
-        }
-    }
-        
-       
-    for(int i=0; i<Nworkers; i++){
-        
-        dispatch_group_async(group, CustomQueues[i], ^{
-                
-            un[i]->evolve_to_target_and_save(istart, iend, Results, Counters);
-        });
-            
-        std::cout << "queued: i=" << i << " N=" << N << " istart=" << istart << " iend=" << iend << "\n";
-        istart += step;
-        iend = min(istart+step,N);
-            
-    }
-    dispatch_group_wait(group, DISPATCH_TIME_FOREVER);
-    //dispatch_release(group);
-    
-    for (int i=0; i<N; i++){
-        
-        Loss+=Results[i]/(double)N;
-        LossSquare+=Results[i]*Results[i]/(double)N;
-        
-        // std::cout<<" " << Results[i];
-    }
-    
-    //std::cout<<" \n\n\n";
-    double two_std = ((LossSquare - Loss*Loss)/(double)N);
-    
-    two_std = 2.*sqrt(two_std);
-    std::pair<double,double> Res;
-    Res.first=Loss;
-    Res.second=two_std;
-    
-    return Res;
-    
-    
-}
-//============================================================
-
-
-//============================================================
-void multi_annealing( dispatch_group_t group, 
-                     Universe* un[], 
-                     dispatch_queue_t* CustomQueues, 
-                     double T_start, double T_end, 
-                     double Target_rejection, 
-                     int Annealing_repeats, 
-                     double starting_jump,
-                     double* Results,
-                     int*    Counters,
-                     double* params0,
-                     double annealing_cycles){
-    //.................................
-    // re-implement annealing
-    
-    double dx[5]={0.,0.,0.,0.,0};
-    double x[5]={0.,0.,0.,0.,0};
-    double rejection[5]={0., 0., 0., 0., 0.};
-    double curr_x, curr_err, x_tmp;
-    double temperature;
-    double ratio, r;
-    int cycle=10;
-    //boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
-    
-    // set up parameter for annealing
-    
-    x[0]=params0[0];
-    x[1]=params0[1];
-    x[2]=params0[2];
-    x[3]=params0[3];
-    x[4]=params0[4];
-    
-    for(int i=0;i<5;i++){
-        dx[i] = starting_jump;
-    }
-    
-    // establish the current value
-    std::pair<double,double>Res;
-    
-    Res = multi_loss(group, un, CustomQueues, Results, Counters, x);
-    std::cout << Res.first << " +- " << Res.second << std::endl;
-    
-    curr_x   = Res.first;
-    curr_err = Res.second;
-    
-    // optimization cycle
-    
-    for(int i=0; i<annealing_cycles; i++){
-        
-        temperature = T_start*exp( i*(log(T_end)-log(T_start))/(double)annealing_cycles);
-        std::cout  << std::endl << "....T = " << wrap_double(temperature,3) << std::endl << std::endl;
-        
-        if (i % cycle == 0 && i > 0){
-            
-            for (int k=0; k<5; k++){
-                rejection[k]/=(double)cycle;
-                
-                if (rejection[k] > 0){
-                    dx[k] = dx[k]/(rejection[k]/Target_rejection);
-                    rejection[k]=0.;
-                }
-                else{
-                    dx[k]*=2.;
-                }
-                std::cout  << dx[k] << " ";
-            }
-            std::cout  << std::endl;
-        }
-        
-        
-        for (int j=0; j<5; j++){
-            
-            ///////////////////////////////
-            if (FIX_VARIABLES==0 || var_fixed[j]==0){
-                
-                
-                
-                // get new value of x[j]
-                double x_hold=x[j];
-                x_tmp = get_new_x(x[j],dx[j]);
-                x[j]=x_tmp;
-                
-                std::cout << wrap_double(x_tmp,10) << " " << wrap_double(j,9) << "\n\n"; 
-                //=======================================
-                //.............................................
-                for(int w=0; w<Nworkers; w++){
-                    un[w]->set_parameter(x_tmp, j);
-                }
-                
-                
-                Res = multi_loss(group, un, CustomQueues, Results, Counters, x);
-                std::cout << Res.first << " +- " << Res.second << std::endl;
-                
-                ratio = min(1.,exp(-(Res.first-curr_x)/temperature));
-                r = rand()/(double)(pow(2.,31)-1.);
-                std::cout << r << " vs " << ratio << std::endl;
-                
-                double ALOT=100000000000.;
-                
-                if (Res.first < ALOT)
-                {
-                    ofstream filestr;
-                    
-                    filestr.open ("best_opt_some.txt", ofstream::app);
-                    
-                    // >> i/o operations here <<
-                    filestr << un[0]->get_target() << "," 
-                    << Res.first 
-                    << "," << un[0]->get_parameter(0) 
-                    << "," << un[0]->get_parameter(1) 
-                    << "," << un[0]->get_parameter(2) 
-                    << "," << un[0]->get_parameter(3) 
-                    << "," << un[0]->get_parameter(4) << "," << Res.second << ",\n";
-                    
-                    filestr.close();
-                    
-                    
-                    filestr.open ("max_dist.txt", ofstream::app);
-                    
-                    // >> i/o operations here <<
-                    filestr << max_dist << ",\n";
-                    
-                    filestr.close();
-                    
-                }
-                
-                
-                if (r > ratio){
-                    
-                    std::cout << " "<< (i+1) << ","<< (j) 
-                    <<" "<< (i+1) << " Did not accept " 
-                    << x_tmp << "(" << j << ")" << std::endl;
-                    std::cout << un[0]->get_parameter(0) 
-                    << " " << un[0]->get_parameter(1) 
-                    << " " << un[0]->get_parameter(2) 
-                    << " " << un[0]->get_parameter(3) 
-                    << " " << un[0]->get_parameter(4) << " " << std::endl;
-                    
-                    x[j]=x_hold;
-                    for(int w=0; w<Nworkers; w++){
-                        un[w]->set_parameter(x[j], j);
-                    }
-                    
-                    
-                    //set_parameter(x[j], j);     
-                    rejection[j]+=1.;
-                }
-                
-                else {
-                    
-                    curr_x   = Res.first;
-                    curr_err = Res.second;
-                    x[j] = x_tmp;
-                    
-                    for(int w=0; w<Nworkers; w++){
-                        un[w]->set_parameter(x[j], j);
-                    }
-                    
-                    std::cout << (i+1) << string_wrap((string) " Rejection counts: ", 8) 
-                    << wrap_double(rejection[0],2) << " " 
-                    << wrap_double(rejection[1],7) << " "
-                    << wrap_double(rejection[2],5) << " " 
-                    << wrap_double(rejection[3],9) << " " 
-                    << wrap_double(rejection[4],6) << " " 
-                    << std::endl << std::endl;
-                    
-                    std::cout << " "<< (i+1) <<","<< (j) 
-                    <<" "
-                    << string_wrap((string) "***** Did accept! ", 3) 
-                    << wrap_double(un[0]->get_parameter(0),2) << " "
-                    << wrap_double(un[0]->get_parameter(1),7) << " "
-                    << wrap_double(un[0]->get_parameter(2),5) << " " 
-                    << wrap_double(un[0]->get_parameter(3),9) << " " 
-                    << wrap_double(un[0]->get_parameter(4),6) << " " 
-                    << std::endl << std::endl;
-                    
-                    
-                    
-                }
-                //........................................................ 
-                
-            }
-        }
-        
-    }
-    
-}
-
-
-
-//================================================
-int
-main(int argc, char* argv[])
-{
-    
-    double params0[6] = {0., 0., 0., 0., 0., 0.2}, target=50., range;
-    string par_names0[6] = {"alpha_i", "alpha_m", "beta", "gamma", "delta", "target"};
-    string par_names1[4] = {"n_epochs", "n_steps", "n_reruns", "range"};
-    string par_names2[5] = {"T_start", "T_end", "Annealing_steps","Target_rejection","Starting_jump"};
-    string par_names3[5] = {"FREEZE_alpha_i", "FREEZE_alpha_m", "FREEZE_beta", "FREEZE_gamma", "FREEZE_delta"};    
-    int params1[4] = {300, 50, 1000, 10};
-    int params3[5] = { 0, 0, 0, 0, 0};
-    
-    //          temperature_start,  temperature_end,  annealing_steps target_rejection  Starting_jump
-    double params2[5] = {1,             0.001,               100,              0.3,           1.5};
-    
-    int verbose_level = 2;
-    const std::string one="one", two="two";
-    static Universe* un[Nworkers];
-    static dispatch_queue_t CustomQueues[Nworkers];
-    
-    static double* Results;
-    static int*    Counters;
-    
-    timeval t1, t2;
-    double elapsedTime;
-    // start timer
-    gettimeofday(&t1, NULL);
-    
-    
-    if (argc < 8) {
-        std::cout << "Usage: super_optimizer alpha_i alpha_m beta gamma delta target_innov [n_epochs n_steps n_reruns] [range] [verbose_level]\n"; 
-        std::cout << "         [T_start T_end Annealing_steps Target_rejection Starting_jump]\n"; 
-        std::cout << "         [FREEZE_alpha_i FREEZE_alpha_m FREEZE_beta FREEZE_gamma FREEZE_delta]\n"; 
-        
-        system("pwd");
-        
-        
-        return(1);
-    }
-    else {
-        for (int nArg=0; nArg < argc; nArg++){
-            //std::cout << nArg << " " << argv[nArg] << std::endl;
-            if (nArg > 0 && nArg < 7){
-                params0[nArg-1]= atof(argv[nArg]);
-                std::cout << par_names0[nArg-1] << ": " << params0[nArg-1] <<  std::endl;
-            }
-            if (nArg > 6 && nArg < 11){
-                params1[nArg-7]= atoi(argv[nArg]);
-                std::cout << par_names1[nArg-7] << ": " << params1[nArg-7] <<  std::endl;
-            }
-            if (nArg == 11){
-                verbose_level = atoi(argv[nArg]);
-                std::cout << "verbose level: " << verbose_level <<  std::endl;
-            }
-            if (nArg > 11 && nArg < 17){
-                params2[nArg-12]= atof(argv[nArg]);
-                std::cout << par_names2[nArg-12] << ": " << params2[nArg-12] <<  std::endl;
-            }
-            if (nArg > 16 && nArg < 22){
-                params3[nArg-17]= atof(argv[nArg]);
-                var_fixed[nArg-17]= atof(argv[nArg]);
-                std::cout << par_names3[nArg-17] << ": " << var_fixed[nArg-17] <<  std::endl;
-            }
-            
-            
-        }
-        
-    }
-    
-    for (int j=0; j<5; j++){
-        
-        cout << j << " | " << var_fixed[j] << " (fixed) \n";
-    }
-	
-    target=params0[5];
-    range = (double)params1[3];
-	int identify_failed = 0;
-	char* filename= (char *)"movie_graph.txt";
- 	int n_ep=params1[0], n_st=params1[1], n_rep=params1[2];
-    
-    //...............................
-    
-    for(int i=0; i<Nworkers; i++){
-        un[i] = new Universe((char *)filename,n_ep,n_st,
-                             (int)n_rep,
-                             identify_failed, target, i2string(i));
-        CustomQueues[i] = dispatch_queue_create(i2char(i), NULL);
-    }
-    
-    //...............................
-    if(n_rep > 0){
-        
-        Results = new double[n_rep];
-        Counters = new int[n_rep];
-        
-    }else {
-        
-        Results =  NULL;
-        Counters = NULL;
-        std::cout << " Number of reruns should be positive! " <<  std::endl;
-        return 0;
-        
-    }
-    //...............................
-    srand(time(0));
-    //srandomdev();
-    
-    {
-        double r=0;
-        for (int j=0; j<100; j++){
-            
-            
-            
-            r = rand()/(double)(pow(2.,31)-1.);
-            std::cout << r << " ";
-        }
-        std::cout << "\n ";
-    }
-  	//random initiation of starting parameters
-    
-    if (range > 0.){
-        
-        for (int i=0; i < 5; i++){
-            
-            if (params0[i]==-100.){
-                
-                double r1 = (rand()/(double)(pow(2.,31)-1.));
-                double r2 = (rand()/(double)(pow(2.,31)-1.));
-                double sign = 1.;
-                
-                if(r1 > 0.5){
-                    sign=-1.;
-                }
-                
-                params0[i] = sign*r2*range;
-                
-                std::cout << par_names0[i] << ": " << params0[i] <<  std::endl;
-            }
-        }
-        
-    }
-    
-    
-    double T_start=params2[0], T_end=params2[1], Target_rejection=params2[3], starting_jump=params2[4];
-    int Annealing_repeats = (int) params2[2];
-    
-    
-    dispatch_group_t group = dispatch_group_create();
-    
-    //.............................
-    multi_annealing(group, un, CustomQueues, T_start, T_end, Target_rejection, Annealing_repeats, 
-                    starting_jump, Results, Counters, params0, Annealing_repeats);
-    
-    //dispatch_group_wait(group, DISPATCH_TIME_FOREVER);
-    dispatch_release(group);
-    //.............................
-    
-    
-    // stop timer
-    gettimeofday(&t2, NULL);
-    
-    // compute and print the elapsed time in millisec
-    elapsedTime = (t2.tv_sec - t1.tv_sec) * 1000.0;      // sec to ms
-    elapsedTime += (t2.tv_usec - t1.tv_usec) / 1000.0;   // us to ms
-    elapsedTime /= 1000.;
-    cout << elapsedTime << " seconds \n .....(" << elapsedTime/60. << " minutes)\n\n";
-    
-    //.....................
-    
-    for(int i=0; i<Nworkers; i++){
-        delete un[i];
-    }
-    
-    //....................
-    if(n_rep > 0){
-        
-        delete [] Results;
-        delete [] Counters;
-        
-    }
-    
-    return 0;
-	
-	
-	
-}
-

Copied: SwiftApps/SciColSim/optimizer.orig-mac.cpp (from rev 5634, SwiftApps/SciColSim/optimizer.orig-mac.cpp)
===================================================================
--- SwiftApps/SciColSim/optimizer.orig-mac.cpp	                        (rev 0)
+++ SwiftApps/SciColSim/optimizer.orig-mac.cpp	2012-02-16 20:59:48 UTC (rev 5639)
@@ -0,0 +1,1673 @@
+//
+//  main.cpp
+//  optimizer
+//
+//  Created by Andrey Rzhetsky on 4/11/11.
+//  Copyright 2011 University of Chicago. All rights reserved.
+//
+
+#define Nworkers 2
+
+#include <fstream>
+#include <iostream>
+#include <stdio.h>
+#include <time.h>
+#include <ctime>    
+#include <algorithm>
+#include <string>
+
+#include <stdio.h>
+#include <sys/param.h>
+#include <sys/time.h>
+#include <sys/types.h>
+
+#include <dispatch/dispatch.h>
+#include <fstream>
+
+
+#include <stdlib.h>
+#include <boost/numeric/ublas/io.hpp>
+#include <boost/graph/graph_traits.hpp>
+#include <boost/graph/dijkstra_shortest_paths.hpp>
+#include <boost/graph/loop_erased_random_walk.hpp>
+#include <boost/graph/random.hpp>
+#include <boost/property_map/property_map.hpp>
+#include <boost/graph/graph_concepts.hpp>
+#include <boost/graph/properties.hpp>
+
+#include <boost/graph/graph_traits.hpp>
+#include <boost/graph/adjacency_list.hpp>
+#include <boost/graph/adjacency_matrix.hpp>
+
+#define BOOST_MATH_OVERFLOW_ERROR_POLICY ignore_error
+#define BOOST_MATH_DISCRETE_QUANTILE_POLICY real
+#include <boost/graph/random.hpp>
+#include <boost/random/geometric_distribution.hpp>
+#include <boost/random/uniform_01.hpp>
+#include <boost/random.hpp>
+#include <boost/random/linear_congruential.hpp>
+#include <boost/random/uniform_int.hpp>
+#include <boost/random/uniform_real.hpp>
+#include <boost/random/variate_generator.hpp>
+#include <boost/generator_iterator.hpp>
+#include <boost/lexical_cast.hpp>
+
+#define INT_INFINITY 2147483647
+
+#define FIX_VARIABLES 1
+
+using namespace boost;
+using namespace std;
+using namespace boost::numeric::ublas;
+
+static int max_dist=0;
+
+typedef boost::adjacency_matrix<boost::directedS> Graph;
+typedef std::pair<int,int> Edge;
+typedef boost::graph_traits<Graph> GraphTraits;
+typedef boost::numeric::ublas::triangular_matrix<double, boost::numeric::ublas::strict_upper> prob;
+typedef boost::numeric::ublas::triangular_matrix<double, boost::numeric::ublas::strict_upper> pathlength;
+typedef boost::graph_traits<Graph>::vertex_descriptor vertex_descriptor;
+
+namespace std {
+	using ::time;
+}
+
+static int var_fixed[5] = {1, 0, 1, 1, 0};
+
+typedef boost::minstd_rand base_generator_type;
+typedef adjacency_list < listS, vecS, directedS,
+no_property, property < edge_weight_t, int > > graph_t;
+typedef graph_traits < graph_t >::vertex_descriptor vertex_descriptor;
+typedef graph_traits < graph_t >::edge_descriptor edge_descriptor;
+
+
+//================================================
+string strDouble(double number)
+{
+    stringstream ss;//create a stringstream
+    ss << number;//add number to the stream
+    return ss.str();//return a string with the contents of the stream
+}
+
+//================================================
+
+double gaussian(double sigma)
+{
+    double GaussNum = 0.0;
+    int NumInSum = 10;
+    for(int i = 0; i < NumInSum; i++)
+    {
+        GaussNum += ((double)rand()/(double)RAND_MAX - 0.5);
+    }
+    GaussNum = GaussNum*sqrt((double)12/(double)NumInSum);
+    
+    
+    return GaussNum;
+    
+}
+
+
+
+//=================================================
+double diffclock(clock_t clock1,clock_t clock2)
+{
+	double diffticks=clock1-clock2;
+	double diffms=(diffticks)/CLOCKS_PER_SEC;
+	return diffms;
+}
+
+//================================================
+//================================================================
+double get_new_x(double x, double dx){
+    
+    double new_x;
+    // boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
+    double r = rand()/(double)(pow(2.,31)-1.);
+    
+    if (r > 0.5){            
+        new_x = x + rand()*dx/(double)(pow(2.,31)-1.);
+    } else {            
+        new_x = x - rand()*dx/(double)(pow(2.,31)-1.);
+    }
+    
+    return new_x;
+    
+}
+
+
+//===============================================   
+string string_wrap(string ins, int mode){
+    
+    std::ostringstream s;
+    
+    switch(mode){
+        case 0:
+            s << "\033[1;29m" << ins << "\033[0m";
+            break;
+        case 1:
+            s << "\033[1;34m" << ins << "\033[0m";
+            break;
+        case 2:
+            s << "\033[1;44m" << ins << "\033[0m";
+            break;
+        case 3:
+            s << "\033[1;35m" << ins << "\033[0m";
+            break;
+        case 4:
+            s << "\033[1;33;44m" << ins << "\033[0m";
+            break;
+        case 5:
+            s << "\033[1;47;34m" << ins << "\033[0m";
+            break;
+        case 6:
+            s << "\033[1;1;31m" << ins << "\033[0m";
+            break;
+        case 7:
+            s << "\033[1;1;33m" << ins << "\033[0m";
+            break;
+        case 8:
+            s << "\033[1;1;43;34m" << ins << "\033[0m";
+            break;
+        case 9:
+            s << "\033[1;1;37m" << ins << "\033[0m";
+            break;
+        case 10:
+            s << "\033[1;30;47m" << ins << "\033[0m";
+            break;
+        default:
+            s << ins;
+    }
+    
+    return s.str();
+}
+
+
+//===============================================
+string wrap_double(double val, int mode){
+    
+    std::ostringstream s;
+    s << string_wrap(strDouble(val),mode);
+    
+    return s.str();
+}
+
+
+
+//===============================================
+const     
+string i2string(int i){
+    
+    std::ostringstream s;
+    s << "worker" 
+    << lexical_cast<std::string>(i);
+    
+    return s.str();
+    
+}
+
+//===============================================
+char* i2char(int i){
+    
+    std::ostringstream s;
+    s << "worker" 
+    << lexical_cast<std::string>(i);
+    
+    char* a=new char[s.str().size()+1];
+    memcpy(a,s.str().c_str(), s.str().size());
+    
+    return a;
+}
+
+//================================================
+class Universe {
+	
+private:
+	
+	double alpha_i;
+	double alpha_m;
+	double beta;
+	double gamma;
+	double delta;
+	
+    double TargetNovelty;
+    double CumulativeRelativeLoss;
+    double CRLsquare;
+    string id;
+    
+    
+	int N_nodes;
+	int M_edges;
+	
+	int N_epochs;
+	int N_steps;
+	int N_repeats;
+	
+	int current_epoch;
+	double current_loss;
+	int current_repeat;
+    double current_novelty;
+	
+	int mode_identify_failed;
+    int verbose_level; // 0 is silent, higher is more
+	
+	double k_max;
+	
+	graph_t Full_g;
+	
+	double **Prob;
+	double **Tried;
+	double **Dist;
+	double **Final;
+    double **EdgeIndex;
+	double *Rank;
+	
+    base_generator_type generator;	
+    boost::uniform_real<> uni_dist;
+    boost::geometric_distribution<double> geo;
+    
+public:
+	
+    
+    
+	//======  Constructor ======
+	Universe(const std::string FileToOpen, int Epochs, int Steps, int Repeats, int identify_failed, double target, const std::string idd)
+	{
+		//typedef array_type2::index index2;
+		
+		
+		std::ifstream inFile;
+        //string line;
+        
+        //-------------------------------
+        
+        base_generator_type gene(42u);
+        generator = gene;
+        generator.seed(static_cast<unsigned int>(std::time(0)));
+        boost::uniform_real<> uni_d(0,1);
+        uni_dist = uni_d;
+        
+        //--------------------------------
+        
+		int i, k;
+        int x, y;
+		Edge* edge_array_mine;
+		int num_arcs_mine, num_nodes_mine;
+		int* weights_mine;
+		
+        TargetNovelty = target;
+        CumulativeRelativeLoss = 0.;
+        CRLsquare = 0.;
+        
+		
+		N_epochs  = Epochs;
+		N_steps   = Steps;
+		N_repeats = Repeats;
+		
+		current_epoch = 0;
+		current_loss = 0.;
+		current_repeat = 0;
+        
+        id = idd;
+        
+        verbose_level = 1;
+		
+		mode_identify_failed = identify_failed;
+        
+		
+		//-------------------------------
+		// The first pass though file with the graph
+		inFile.open(FileToOpen.c_str());
+		if (inFile.fail()) {
+			cout << "Unable to open file";
+			exit(1); // terminate with error
+		}else {
+            
+            if (verbose_level > 2){
+                std::cout <<  " Opened <" << FileToOpen << ">"<<std::endl;
+            }
+        }
+		
+		i=0;
+        std::string line;
+		//while (! inFile.eof() && ! inFile.fail()) {
+        while (1==1) {
+            
+            inFile >> x;
+            inFile >> y;
+            
+            if (verbose_level > 2){
+                std::cout << " x: " << x;
+                std::cout << " y: " << y << std::endl;
+            }
+            
+			if (i==0){
+				N_nodes=x;
+				M_edges=y;	
+                break;
+			}
+			i++;
+            
+			
+		}
+		inFile.close();
+        
+        if (verbose_level == 2){
+            std::cout << N_nodes <<  " nodes, " << M_edges << " edges"<<std::endl;
+        }
+		
+		// k_max is the longest distance possible
+		
+        //k_max = M_edges;
+		k_max = 70;
+        
+		//------------------------------------
+		// Get memory allocated for all class members
+		
+		Prob = allocate_2Dmatrix(N_nodes, N_nodes);
+		Tried = allocate_2Dmatrix(N_nodes, N_nodes);
+		Dist = allocate_2Dmatrix(N_nodes, N_nodes);
+		Final = allocate_2Dmatrix(N_nodes, N_nodes);
+        EdgeIndex = allocate_2Dmatrix(N_nodes, N_nodes);
+		Rank = allocate_1Dmatrix(N_nodes);
+		
+        //The second pass through file with the graph
+        
+		for(int i = 0; i < N_nodes; ++i) {
+			Rank[i]=0.;
+			for(int j = 0; j < N_nodes; ++j) {
+				Final[i][j] = 0.;
+				Prob[i][j]=0.;
+				Dist[i][j]=-1.;
+				Tried[i][j]=0.;
+                EdgeIndex[i][j]=-1;
+			}
+		}
+        
+		
+		// Fill in the final graph -- and we are ready to go!
+        
+	    inFile.open(FileToOpen.c_str());
+		if (!inFile) {
+            std::cout << "Unable to open file";
+			exit(1); // terminate with error
+		}
+		else {
+            
+            if (verbose_level > 2){
+                std::cout <<  " Opened <" << FileToOpen << ">"<<std::endl;
+            }
+        }
+        
+		i=0;  
+		while (inFile >> x && inFile >>y) {
+			if (i > 0) {
+				Final[x][y]=1.;
+				Final[y][x]=1.;
+                
+                
+                if (verbose_level == 2){
+                    std::cout << ".";
+                }
+			}
+			i++;
+			
+		}
+        if (verbose_level == 2){
+            std::cout << std::endl;
+        }
+		inFile.close(); 
+		
+        k=0;
+        for (int i=0; i<N_nodes-1; i++){
+            for (int j=i+1;j<N_nodes; j++){
+                if(Final[i][j] > 0.){
+                    EdgeIndex[i][j]=k;
+                    k++;
+                }
+            }
+        }
+        
+        
+		
+		//+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+		// create graph -- hopefully, we can keep it, just modifying edge weights
+		
+		
+		edge_array_mine = new Edge[2*M_edges];
+		num_arcs_mine = 2*M_edges;
+		num_nodes_mine = N_nodes;
+		weights_mine = new int[2*M_edges];
+		for (int i=0; i<2*M_edges; i++){ weights_mine[i]=1;}
+		
+		k=0;
+		for(int i=0; i<N_nodes-1; i++){
+			for( int j=i+1; j<N_nodes; j++){
+				if (Final[i][j]>0.){
+					edge_array_mine[2*k]  =Edge(i,j);
+					edge_array_mine[2*k+1]=Edge(j,i);
+					k++;
+				}
+			}
+		}
+		graph_t g(edge_array_mine, edge_array_mine + num_arcs_mine, weights_mine, num_nodes_mine);
+		
+		Full_g = g;
+		delete edge_array_mine;
+		delete weights_mine;
+		
+		//===========================================================================
+		std::vector<edge_descriptor> p(num_edges(Full_g));
+		std::vector<int> d(num_edges(Full_g));
+		edge_descriptor s;
+		boost::graph_traits<graph_t>::vertex_descriptor u, v;
+		
+		for (int i=0; i<N_nodes-1; i++){
+			for (int j=i+1; j<N_nodes; j++){
+				if (Final[i][j] > 0.){
+					u = vertex(i, Full_g);
+					v = vertex(j, Full_g);
+					remove_edge(u,v,Full_g);
+					remove_edge(v,u,Full_g);
+					
+				}
+			}
+		}
+		
+        
+    }
+	
+	
+	//=====================================================================
+	int sample_failed_number(double pfail){
+		
+		//boost::geometric_distribution<double> geo(pfail);
+		//boost::variate_generator<base_generator_type&, geometric_distribution<double> > geom(generator, geo);
+		
+		double r, u, g;
+        
+        r=0.;
+		for(int i=0; i<N_steps; i++){
+            
+            u=(double)rand();
+            u = 1.-u /(double)(pow(2.,31)-1.);
+            g=(int)(ceil(log(u) / log(pfail)));
+            
+			//r += geom();
+            
+            r+=g;
+		}
+        
+        if (verbose_level>=3){
+            std::cout << id << " failed " << r << std::endl;
+		}
+		return r;
+		
+	}
+    
+    //=============================================
+    double get_target(void){
+        return TargetNovelty;
+    }
+	
+    //=============================================
+    void set_target(double target){
+        TargetNovelty=target;
+    }
+	
+	//=============================================
+	int sample(){
+		
+        //boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
+        // double r = uni(), Summa = 0.;
+        
+        
+        
+        double r = rand(), Summa = 0.;
+        r /= (double)(pow(2.,31)-1.);
+		int result = 0;
+		int finished = 0;
+        
+        if (verbose_level==4){
+            std::cout << id << " sampled " << r << std::endl;
+        }
+		
+		for(int i=0; i<N_nodes-1 && finished==0; i++){			
+			for( int j=i+1; j<N_nodes && finished==0; j++){
+				
+				Summa += Prob[i][j];
+				
+				if (Summa > r){
+					
+					Tried[i][j]+=1.;
+					
+					if (Final[i][j] > 0.){
+						result = 1;
+					}
+					finished = 1;										
+				}
+			}
+		}
+		
+		return result;
+		
+	}
+	
+	//===============================
+	void update_current_graph(void){
+		
+		std::vector<edge_descriptor> p(num_edges(Full_g));
+		std::vector<int> d(num_edges(Full_g));
+		edge_descriptor s;
+		boost::graph_traits<graph_t>::vertex_descriptor u, v;
+		
+		//property_map<graph_t, edge_weight_t>::type weightmap = get(edge_weight, Full_g);
+		for (int i=0; i<N_nodes-1; i++){
+			for (int j=i+1; j<N_nodes; j++){
+				if (Final[i][j] > 0. && Tried[i][j]>0){
+					//s = edge(i, j, Full_g);	
+					boost::graph_traits<graph_t>::edge_descriptor e1,e2;
+					bool found1, found2;
+					u = vertex(i, Full_g);
+					v = vertex(j, Full_g);
+					tie(e1, found1) = edge(u, v, Full_g);
+					tie(e2, found2) = edge(v, u, Full_g);
+					if (!found1 && !found2){
+						add_edge(u,v,1,Full_g);
+					    add_edge(v,u,1,Full_g);
+					}
+					
+				}
+			}
+			
+		}
+	}
+	
+	//===============================
+	void update_distances(void){
+		// put shortest paths to the *Dist[][]
+		std::vector<vertex_descriptor> p(num_vertices(Full_g));
+		std::vector<int> d(num_vertices(Full_g));
+		vertex_descriptor s;
+		
+		
+		// put shortest paths to the *Dist[][]
+		for (int j=0; j<num_vertices(Full_g); j++){
+			
+			if(Rank[j] > 0.){
+				s = vertex(j, Full_g);	 
+				dijkstra_shortest_paths(Full_g, s, predecessor_map(&p[0]).distance_map(&d[0]));
+				
+				//std::cout <<" Vertex "<< j << std::endl;
+				graph_traits < graph_t >::vertex_iterator vi, vend;
+				
+				for (boost::tie(vi, vend) = vertices(Full_g); vi != vend; ++vi) {
+					
+					if (p[*vi]!=*vi){
+						Dist[*vi][j]=d[*vi];
+						Dist[j][*vi]=d[*vi];
+                        
+                        if (Dist[*vi][j]>max_dist){
+                            max_dist=Dist[*vi][j];
+                        }
+                        
+                        
+					} else {
+						Dist[*vi][j]=-1.;
+						Dist[j][*vi]=-1.;
+					}
+				}
+			}
+			
+		}
+		
+		
+	}
+	
+	//======================================================
+	void update_ranks(void){
+		
+		for(int i=0; i<N_nodes; i++){
+			Rank[i]=0.;
+		}
+		
+		for(int i=0; i<N_nodes-1; i++){
+			for( int j=i+1; j<N_nodes; j++){
+				if (Tried[i][j]>0. && Final[i][j] >0.){
+					Rank[i]++;
+					Rank[j]++;
+				}
+			}
+		}
+		
+	}
+	
+	//====================================================================
+	void set_world(double a_i, double a_m, double b, double g, double d){
+		
+		alpha_i=a_i;
+		alpha_m=a_m;
+		gamma=g;
+		beta=b;
+		delta=d;
+		
+	}
+	
+	//====================================================================
+	void reset_world(){
+		
+        //====================================================
+		std::vector<edge_descriptor> p(num_edges(Full_g));
+		std::vector<int> d(num_edges(Full_g));
+		edge_descriptor s;
+		boost::graph_traits<graph_t>::vertex_descriptor u, v;
+        
+		
+		for (int i=0; i<N_nodes-1; i++){
+			for (int j=i+1; j<N_nodes; j++){
+				if (Final[i][j] > 0. && Tried[i][j] > 0){
+					u = vertex(i, Full_g);
+					v = vertex(j, Full_g);
+					remove_edge(u,v,Full_g);
+					remove_edge(v,u,Full_g);
+					
+				}
+			}
+		}
+        
+        //==================================================
+        
+		current_loss=0;
+		current_epoch=0;
+		current_repeat++;
+        current_novelty=0;
+		
+		for(int i = 0; i < N_nodes; ++i) {
+			Rank[i]=0.;
+			for(int j = 0; j < N_nodes; ++j) {
+				Prob[i][j]=0.;
+				Dist[i][j]=-1.;
+				Tried[i][j]=0.;
+			}
+		}
+	}
+	
+	
+    //==============================================
+    void show_parameters(void){
+        
+        std::cout << "Parameters: " 
+        << alpha_i << " "
+        << alpha_m << " | "
+        << beta << " "
+        << gamma << " | "
+        << delta << std::endl;
+        
+    }
+    
+    
+    
+    //===============================================
+    string file_name(){
+        
+        std::ostringstream s;
+        s << "world_" 
+        << lexical_cast<std::string>(alpha_i) << "_" 
+        << lexical_cast<std::string>(alpha_m) << "_"
+        << lexical_cast<std::string>(beta) << "_"
+        << lexical_cast<std::string>(gamma) << "_"
+        << lexical_cast<std::string>(delta) << "_"
+        << lexical_cast<std::string>(N_epochs) << "_"
+        << lexical_cast<std::string>(N_steps) << "_"
+        << lexical_cast<std::string>(N_repeats) << ".txt";
+        
+        return s.str();
+        
+    }
+    
+    
+    
+    
+    //=================================================
+    void set_verbose(int verbose){
+        
+        verbose_level = verbose;
+    }
+    
+    
+    //=============================================================
+    void update_probabilities(void){
+        
+        
+        //=========================
+		// Compute sampling probabilities
+		// first pass: \xi_i,j
+		for(int i=0; i<N_nodes-1; i++){
+			for( int j=i+1; j<N_nodes; j++){
+				
+				double bg = 0.;
+				
+				Prob[i][j] = alpha_i*log(min(Rank[i]+1.,Rank[j]+1.)) + 
+                alpha_m*log(max(Rank[i]+1.,Rank[j]+1.));
+				
+                if (Dist[i][j] > 0.){
+                    
+                    double k = Dist[i][j];
+                    if (k >= k_max){
+                        k = k_max-1;
+                    }
+					
+                    bg = beta * log(k/k_max) + gamma * log(1. - k/k_max);
+					
+                } else {
+                    bg = delta;
+                }
+				
+				Prob[i][j] = exp(Prob[i][j] + bg);
+			}
+		}
+        
+		
+		// second pass: sum
+		double Summa = 0.;
+		
+		for(int i=0; i<N_nodes-1; i++){
+			for( int j=i+1; j<N_nodes; j++){
+				Summa += Prob[i][j];
+			}
+		}
+		
+		// third pass: normalize
+		for(int i=0; i<N_nodes-1; i++){
+			for( int j=i+1; j<N_nodes; j++){
+				Prob[i][j] /= Summa;
+			}
+		}
+        
+    }
+    
+	// Now we are ready for simulations
+	//==============================================
+	void update_world(){
+		
+		int failed = 0;
+        
+		// Given current universe compute shortest paths
+		//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+		
+		update_current_graph();
+		update_ranks();				
+		update_distances();
+		update_probabilities();
+		
+		//===============================
+		// sampling
+		int result;
+		double cost=0., novel=0.;
+		int publishable = 0;
+		
+		
+		//^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+		if (mode_identify_failed == 1){
+			
+			while(publishable < N_steps){
+				
+		    	result = sample();
+			    publishable += result;
+			    failed += (1-result);
+				
+			}
+			
+			for(int i=0; i<N_nodes-1; i++){
+				for( int j=i+1; j<N_nodes; j++){
+					
+					cost+=Tried[i][j];
+					
+					if (Tried[i][j]>0. && Final[i][j]>0.){
+						novel+=1.;
+					}
+				}
+			}
+			
+		}
+		//^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+		else {
+			
+			double pfail=0.;
+			int n_failed;
+			//, n_check = 0;
+			
+			for(int i=0; i<N_nodes-1; i++){
+				for( int j=i+1; j<N_nodes; j++){
+					if (Final[i][j] == 0.){
+						pfail += Prob[i][j];
+						Prob[i][j] = 0.;
+					}
+					
+				}
+			}
+			
+			for(int i=0; i<N_nodes-1; i++){
+				for( int j=i+1; j<N_nodes; j++){
+					Prob[i][j] /= (1.-pfail);
+				}
+				//std::cout << std::endl;
+			}			
+			
+			n_failed = sample_failed_number(pfail);
+			while(publishable < N_steps){
+				
+		    	result = sample();
+			    publishable += result;					
+			}
+            
+            
+			current_loss += (n_failed + N_steps);
+			cost = current_loss;
+			
+			for(int i=0; i<N_nodes-1; i++){
+				for( int j=i+1; j<N_nodes; j++){
+					
+					if (Tried[i][j]>0. && Final[i][j]>0.){
+						novel+=1.;
+					}
+				}
+			}
+		}
+        
+        current_novelty = novel;
+        
+        
+		//^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+		if (verbose_level == 2){
+            std::cout << (current_repeat+1) << "  epoch=" << (current_epoch+1) 
+            
+		    << "  cost=" << cost 
+		    << " novel=" << novel 
+		    << " rel_loss=" << cost/novel
+		    << std::endl;
+        }
+        
+		current_epoch++;
+	}
+	
+	
+	//======  Destructor ======
+	~Universe(){
+		
+		delete_2Dmatrix(Final, N_nodes);
+		delete_2Dmatrix(Dist, N_nodes);
+		delete_2Dmatrix(Tried, N_nodes);
+		delete_2Dmatrix(Prob, N_nodes);
+        delete_2Dmatrix(EdgeIndex, N_nodes);
+		delete_1Dmatrix(Rank);		
+	}
+    
+    //================================================
+    // Allocate memory
+    double** allocate_2Dmatrix(int N, int M)
+    {
+        double **pointer;
+        
+        if (verbose_level == 2){
+            std::cout<< "["<<N<<"|"<<M<<"]"<<std::endl;
+        }
+        pointer = new double*[N];
+        for (int i = 0; i < N; ++i)
+            pointer[i] = new double[M];	
+        
+        return pointer;
+    }
+    //===================
+    double* allocate_1Dmatrix(int N)
+    {
+        double *pointer;
+        
+        if(N > 0){
+            
+            pointer = new double[N];
+            
+        }else {
+            
+            pointer = NULL;
+        }
+        
+        return pointer;
+        
+    }
+    
+    //==============================================
+    // De-Allocate memory to prevent memory leak
+    void delete_2Dmatrix(double **pointer, int N){
+        
+        if (pointer != NULL){
+            
+            for (int i = 0; i < N; ++i){
+                delete [] pointer[i];
+            }
+            delete [] pointer;
+        }
+    }
+    //====================
+    void delete_1Dmatrix(double *pointer){
+        
+        delete [] pointer;
+    }
+    
+    //===========================================
+    double get_rel_loss(){
+        
+        return CumulativeRelativeLoss ;
+    }
+    
+    //===========================================
+    double get_rel_loss_err(){
+        
+        return CRLsquare ;
+    }
+    
+    
+    
+    //==================================================================================
+    void evolve_to_target_and_save(int istart, int iend, double* storage, int* counters){
+        
+        double ALOT=100000000000.;
+        
+        reset_world();
+        
+        for (int k = istart; k < iend; k++){
+            
+            
+            for(int i=0; i< N_epochs &&  current_novelty < TargetNovelty; i++){
+                update_world();
+            }
+            
+            storage[k]=current_loss/current_novelty;
+            counters[k]=1;
+            
+            
+            reset_world();
+        }
+        
+    }
+    //==============================================
+    int get_reruns(void){
+        return N_repeats;
+    }
+    
+    //==============================================
+    double get_parameter(int i){
+        
+        switch(i){
+            case 0:
+                return alpha_i;
+            case 1:
+                return alpha_m;
+            case 2:
+                return beta;
+            case 3:
+                return gamma;
+            case 4:
+                return delta;
+            default:
+                
+                std::cout << "Erroneous parameter id!!!!\n\n\n";
+                return 0.;
+        }
+    }
+    
+    
+    //==============================================
+    void evolve_to_target(){
+        
+        reset_world();
+        if (beta < -1. || gamma < -1.){
+            CumulativeRelativeLoss = 100000000000.;
+            CRLsquare = 0.;
+            return;
+        }
+        
+        
+        for (int k=0; k< N_repeats; k++){
+            
+            
+            for(int i=0; i<N_epochs &&  current_novelty < TargetNovelty; i++){
+                update_world();
+            }
+            
+            CumulativeRelativeLoss += current_loss/current_novelty;
+            CRLsquare += (current_loss/current_novelty)*(current_loss/current_novelty);
+            if(verbose_level==3){
+                std::cout <<  CumulativeRelativeLoss << " | " << CRLsquare << std::endl;
+            }
+            
+            if(verbose_level==1){
+                std::cout <<  "." ;
+                
+            }
+            else if(verbose_level==2){
+                std::cout <<  "**" << (k+1) <<  "**  curr loss " << current_loss << "; curr novelty " << current_novelty << std::endl;
+            }
+            
+            
+            reset_world();
+        }
+        
+        CumulativeRelativeLoss /= double(N_repeats);
+        CRLsquare /= double(N_repeats);
+        
+        if(verbose_level==1){
+            std::cout << std::endl;
+        }
+        
+        if(verbose_level==2){
+            std::cout <<  CumulativeRelativeLoss << " || " << CRLsquare << std::endl;
+        }
+        
+        CRLsquare = 2*sqrt((CRLsquare - CumulativeRelativeLoss*CumulativeRelativeLoss)/double(N_repeats));
+        
+    }
+    
+    
+    //================================================================    
+    int set_parameter(double value, int position){
+        
+        if (position < 0 || position > 4) {return 0;}
+        
+        else {
+            
+            switch(position){
+                case 0:
+                    alpha_i=value;
+                    return 1;
+                case 1:
+                    alpha_m=value;
+                    return 1;
+                case 2:
+                    beta=value;
+                    return 1;
+                case 3:
+                    gamma=value;
+                    return 1;
+                case 4:
+                    delta=value;
+                    return 1;
+            }
+            
+        }
+        
+        return 0;
+    }
+    
+    
+    //=================================================================
+    void try_annealing(double starting_jump, int iterations, 
+                       double temp_start, double temp_end, double target_rejection){
+        
+        double dx[5]={0.,0.,0.,0.,0};
+        double x[5]={0.,0.,0.,0.,0};
+        double rejection[5]={0., 0., 0., 0., 0.};
+        double curr_x, curr_err, x_tmp;
+        double temperature;
+        double ratio, r;
+        int cycle=10;
+        boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
+        
+        // set up parameter for annealing
+        
+        x[0]=alpha_i;
+        x[1]=alpha_m;
+        x[2]=beta;
+        x[3]=gamma;
+        x[4]=delta;
+        
+        for(int i=0;i<5;i++){
+            dx[i] = starting_jump;
+        }
+        
+        // establish the current value
+        
+        //..........................................
+        evolve_to_target();        
+        std::cout << CumulativeRelativeLoss << " +- " << CRLsquare << std::endl;
+        
+        curr_x   = CumulativeRelativeLoss;
+        curr_err = CRLsquare;
+        CumulativeRelativeLoss = 0;
+        CRLsquare = 0;
+        //...........................................
+        
+        // optimization cycle
+        for(int i=0; i<iterations; i++){
+            
+            temperature = temp_start*exp( i*(log(temp_end)-log(temp_start))/(double)iterations);
+            std::cout  << std::endl << "....T = " << wrap_double(temperature,3) << std::endl << std::endl;
+            
+            if (i % cycle == 0 && i > 0){
+                
+                for (int k=0; k<5; k++){
+                    
+                    rejection[k]/=(double)cycle;
+                    if (rejection[k] > 0){
+                        dx[k] = dx[k]/(rejection[k]/target_rejection);
+                        rejection[k]=0.;
+                    }
+                    else{
+                        dx[k]*=2.;
+                    }
+                    std::cout  << dx[k] << " ";
+                }
+                std::cout  << std::endl;
+            }
+            
+            
+            for (int j=0; j<5; j++){
+                
+                // get new value of x[j]
+                x_tmp = get_new_x(x[j],dx[j]);
+                
+                
+                
+                //.............................................
+                set_parameter(x_tmp, j);
+                
+                
+                evolve_to_target(); 
+                
+                std::cout  << std::endl << "......... " << std::endl;
+                std::cout << "Trying... " << CumulativeRelativeLoss << " +- " << CRLsquare << std::endl;
+                
+                ratio = min(1.,exp(-(CumulativeRelativeLoss-curr_x)/temperature));
+                r = uni();
+                std::cout << r << " vs " << ratio << std::endl;
+                
+                if (r > ratio){
+                    
+                    std::cout << string_wrap(id, 4) <<" "<< (i+1) << ","<< (j) 
+                    <<" "<< (i+1) << " Did not accept " 
+                    << x_tmp << "(" << j << ")" << std::endl;
+                    std::cout << alpha_i << " "<< alpha_m << " "
+                    << beta << " " << gamma << " " 
+                    << delta << " " << std::endl;
+                    set_parameter(x[j], j);
+                    CumulativeRelativeLoss = 0;
+                    CRLsquare = 0;
+                    
+                    rejection[j]+=1.;
+                }
+                
+                else {
+                    
+                    curr_x   = CumulativeRelativeLoss;
+                    curr_err = CRLsquare;
+                    x[j] = x_tmp;
+                    CumulativeRelativeLoss = 0;
+                    CRLsquare = 0;
+                    std::cout << (i+1) << string_wrap((string) " Rejection counts: ", 8) 
+                    << wrap_double(rejection[0],2) 
+                    << " "<< wrap_double(rejection[1], 7) << " "
+                    << wrap_double(rejection[2],5) << " " << wrap_double(rejection[2],9) << " " 
+                    << wrap_double(rejection[4],6) << " " 
+                    << std::endl << std::endl;
+                    
+                    std::cout << string_wrap(id, 4) <<" "<< (i+1) <<","<< (j) 
+                    <<" "
+                    << string_wrap((string) "***** Did accept! ", 3) 
+                    << wrap_double(alpha_i,2) 
+                    << " "<< wrap_double(alpha_m, 7) << " "
+                    << wrap_double(beta,5) << " " 
+                    << wrap_double(gamma,9) << " " 
+                    << wrap_double(delta,6) << " " 
+                    << std::endl << std::endl;
+                    
+                }
+                //........................................................ 
+                
+            }
+            
+        }
+        
+    }
+    
+	
+};
+
+//============================================================
+
+std::pair<double,double> multi_loss(dispatch_group_t group, 
+                                    Universe* un[], 
+                                    dispatch_queue_t* CustomQueues,
+                                    double* Results,
+                                    int*    Counters,
+                                    double* params){
+    
+    int N = un[0]->get_reruns();
+    int step = (int)(double)N/(double)(Nworkers);
+    int istart=0;
+    int iend = istart+step;
+    
+    double Loss=0., LossSquare=0.;
+    
+    for(int i=0; i<Nworkers; i++){
+        for(int j=0; j<5; j++){
+            un[i]->set_parameter(params[j],j);
+        }
+    }
+        
+       
+    for(int i=0; i<Nworkers; i++){
+        
+        dispatch_group_async(group, CustomQueues[i], ^{
+                
+            un[i]->evolve_to_target_and_save(istart, iend, Results, Counters);
+        });
+            
+        std::cout << "queued: i=" << i << " N=" << N << " istart=" << istart << " iend=" << iend << "\n";
+        istart += step;
+        iend = min(istart+step,N);
+            
+    }
+    dispatch_group_wait(group, DISPATCH_TIME_FOREVER);
+    //dispatch_release(group);
+    
+    for (int i=0; i<N; i++){
+        
+        Loss+=Results[i]/(double)N;
+        LossSquare+=Results[i]*Results[i]/(double)N;
+        
+        // std::cout<<" " << Results[i];
+    }
+    
+    //std::cout<<" \n\n\n";
+    double two_std = ((LossSquare - Loss*Loss)/(double)N);
+    
+    two_std = 2.*sqrt(two_std);
+    std::pair<double,double> Res;
+    Res.first=Loss;
+    Res.second=two_std;
+    
+    return Res;
+    
+    
+}
+//============================================================
+
+
+//============================================================
+void multi_annealing( dispatch_group_t group, 
+                     Universe* un[], 
+                     dispatch_queue_t* CustomQueues, 
+                     double T_start, double T_end, 
+                     double Target_rejection, 
+                     int Annealing_repeats, 
+                     double starting_jump,
+                     double* Results,
+                     int*    Counters,
+                     double* params0,
+                     double annealing_cycles){
+    //.................................
+    // re-implement annealing
+    
+    double dx[5]={0.,0.,0.,0.,0};
+    double x[5]={0.,0.,0.,0.,0};
+    double rejection[5]={0., 0., 0., 0., 0.};
+    double curr_x, curr_err, x_tmp;
+    double temperature;
+    double ratio, r;
+    int cycle=10;
+    //boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
+    
+    // set up parameter for annealing
+    
+    x[0]=params0[0];
+    x[1]=params0[1];
+    x[2]=params0[2];
+    x[3]=params0[3];
+    x[4]=params0[4];
+    
+    for(int i=0;i<5;i++){
+        dx[i] = starting_jump;
+    }
+    
+    // establish the current value
+    std::pair<double,double>Res;
+    
+    Res = multi_loss(group, un, CustomQueues, Results, Counters, x);
+    std::cout << Res.first << " +- " << Res.second << std::endl;
+    
+    curr_x   = Res.first;
+    curr_err = Res.second;
+    
+    // optimization cycle
+    
+    for(int i=0; i<annealing_cycles; i++){
+        
+        temperature = T_start*exp( i*(log(T_end)-log(T_start))/(double)annealing_cycles);
+        std::cout  << std::endl << "....T = " << wrap_double(temperature,3) << std::endl << std::endl;
+        
+        if (i % cycle == 0 && i > 0){
+            
+            for (int k=0; k<5; k++){
+                rejection[k]/=(double)cycle;
+                
+                if (rejection[k] > 0){
+                    dx[k] = dx[k]/(rejection[k]/Target_rejection);
+                    rejection[k]=0.;
+                }
+                else{
+                    dx[k]*=2.;
+                }
+                std::cout  << dx[k] << " ";
+            }
+            std::cout  << std::endl;
+        }
+        
+        
+        for (int j=0; j<5; j++){
+            
+            ///////////////////////////////
+            if (FIX_VARIABLES==0 || var_fixed[j]==0){
+                
+                
+                
+                // get new value of x[j]
+                double x_hold=x[j];
+                x_tmp = get_new_x(x[j],dx[j]);
+                x[j]=x_tmp;
+                
+                std::cout << wrap_double(x_tmp,10) << " " << wrap_double(j,9) << "\n\n"; 
+                //=======================================
+                //.............................................
+                for(int w=0; w<Nworkers; w++){
+                    un[w]->set_parameter(x_tmp, j);
+                }
+                
+                
+                Res = multi_loss(group, un, CustomQueues, Results, Counters, x);
+                std::cout << Res.first << " +- " << Res.second << std::endl;
+                
+                ratio = min(1.,exp(-(Res.first-curr_x)/temperature));
+                r = rand()/(double)(pow(2.,31)-1.);
+                std::cout << r << " vs " << ratio << std::endl;
+                
+                double ALOT=100000000000.;
+                
+                if (Res.first < ALOT)
+                {
+                    ofstream filestr;
+                    
+                    filestr.open ("best_opt_some.txt", ofstream::app);
+                    
+                    // >> i/o operations here <<
+                    filestr << un[0]->get_target() << "," 
+                    << Res.first 
+                    << "," << un[0]->get_parameter(0) 
+                    << "," << un[0]->get_parameter(1) 
+                    << "," << un[0]->get_parameter(2) 
+                    << "," << un[0]->get_parameter(3) 
+                    << "," << un[0]->get_parameter(4) << "," << Res.second << ",\n";
+                    
+                    filestr.close();
+                    
+                    
+                    filestr.open ("max_dist.txt", ofstream::app);
+                    
+                    // >> i/o operations here <<
+                    filestr << max_dist << ",\n";
+                    
+                    filestr.close();
+                    
+                }
+                
+                
+                if (r > ratio){
+                    
+                    std::cout << " "<< (i+1) << ","<< (j) 
+                    <<" "<< (i+1) << " Did not accept " 
+                    << x_tmp << "(" << j << ")" << std::endl;
+                    std::cout << un[0]->get_parameter(0) 
+                    << " " << un[0]->get_parameter(1) 
+                    << " " << un[0]->get_parameter(2) 
+                    << " " << un[0]->get_parameter(3) 
+                    << " " << un[0]->get_parameter(4) << " " << std::endl;
+                    
+                    x[j]=x_hold;
+                    for(int w=0; w<Nworkers; w++){
+                        un[w]->set_parameter(x[j], j);
+                    }
+                    
+                    
+                    //set_parameter(x[j], j);     
+                    rejection[j]+=1.;
+                }
+                
+                else {
+                    
+                    curr_x   = Res.first;
+                    curr_err = Res.second;
+                    x[j] = x_tmp;
+                    
+                    for(int w=0; w<Nworkers; w++){
+                        un[w]->set_parameter(x[j], j);
+                    }
+                    
+                    std::cout << (i+1) << string_wrap((string) " Rejection counts: ", 8) 
+                    << wrap_double(rejection[0],2) << " " 
+                    << wrap_double(rejection[1],7) << " "
+                    << wrap_double(rejection[2],5) << " " 
+                    << wrap_double(rejection[3],9) << " " 
+                    << wrap_double(rejection[4],6) << " " 
+                    << std::endl << std::endl;
+                    
+                    std::cout << " "<< (i+1) <<","<< (j) 
+                    <<" "
+                    << string_wrap((string) "***** Did accept! ", 3) 
+                    << wrap_double(un[0]->get_parameter(0),2) << " "
+                    << wrap_double(un[0]->get_parameter(1),7) << " "
+                    << wrap_double(un[0]->get_parameter(2),5) << " " 
+                    << wrap_double(un[0]->get_parameter(3),9) << " " 
+                    << wrap_double(un[0]->get_parameter(4),6) << " " 
+                    << std::endl << std::endl;
+                    
+                    
+                    
+                }
+                //........................................................ 
+                
+            }
+        }
+        
+    }
+    
+}
+
+
+
+//================================================
+int
+main(int argc, char* argv[])
+{
+    
+    double params0[6] = {0., 0., 0., 0., 0., 0.2}, target=50., range;
+    string par_names0[6] = {"alpha_i", "alpha_m", "beta", "gamma", "delta", "target"};
+    string par_names1[4] = {"n_epochs", "n_steps", "n_reruns", "range"};
+    string par_names2[5] = {"T_start", "T_end", "Annealing_steps","Target_rejection","Starting_jump"};
+    string par_names3[5] = {"FREEZE_alpha_i", "FREEZE_alpha_m", "FREEZE_beta", "FREEZE_gamma", "FREEZE_delta"};    
+    int params1[4] = {300, 50, 1000, 10};
+    int params3[5] = { 0, 0, 0, 0, 0};
+    
+    //          temperature_start,  temperature_end,  annealing_steps target_rejection  Starting_jump
+    double params2[5] = {1,             0.001,               100,              0.3,           1.5};
+    
+    int verbose_level = 2;
+    const std::string one="one", two="two";
+    static Universe* un[Nworkers];
+    static dispatch_queue_t CustomQueues[Nworkers];
+    
+    static double* Results;
+    static int*    Counters;
+    
+    timeval t1, t2;
+    double elapsedTime;
+    // start timer
+    gettimeofday(&t1, NULL);
+    
+    
+    if (argc < 8) {
+        std::cout << "Usage: super_optimizer alpha_i alpha_m beta gamma delta target_innov [n_epochs n_steps n_reruns] [range] [verbose_level]\n"; 
+        std::cout << "         [T_start T_end Annealing_steps Target_rejection Starting_jump]\n"; 
+        std::cout << "         [FREEZE_alpha_i FREEZE_alpha_m FREEZE_beta FREEZE_gamma FREEZE_delta]\n"; 
+        
+        system("pwd");
+        
+        
+        return(1);
+    }
+    else {
+        for (int nArg=0; nArg < argc; nArg++){
+            //std::cout << nArg << " " << argv[nArg] << std::endl;
+            if (nArg > 0 && nArg < 7){
+                params0[nArg-1]= atof(argv[nArg]);
+                std::cout << par_names0[nArg-1] << ": " << params0[nArg-1] <<  std::endl;
+            }
+            if (nArg > 6 && nArg < 11){
+                params1[nArg-7]= atoi(argv[nArg]);
+                std::cout << par_names1[nArg-7] << ": " << params1[nArg-7] <<  std::endl;
+            }
+            if (nArg == 11){
+                verbose_level = atoi(argv[nArg]);
+                std::cout << "verbose level: " << verbose_level <<  std::endl;
+            }
+            if (nArg > 11 && nArg < 17){
+                params2[nArg-12]= atof(argv[nArg]);
+                std::cout << par_names2[nArg-12] << ": " << params2[nArg-12] <<  std::endl;
+            }
+            if (nArg > 16 && nArg < 22){
+                params3[nArg-17]= atof(argv[nArg]);
+                var_fixed[nArg-17]= atof(argv[nArg]);
+                std::cout << par_names3[nArg-17] << ": " << var_fixed[nArg-17] <<  std::endl;
+            }
+            
+            
+        }
+        
+    }
+    
+    for (int j=0; j<5; j++){
+        
+        cout << j << " | " << var_fixed[j] << " (fixed) \n";
+    }
+	
+    target=params0[5];
+    range = (double)params1[3];
+	int identify_failed = 0;
+	char* filename= (char *)"movie_graph.txt";
+ 	int n_ep=params1[0], n_st=params1[1], n_rep=params1[2];
+    
+    //...............................
+    
+    for(int i=0; i<Nworkers; i++){
+        un[i] = new Universe((char *)filename,n_ep,n_st,
+                             (int)n_rep,
+                             identify_failed, target, i2string(i));
+        CustomQueues[i] = dispatch_queue_create(i2char(i), NULL);
+    }
+    
+    //...............................
+    if(n_rep > 0){
+        
+        Results = new double[n_rep];
+        Counters = new int[n_rep];
+        
+    }else {
+        
+        Results =  NULL;
+        Counters = NULL;
+        std::cout << " Number of reruns should be positive! " <<  std::endl;
+        return 0;
+        
+    }
+    //...............................
+    srand(time(0));
+    //srandomdev();
+    
+    {
+        double r=0;
+        for (int j=0; j<100; j++){
+            
+            
+            
+            r = rand()/(double)(pow(2.,31)-1.);
+            std::cout << r << " ";
+        }
+        std::cout << "\n ";
+    }
+  	//random initiation of starting parameters
+    
+    if (range > 0.){
+        
+        for (int i=0; i < 5; i++){
+            
+            if (params0[i]==-100.){
+                
+                double r1 = (rand()/(double)(pow(2.,31)-1.));
+                double r2 = (rand()/(double)(pow(2.,31)-1.));
+                double sign = 1.;
+                
+                if(r1 > 0.5){
+                    sign=-1.;
+                }
+                
+                params0[i] = sign*r2*range;
+                
+                std::cout << par_names0[i] << ": " << params0[i] <<  std::endl;
+            }
+        }
+        
+    }
+    
+    
+    double T_start=params2[0], T_end=params2[1], Target_rejection=params2[3], starting_jump=params2[4];
+    int Annealing_repeats = (int) params2[2];
+    
+    
+    dispatch_group_t group = dispatch_group_create();
+    
+    //.............................
+    multi_annealing(group, un, CustomQueues, T_start, T_end, Target_rejection, Annealing_repeats, 
+                    starting_jump, Results, Counters, params0, Annealing_repeats);
+    
+    //dispatch_group_wait(group, DISPATCH_TIME_FOREVER);
+    dispatch_release(group);
+    //.............................
+    
+    
+    // stop timer
+    gettimeofday(&t2, NULL);
+    
+    // compute and print the elapsed time in millisec
+    elapsedTime = (t2.tv_sec - t1.tv_sec) * 1000.0;      // sec to ms
+    elapsedTime += (t2.tv_usec - t1.tv_usec) / 1000.0;   // us to ms
+    elapsedTime /= 1000.;
+    cout << elapsedTime << " seconds \n .....(" << elapsedTime/60. << " minutes)\n\n";
+    
+    //.....................
+    
+    for(int i=0; i<Nworkers; i++){
+        delete un[i];
+    }
+    
+    //....................
+    if(n_rep > 0){
+        
+        delete [] Results;
+        delete [] Counters;
+        
+    }
+    
+    return 0;
+	
+	
+	
+}
+

Deleted: SwiftApps/SciColSim/optimizer.protomods.cpp
===================================================================
--- SwiftApps/SciColSim/optimizer.protomods.cpp	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/optimizer.protomods.cpp	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,1818 +0,0 @@
-//
-//  main.cpp
-//  optimizer
-//
-//  Created by Andrey Rzhetsky on 4/11/11.
-//  Copyright 2011 University of Chicago. All rights reserved.
-//
-
-#define MAXNworkers 24
-int Nworkers=MAXNworkers;
-
-// Add operation code to enable existing code to be used at lower level from Swift scripts:
-
-char operation = 'n'; // n: normal; m: do one multi_loss (with n_reruns).
-                      // Not used: a: analyze and generate next annealing parameter set. g: tbd
-
-#include <fstream>
-#include <sstream>
-#include <iostream>
-#include <stdio.h>
-#include <time.h>
-#include <ctime>    
-#include <algorithm>
-#include <string>
-
-#include <stdio.h>
-#include <sys/param.h>
-#include <sys/time.h>
-#include <sys/types.h>
-
-// #include <dispatch/dispatch.h>
-#include <fstream>
-
-
-#include <stdlib.h>
-#include <boost/numeric/ublas/io.hpp>
-#include <boost/graph/graph_traits.hpp>
-#include <boost/graph/dijkstra_shortest_paths.hpp>
-#include <boost/graph/loop_erased_random_walk.hpp>
-#include <boost/graph/random.hpp>
-#include <boost/property_map/property_map.hpp>
-#include <boost/graph/graph_concepts.hpp>
-#include <boost/graph/properties.hpp>
-
-#include <boost/graph/graph_traits.hpp>
-#include <boost/graph/adjacency_list.hpp>
-#include <boost/graph/adjacency_matrix.hpp>
-
-#define BOOST_MATH_OVERFLOW_ERROR_POLICY ignore_error
-#define BOOST_MATH_DISCRETE_QUANTILE_POLICY real
-#include <boost/graph/random.hpp>
-#include <boost/random/geometric_distribution.hpp>
-#include <boost/random/uniform_01.hpp>
-#include <boost/random.hpp>
-#include <boost/random/linear_congruential.hpp>
-#include <boost/random/uniform_int.hpp>
-#include <boost/random/uniform_real.hpp>
-#include <boost/random/variate_generator.hpp>
-#include <boost/generator_iterator.hpp>
-#include <boost/lexical_cast.hpp>
-
-#define INT_INFINITY 2147483647
-#define NEVOPARAMS 5
-
-#define FIX_VARIABLES 1
-
-using namespace boost;
-using namespace std;
-using namespace boost::numeric::ublas;
-
-static int max_dist=0;
-
-typedef boost::adjacency_matrix<boost::directedS> Graph;
-typedef std::pair<int,int> Edge;
-typedef boost::graph_traits<Graph> GraphTraits;
-typedef boost::numeric::ublas::triangular_matrix<double, boost::numeric::ublas::strict_upper> prob;
-typedef boost::numeric::ublas::triangular_matrix<double, boost::numeric::ublas::strict_upper> pathlength;
-typedef boost::graph_traits<Graph>::vertex_descriptor vertex_descriptor;
-
-namespace std {
-	using ::time;
-}
-
-static int var_fixed[NEVOPARAMS] = {1, 0, 1, 1, 0};
-
-typedef boost::minstd_rand base_generator_type;
-typedef adjacency_list < listS, vecS, directedS,
-no_property, property < edge_weight_t, int > > graph_t;
-typedef graph_traits < graph_t >::vertex_descriptor vertex_descriptor;
-typedef graph_traits < graph_t >::edge_descriptor edge_descriptor;
-
-
-//================================================
-string strDouble(double number)
-{
-    stringstream ss;//create a stringstream
-    ss << number;//add number to the stream
-    return ss.str();//return a string with the contents of the stream
-}
-
-//================================================
-
-double gaussian(double sigma)
-{
-    double GaussNum = 0.0;
-    int NumInSum = 10;
-    for(int i = 0; i < NumInSum; i++)
-    {
-        GaussNum += ((double)rand()/(double)RAND_MAX - 0.5);
-    }
-    GaussNum = GaussNum*sqrt((double)12/(double)NumInSum);
-    
-    
-    return GaussNum;
-    
-}
-
-
-
-//=================================================
-double diffclock(clock_t clock1,clock_t clock2)
-{
-	double diffticks=clock1-clock2;
-	double diffms=(diffticks)/CLOCKS_PER_SEC;
-	return diffms;
-}
-
-//================================================
-//================================================================
-double get_new_x(double x, double dx){
-    
-    double new_x;
-    // boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
-    double r = rand()/(double)(pow(2.,31)-1.);
-    
-    if (r > 0.5){            
-        new_x = x + rand()*dx/(double)(pow(2.,31)-1.);
-    } else {            
-        new_x = x - rand()*dx/(double)(pow(2.,31)-1.);
-    }
-    
-    return new_x;
-    
-}
-
-
-//===============================================   
-string string_wrap(string ins, int mode){
-    
-    std::ostringstream s;
-    
-    switch(mode){
-        case 0:
-            s << "\033[1;29m" << ins << "\033[0m";
-            break;
-        case 1:
-            s << "\033[1;34m" << ins << "\033[0m";
-            break;
-        case 2:
-            s << "\033[1;44m" << ins << "\033[0m";
-            break;
-        case 3:
-            s << "\033[1;35m" << ins << "\033[0m";
-            break;
-        case 4:
-            s << "\033[1;33;44m" << ins << "\033[0m";
-            break;
-        case 5:
-            s << "\033[1;47;34m" << ins << "\033[0m";
-            break;
-        case 6:
-            s << "\033[1;1;31m" << ins << "\033[0m";
-            break;
-        case 7:
-            s << "\033[1;1;33m" << ins << "\033[0m";
-            break;
-        case 8:
-            s << "\033[1;1;43;34m" << ins << "\033[0m";
-            break;
-        case 9:
-            s << "\033[1;1;37m" << ins << "\033[0m";
-            break;
-        case 10:
-            s << "\033[1;30;47m" << ins << "\033[0m";
-            break;
-        default:
-            s << ins;
-    }
-    
-    return s.str();
-}
-
-
-//===============================================
-string wrap_double(double val, int mode){
-    
-    std::ostringstream s;
-    s << string_wrap(strDouble(val),mode);
-    
-    return s.str();
-}
-
-
-
-//===============================================
-const     
-string i2string(int i){
-    
-    std::ostringstream s;
-    s << "worker" 
-    << lexical_cast<std::string>(i);
-    
-    return s.str();
-    
-}
-
-//===============================================
-char* i2char(int i){
-    
-    std::ostringstream s;
-    s << "worker" 
-    << lexical_cast<std::string>(i);
-    
-    char* a=new char[s.str().size()+1];
-    memcpy(a,s.str().c_str(), s.str().size());
-    
-    return a;
-}
-
-
-template <class T>
-bool from_string(T& t, 
-                 const std::string& s, 
-                 std::ios_base& (*f)(std::ios_base&))
-{
-  std::istringstream iss(s);
-  return !(iss >> f >> t).fail();
-}
-
-//================================================
-class Universe {
-	
-private:
-	
-	double alpha_i;
-	double alpha_m;
-	double beta;
-	double gamma;
-	double delta;
-	
-    double TargetNovelty;
-    double CumulativeRelativeLoss;
-    double CRLsquare;
-    string id;
-    
-    
-	int N_nodes;
-	int M_edges;
-	
-	int N_epochs;
-	int N_steps;
-	int N_repeats;
-	
-	int current_epoch;
-	double current_loss;
-	int current_repeat;
-    double current_novelty;
-	
-	int mode_identify_failed;
-    int verbose_level; // 0 is silent, higher is more
-	
-	double k_max;
-	
-	graph_t Full_g;
-	
-	double **Prob;
-	double **Tried;
-	double **Dist;
-	double **Final;
-    double **EdgeIndex;
-	double *Rank;
-	
-    base_generator_type generator;	
-    boost::uniform_real<> uni_dist;
-    boost::geometric_distribution<double> geo;
-    
-public:
-	
-    
-    
-	//======  Constructor ======
-	Universe(const std::string FileToOpen, int Epochs, int Steps, int Repeats, int identify_failed, double target, const std::string idd)
-	{
-		//typedef array_type2::index index2;
-		
-		
-		std::ifstream inFile;
-        //string line;
-        
-        //-------------------------------
-        
-        base_generator_type gene(42u);
-        generator = gene;
-        generator.seed(static_cast<unsigned int>(std::time(0)));
-        boost::uniform_real<> uni_d(0,1);
-        uni_dist = uni_d;
-        
-        //--------------------------------
-        
-		int i, k;
-        int x, y;
-		Edge* edge_array_mine;
-		int num_arcs_mine, num_nodes_mine;
-		int* weights_mine;
-		
-        TargetNovelty = target;
-        CumulativeRelativeLoss = 0.;
-        CRLsquare = 0.;
-        
-		
-		N_epochs  = Epochs;
-		N_steps   = Steps;
-		N_repeats = Repeats;
-		
-		current_epoch = 0;
-		current_loss = 0.;
-		current_repeat = 0;
-        
-        id = idd;
-        
-        verbose_level = 1;
-		
-		mode_identify_failed = identify_failed;
-        
-		
-		//-------------------------------
-		// The first pass though file with the graph
-		inFile.open(FileToOpen.c_str());
-		if (inFile.fail()) {
-			cout << "Unable to open file";
-			exit(1); // terminate with error
-		}else {
-            
-            if (verbose_level > 2){
-                std::cout <<  " Opened <" << FileToOpen << ">"<<std::endl;
-            }
-        }
-		
-		i=0;
-        std::string line;
-		//while (! inFile.eof() && ! inFile.fail()) {
-        while (1==1) {
-            
-            inFile >> x;
-            inFile >> y;
-            
-            if (verbose_level > 2){
-                std::cout << " x: " << x;
-                std::cout << " y: " << y << std::endl;
-            }
-            
-			if (i==0){
-				N_nodes=x;
-				M_edges=y;	
-                break;
-			}
-			i++;
-            
-			
-		}
-		inFile.close();
-        
-        if (verbose_level == 2){
-            std::cout << N_nodes <<  " nodes, " << M_edges << " edges"<<std::endl;
-        }
-		
-		// k_max is the longest distance possible
-		
-        //k_max = M_edges;
-		k_max = 70;
-        
-		//------------------------------------
-		// Get memory allocated for all class members
-		
-		Prob = allocate_2Dmatrix(N_nodes, N_nodes);
-		Tried = allocate_2Dmatrix(N_nodes, N_nodes);
-		Dist = allocate_2Dmatrix(N_nodes, N_nodes);
-		Final = allocate_2Dmatrix(N_nodes, N_nodes);
-        EdgeIndex = allocate_2Dmatrix(N_nodes, N_nodes);
-		Rank = allocate_1Dmatrix(N_nodes);
-		
-        //The second pass through file with the graph
-        
-		for(int i = 0; i < N_nodes; ++i) {
-			Rank[i]=0.;
-			for(int j = 0; j < N_nodes; ++j) {
-				Final[i][j] = 0.;
-				Prob[i][j]=0.;
-				Dist[i][j]=-1.;
-				Tried[i][j]=0.;
-                EdgeIndex[i][j]=-1;
-			}
-		}
-        
-		
-		// Fill in the final graph -- and we are ready to go!
-        
-	    inFile.open(FileToOpen.c_str());
-		if (!inFile) {
-            std::cout << "Unable to open file";
-			exit(1); // terminate with error
-		}
-		else {
-            
-            if (verbose_level > 2){
-                std::cout <<  " Opened <" << FileToOpen << ">"<<std::endl;
-            }
-        }
-        
-		i=0;  
-		while (inFile >> x && inFile >>y) {
-			if (i > 0) {
-				Final[x][y]=1.;
-				Final[y][x]=1.;
-                
-                
-                if (verbose_level == 2){
-                    std::cout << ".";
-                }
-			}
-			i++;
-			
-		}
-        if (verbose_level == 2){
-            std::cout << std::endl;
-        }
-		inFile.close(); 
-		
-        k=0;
-        for (int i=0; i<N_nodes-1; i++){
-            for (int j=i+1;j<N_nodes; j++){
-                if(Final[i][j] > 0.){
-                    EdgeIndex[i][j]=k;
-                    k++;
-                }
-            }
-        }
-        
-        
-		
-		//+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
-		// create graph -- hopefully, we can keep it, just modifying edge weights
-		
-		
-		edge_array_mine = new Edge[2*M_edges];
-		num_arcs_mine = 2*M_edges;
-		num_nodes_mine = N_nodes;
-		weights_mine = new int[2*M_edges];
-		for (int i=0; i<2*M_edges; i++){ weights_mine[i]=1;}
-		
-		k=0;
-		for(int i=0; i<N_nodes-1; i++){
-			for( int j=i+1; j<N_nodes; j++){
-				if (Final[i][j]>0.){
-					edge_array_mine[2*k]  =Edge(i,j);
-					edge_array_mine[2*k+1]=Edge(j,i);
-					k++;
-				}
-			}
-		}
-		graph_t g(edge_array_mine, edge_array_mine + num_arcs_mine, weights_mine, num_nodes_mine);
-		
-		Full_g = g;
-		delete edge_array_mine;
-		delete weights_mine;
-		
-		//===========================================================================
-		std::vector<edge_descriptor> p(num_edges(Full_g));
-		std::vector<int> d(num_edges(Full_g));
-		edge_descriptor s;
-		boost::graph_traits<graph_t>::vertex_descriptor u, v;
-		
-		for (int i=0; i<N_nodes-1; i++){
-			for (int j=i+1; j<N_nodes; j++){
-				if (Final[i][j] > 0.){
-					u = vertex(i, Full_g);
-					v = vertex(j, Full_g);
-					remove_edge(u,v,Full_g);
-					remove_edge(v,u,Full_g);
-					
-				}
-			}
-		}
-		
-        
-    }
-	
-	
-	//=====================================================================
-	int sample_failed_number(double pfail){
-		
-		//boost::geometric_distribution<double> geo(pfail);
-		//boost::variate_generator<base_generator_type&, geometric_distribution<double> > geom(generator, geo);
-		
-		double r, u, g;
-        
-        r=0.;
-		for(int i=0; i<N_steps; i++){
-            
-            u=(double)rand();
-            u = 1.-u /(double)(pow(2.,31)-1.);
-            g=(int)(ceil(log(u) / log(pfail)));
-            
-			//r += geom();
-            
-            r+=g;
-		}
-        
-        if (verbose_level>=3){
-            std::cout << id << " failed " << r << std::endl;
-		}
-		return r;
-		
-	}
-    
-    //=============================================
-    double get_target(void){
-        return TargetNovelty;
-    }
-	
-    //=============================================
-    void set_target(double target){
-        TargetNovelty=target;
-    }
-	
-	//=============================================
-	int sample(){
-		
-        //boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
-        // double r = uni(), Summa = 0.;
-        
-        
-        
-        double r = rand(), Summa = 0.;
-        r /= (double)(pow(2.,31)-1.);
-		int result = 0;
-		int finished = 0;
-        
-        if (verbose_level==4){
-            std::cout << id << " sampled " << r << std::endl;
-        }
-		
-		for(int i=0; i<N_nodes-1 && finished==0; i++){			
-			for( int j=i+1; j<N_nodes && finished==0; j++){
-				
-				Summa += Prob[i][j];
-				
-				if (Summa > r){
-					
-					Tried[i][j]+=1.;
-					
-					if (Final[i][j] > 0.){
-						result = 1;
-					}
-					finished = 1;										
-				}
-			}
-		}
-		
-		return result;
-		
-	}
-	
-	//===============================
-	void update_current_graph(void){
-		
-		std::vector<edge_descriptor> p(num_edges(Full_g));
-		std::vector<int> d(num_edges(Full_g));
-		edge_descriptor s;
-		boost::graph_traits<graph_t>::vertex_descriptor u, v;
-		
-		//property_map<graph_t, edge_weight_t>::type weightmap = get(edge_weight, Full_g);
-		for (int i=0; i<N_nodes-1; i++){
-			for (int j=i+1; j<N_nodes; j++){
-				if (Final[i][j] > 0. && Tried[i][j]>0){
-					//s = edge(i, j, Full_g);	
-					boost::graph_traits<graph_t>::edge_descriptor e1,e2;
-					bool found1, found2;
-					u = vertex(i, Full_g);
-					v = vertex(j, Full_g);
-					tie(e1, found1) = edge(u, v, Full_g);
-					tie(e2, found2) = edge(v, u, Full_g);
-					if (!found1 && !found2){
-						add_edge(u,v,1,Full_g);
-					    add_edge(v,u,1,Full_g);
-					}
-					
-				}
-			}
-			
-		}
-	}
-	
-	//===============================
-	void update_distances(void){
-		// put shortest paths to the *Dist[][]
-		std::vector<vertex_descriptor> p(num_vertices(Full_g));
-		std::vector<int> d(num_vertices(Full_g));
-		vertex_descriptor s;
-		
-		
-		// put shortest paths to the *Dist[][]
-		for (int j=0; j<num_vertices(Full_g); j++){
-			
-			if(Rank[j] > 0.){
-				s = vertex(j, Full_g);	 
-				dijkstra_shortest_paths(Full_g, s, predecessor_map(&p[0]).distance_map(&d[0]));
-				
-				//std::cout <<" Vertex "<< j << std::endl;
-				graph_traits < graph_t >::vertex_iterator vi, vend;
-				
-				for (boost::tie(vi, vend) = vertices(Full_g); vi != vend; ++vi) {
-					
-					if (p[*vi]!=*vi){
-						Dist[*vi][j]=d[*vi];
-						Dist[j][*vi]=d[*vi];
-                        
-                        if (Dist[*vi][j]>max_dist){
-                            max_dist=Dist[*vi][j];
-                        }
-                        
-                        
-					} else {
-						Dist[*vi][j]=-1.;
-						Dist[j][*vi]=-1.;
-					}
-				}
-			}
-			
-		}
-		
-		
-	}
-	
-	//======================================================
-	void update_ranks(void){
-		
-		for(int i=0; i<N_nodes; i++){
-			Rank[i]=0.;
-		}
-		
-		for(int i=0; i<N_nodes-1; i++){
-			for( int j=i+1; j<N_nodes; j++){
-				if (Tried[i][j]>0. && Final[i][j] >0.){
-					Rank[i]++;
-					Rank[j]++;
-				}
-			}
-		}
-		
-	}
-	
-	//====================================================================
-	void set_world(double a_i, double a_m, double b, double g, double d){
-		
-		alpha_i=a_i;
-		alpha_m=a_m;
-		gamma=g;
-		beta=b;
-		delta=d;
-		
-	}
-	
-	//====================================================================
-	void reset_world(){
-		
-        //====================================================
-		std::vector<edge_descriptor> p(num_edges(Full_g));
-		std::vector<int> d(num_edges(Full_g));
-		edge_descriptor s;
-		boost::graph_traits<graph_t>::vertex_descriptor u, v;
-        
-		
-		for (int i=0; i<N_nodes-1; i++){
-			for (int j=i+1; j<N_nodes; j++){
-				if (Final[i][j] > 0. && Tried[i][j] > 0){
-					u = vertex(i, Full_g);
-					v = vertex(j, Full_g);
-					remove_edge(u,v,Full_g);
-					remove_edge(v,u,Full_g);
-					
-				}
-			}
-		}
-        
-        //==================================================
-        
-		current_loss=0;
-		current_epoch=0;
-		current_repeat++;
-        current_novelty=0;
-		
-		for(int i = 0; i < N_nodes; ++i) {
-			Rank[i]=0.;
-			for(int j = 0; j < N_nodes; ++j) {
-				Prob[i][j]=0.;
-				Dist[i][j]=-1.;
-				Tried[i][j]=0.;
-			}
-		}
-	}
-	
-	
-    //==============================================
-    void show_parameters(void){
-        
-        std::cout << "Parameters: " 
-        << alpha_i << " "
-        << alpha_m << " | "
-        << beta << " "
-        << gamma << " | "
-        << delta << std::endl;
-        
-    }
-    
-    
-    
-    //===============================================
-    string file_name(){
-        
-        std::ostringstream s;
-        s << "world_" 
-        << lexical_cast<std::string>(alpha_i) << "_" 
-        << lexical_cast<std::string>(alpha_m) << "_"
-        << lexical_cast<std::string>(beta) << "_"
-        << lexical_cast<std::string>(gamma) << "_"
-        << lexical_cast<std::string>(delta) << "_"
-        << lexical_cast<std::string>(N_epochs) << "_"
-        << lexical_cast<std::string>(N_steps) << "_"
-        << lexical_cast<std::string>(N_repeats) << ".txt";
-        
-        return s.str();
-        
-    }
-    
-    
-    
-    
-    //=================================================
-    void set_verbose(int verbose){
-        
-        verbose_level = verbose;
-    }
-    
-    
-    //=============================================================
-    void update_probabilities(void){
-        
-        
-        //=========================
-		// Compute sampling probabilities
-		// first pass: \xi_i,j
-		for(int i=0; i<N_nodes-1; i++){
-			for( int j=i+1; j<N_nodes; j++){
-				
-				double bg = 0.;
-				
-				Prob[i][j] = alpha_i*log(min(Rank[i]+1.,Rank[j]+1.)) + 
-                alpha_m*log(max(Rank[i]+1.,Rank[j]+1.));
-				
-                if (Dist[i][j] > 0.){
-                    
-                    double k = Dist[i][j];
-                    if (k >= k_max){
-                        k = k_max-1;
-                    }
-					
-                    bg = beta * log(k/k_max) + gamma * log(1. - k/k_max);
-					
-                } else {
-                    bg = delta;
-                }
-				
-				Prob[i][j] = exp(Prob[i][j] + bg);
-			}
-		}
-        
-		
-		// second pass: sum
-		double Summa = 0.;
-		
-		for(int i=0; i<N_nodes-1; i++){
-			for( int j=i+1; j<N_nodes; j++){
-				Summa += Prob[i][j];
-			}
-		}
-		
-		// third pass: normalize
-		for(int i=0; i<N_nodes-1; i++){
-			for( int j=i+1; j<N_nodes; j++){
-				Prob[i][j] /= Summa;
-			}
-		}
-        
-    }
-    
-	// Now we are ready for simulations
-	//==============================================
-	void update_world(){
-		
-		int failed = 0;
-        
-		// Given current universe compute shortest paths
-		//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-		
-		update_current_graph();
-		update_ranks();				
-		update_distances();
-		update_probabilities();
-		
-		//===============================
-		// sampling
-		int result;
-		double cost=0., novel=0.;
-		int publishable = 0;
-		
-		
-		//^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-		if (mode_identify_failed == 1){
-			
-			while(publishable < N_steps){
-				
-		    	result = sample();
-			    publishable += result;
-			    failed += (1-result);
-				
-			}
-			
-			for(int i=0; i<N_nodes-1; i++){
-				for( int j=i+1; j<N_nodes; j++){
-					
-					cost+=Tried[i][j];
-					
-					if (Tried[i][j]>0. && Final[i][j]>0.){
-						novel+=1.;
-					}
-				}
-			}
-			
-		}
-		//^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-		else {
-			
-			double pfail=0.;
-			int n_failed;
-			//, n_check = 0;
-			
-			for(int i=0; i<N_nodes-1; i++){
-				for( int j=i+1; j<N_nodes; j++){
-					if (Final[i][j] == 0.){
-						pfail += Prob[i][j];
-						Prob[i][j] = 0.;
-					}
-					
-				}
-			}
-			
-			for(int i=0; i<N_nodes-1; i++){
-				for( int j=i+1; j<N_nodes; j++){
-					Prob[i][j] /= (1.-pfail);
-				}
-				//std::cout << std::endl;
-			}			
-			
-			n_failed = sample_failed_number(pfail);
-			while(publishable < N_steps){
-				
-		    	result = sample();
-			    publishable += result;					
-			}
-            
-            
-			current_loss += (n_failed + N_steps);
-			cost = current_loss;
-			
-			for(int i=0; i<N_nodes-1; i++){
-				for( int j=i+1; j<N_nodes; j++){
-					
-					if (Tried[i][j]>0. && Final[i][j]>0.){
-						novel+=1.;
-					}
-				}
-			}
-		}
-        
-        current_novelty = novel;
-        
-        
-		//^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-		if (verbose_level == 2){
-            std::cout << (current_repeat+1) << "  epoch=" << (current_epoch+1) 
-            
-		    << "  cost=" << cost 
-		    << " novel=" << novel 
-		    << " rel_loss=" << cost/novel
-		    << std::endl;
-        }
-        
-		current_epoch++;
-	}
-	
-	
-	//======  Destructor ======
-	~Universe(){
-		
-		delete_2Dmatrix(Final, N_nodes);
-		delete_2Dmatrix(Dist, N_nodes);
-		delete_2Dmatrix(Tried, N_nodes);
-		delete_2Dmatrix(Prob, N_nodes);
-        delete_2Dmatrix(EdgeIndex, N_nodes);
-		delete_1Dmatrix(Rank);		
-	}
-    
-    //================================================
-    // Allocate memory
-    double** allocate_2Dmatrix(int N, int M)
-    {
-        double **pointer;
-        
-        if (verbose_level == 2){
-            std::cout<< "["<<N<<"|"<<M<<"]"<<std::endl;
-        }
-        pointer = new double*[N];
-        for (int i = 0; i < N; ++i)
-            pointer[i] = new double[M];	
-        
-        return pointer;
-    }
-    //===================
-    double* allocate_1Dmatrix(int N)
-    {
-        double *pointer;
-        
-        if(N > 0){
-            
-            pointer = new double[N];
-            
-        }else {
-            
-            pointer = NULL;
-        }
-        
-        return pointer;
-        
-    }
-    
-    //==============================================
-    // De-Allocate memory to prevent memory leak
-    void delete_2Dmatrix(double **pointer, int N){
-        
-        if (pointer != NULL){
-            
-            for (int i = 0; i < N; ++i){
-                delete [] pointer[i];
-            }
-            delete [] pointer;
-        }
-    }
-    //====================
-    void delete_1Dmatrix(double *pointer){
-        
-        delete [] pointer;
-    }
-    
-    //===========================================
-    double get_rel_loss(){
-        
-        return CumulativeRelativeLoss ;
-    }
-    
-    //===========================================
-    double get_rel_loss_err(){
-        
-        return CRLsquare ;
-    }
-    
-    
-    
-    //==================================================================================
-    void evolve_to_target_and_save(int istart, int iend, double* storage, int* counters){
-        
-        double ALOT=100000000000.;
-        
-	// std::cout<<" evolve_to_target_and_save: istart=" << istart << "iend=" << iend << "\n";
-
-        reset_world();
-        
-        for (int k = istart; k < iend; k++){
-
-	  // std::cout<<" evolve: k=" << k << "\n";
-
-            
-            
-            for(int i=0; i< N_epochs &&  current_novelty < TargetNovelty; i++){
-	      // std::cout<<" evolve: k=" << k << " i=" << i << " cur=" << current_novelty << " Target=" << TargetNovelty << "\n";
-                update_world();
-            }
-            
-            storage[k]=current_loss/current_novelty;
-            counters[k]=1;
-            
-            
-            reset_world();
-        }
-        
-    }
-    //==============================================
-    int get_reruns(void){
-        return N_repeats;
-    }
-    
-    //==============================================
-    double get_parameter(int i){
-        
-        switch(i){
-            case 0:
-                return alpha_i;
-            case 1:
-                return alpha_m;
-            case 2:
-                return beta;
-            case 3:
-                return gamma;
-            case 4:
-                return delta;
-            default:
-                
-                std::cout << "Erroneous parameter id!!!!\n\n\n";
-                return 0.;
-        }
-    }
-    
-    
-    //==============================================
-    void evolve_to_target(){
-        
-        reset_world();
-        if (beta < -1. || gamma < -1.){
-            CumulativeRelativeLoss = 100000000000.;
-            CRLsquare = 0.;
-            return;
-        }
-        
-        
-        for (int k=0; k< N_repeats; k++){
-            
-            
-            for(int i=0; i<N_epochs &&  current_novelty < TargetNovelty; i++){
-                update_world();
-            }
-            
-            CumulativeRelativeLoss += current_loss/current_novelty;
-            CRLsquare += (current_loss/current_novelty)*(current_loss/current_novelty);
-            if(verbose_level==3){
-                std::cout <<  CumulativeRelativeLoss << " | " << CRLsquare << std::endl;
-            }
-            
-            if(verbose_level==1){
-                std::cout <<  "." ;
-                
-            }
-            else if(verbose_level==2){
-                std::cout <<  "**" << (k+1) <<  "**  curr loss " << current_loss << "; curr novelty " << current_novelty << std::endl;
-            }
-            
-            
-            reset_world();
-        }
-        
-        CumulativeRelativeLoss /= double(N_repeats);
-        CRLsquare /= double(N_repeats);
-        
-        if(verbose_level==1){
-            std::cout << std::endl;
-        }
-        
-        if(verbose_level==2){
-            std::cout <<  CumulativeRelativeLoss << " || " << CRLsquare << std::endl;
-        }
-        
-        CRLsquare = 2*sqrt((CRLsquare - CumulativeRelativeLoss*CumulativeRelativeLoss)/double(N_repeats));
-        
-    }
-    
-    
-    //================================================================    
-    int set_parameter(double value, int position){
-        
-        if (position < 0 || position > 4) {return 0;}
-        
-        else {
-            
-            switch(position){
-                case 0:
-                    alpha_i=value;
-                    return 1;
-                case 1:
-                    alpha_m=value;
-                    return 1;
-                case 2:
-                    beta=value;
-                    return 1;
-                case 3:
-                    gamma=value;
-                    return 1;
-                case 4:
-                    delta=value;
-                    return 1;
-            }
-            
-        }
-        
-        return 0;
-    }
-
-#ifdef notdef
-    //=================================================================
-    void try_annealing(double starting_jump, int iterations, 
-                       double temp_start, double temp_end, double target_rejection){
-        
-        double dx[5]={0.,0.,0.,0.,0};
-        double x[5]={0.,0.,0.,0.,0};
-        double rejection[5]={0., 0., 0., 0., 0.};
-        double curr_x, curr_err, x_tmp;
-        double temperature;
-        double ratio, r;
-        int cycle=10;
-        boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
-        
-        // set up parameter for annealing
-        
-        x[0]=alpha_i;
-        x[1]=alpha_m;
-        x[2]=beta;
-        x[3]=gamma;
-        x[4]=delta;
-        
-        for(int i=0;i<5;i++){
-            dx[i] = starting_jump;
-        }
-        
-        // establish the current value
-        
-        //..........................................
-        evolve_to_target();        
-        std::cout << CumulativeRelativeLoss << " +- " << CRLsquare << std::endl;
-        
-        curr_x   = CumulativeRelativeLoss;
-        curr_err = CRLsquare;
-        CumulativeRelativeLoss = 0;
-        CRLsquare = 0;
-        //...........................................
-        
-        // optimization cycle
-        for(int i=0; i<iterations; i++){
-            
-            temperature = temp_start*exp( i*(log(temp_end)-log(temp_start))/(double)iterations);
-            std::cout  << std::endl << "....T = " << wrap_double(temperature,3) << std::endl << std::endl;
-            
-            if (i % cycle == 0 && i > 0){
-                
-                for (int k=0; k<5; k++){
-                    
-                    rejection[k]/=(double)cycle;
-                    if (rejection[k] > 0){
-                        dx[k] = dx[k]/(rejection[k]/target_rejection);
-                        rejection[k]=0.;
-                    }
-                    else{
-                        dx[k]*=2.;
-                    }
-                    std::cout  << dx[k] << " ";
-                }
-                std::cout  << std::endl;
-            }
-            
-            
-            for (int j=0; j<5; j++){
-                
-                // get new value of x[j]
-                x_tmp = get_new_x(x[j],dx[j]);
-                
-                
-                
-                //.............................................
-                set_parameter(x_tmp, j);
-                
-                
-                evolve_to_target(); 
-                
-                std::cout  << std::endl << "......... " << std::endl;
-                std::cout << "Trying... " << CumulativeRelativeLoss << " +- " << CRLsquare << std::endl;
-                
-                ratio = min(1.,exp(-(CumulativeRelativeLoss-curr_x)/temperature));
-                r = uni();
-                std::cout << r << " vs " << ratio << std::endl;
-                
-                if (r > ratio){
-                    
-                    std::cout << string_wrap(id, 4) <<" "<< (i+1) << ","<< (j) 
-                    <<" "<< (i+1) << " Did not accept " 
-                    << x_tmp << "(" << j << ")" << std::endl;
-                    std::cout << alpha_i << " "<< alpha_m << " "
-                    << beta << " " << gamma << " " 
-                    << delta << " " << std::endl;
-                    set_parameter(x[j], j);
-                    CumulativeRelativeLoss = 0;
-                    CRLsquare = 0;
-                    
-                    rejection[j]+=1.;
-                }
-                
-                else {
-                    
-                    curr_x   = CumulativeRelativeLoss;
-                    curr_err = CRLsquare;
-                    x[j] = x_tmp;
-                    CumulativeRelativeLoss = 0;
-                    CRLsquare = 0;
-                    std::cout << (i+1) << string_wrap((string) " Rejection counts: ", 8) 
-                    << wrap_double(rejection[0],2) 
-                    << " "<< wrap_double(rejection[1], 7) << " "
-                    << wrap_double(rejection[2],5) << " " << wrap_double(rejection[2],9) << " " 
-                    << wrap_double(rejection[4],6) << " " 
-                    << std::endl << std::endl;
-                    
-                    std::cout << string_wrap(id, 4) <<" "<< (i+1) <<","<< (j) 
-                    <<" "
-                    << string_wrap((string) "***** Did accept! ", 3) 
-                    << wrap_double(alpha_i,2) 
-                    << " "<< wrap_double(alpha_m, 7) << " "
-                    << wrap_double(beta,5) << " " 
-                    << wrap_double(gamma,9) << " " 
-                    << wrap_double(delta,6) << " " 
-                    << std::endl << std::endl;
-                    
-                }
-                //........................................................ 
-                
-            }
-            
-        }
-        
-    }
-    
-#endif // notdef
-
-	
-};
-
-
-//============================================================
-
-std::pair<double,double> multi_loss( // dispatch_group_t group, 
-                                    Universe* un[], 
-                                    // dispatch_queue_t* CustomQueues,
-                                    double* Results,
-                                    int*    Counters,
-                                    double* params){
-    
-    int N = un[0]->get_reruns();
-    int step = (int)(double)N/(double)(Nworkers);
-    int istart=0;
-    int iend = istart+step;
-    
-    double Loss=0., LossSquare=0.;
-    
-    timeval startTime, endTime;
-    double elapsedTime;
-    // start timer
-    gettimeofday(&startTime, NULL);
-
-    //err:    for(int i=0; i<Nworkers; i++){
-
-    std::cout << "Entry to multi_loss, params: ";
-    for(int i=0; i<Nworkers; i++){
-        for(int j=0; j<NEVOPARAMS; j++){
-	  std::cout << "[" << un[i]->get_parameter(j) << "," << params[j] << "] ";
-            un[i]->set_parameter(params[j],j);
-        }
-    }
-    std::cout << "\n";
-    int i;
-    #pragma omp parallel for private (i)
-    for(i=0; i<Nworkers; i++){
-
-      // dispatch_group_async(group, CustomQueues[i], ^{
-      std::cout<<"multi_loss: Calling evolve_to_target_and_save i=" << i << " N=" << N << " step=" << step << " istart=" << i*step << " iend=" << (i+1)*step << "\n";
-      //un[i]->evolve_to_target_and_save(istart, iend, Results, Counters);
-      un[i]->evolve_to_target_and_save(i*step, min((i+1)*step,N), Results, Counters);
-      //});
-
-      // std::cout<<"multi_loss: Returned from evolve_to_target_and_save " << i << "\n";
-      
-
-      //istart += step;
-      //iend = min(istart+step,N);
-            
-    }
-    // err    }
-    // dispatch_group_wait(group, DISPATCH_TIME_FOREVER);
-    //dispatch_release(group);
-    
-
-    for (int i=0; i<N; i++){
-        
-        Loss+=Results[i]/(double)N;
-        LossSquare+=Results[i]*Results[i]/(double)N;
-        
-        std::cout<<i<<":"<< Results[i] << " ";
-    }
-    
-    std::cout<<" \n\n\n";
-    double two_std = ((LossSquare - Loss*Loss)/(double)N);
-    
-    two_std = 2.*sqrt(two_std);
-    std::pair<double,double> Res;
-    Res.first=Loss;
-    Res.second=two_std;
-
-    gettimeofday(&endTime, NULL);
-    elapsedTime = (endTime.tv_sec - startTime.tv_sec) * 1000.0;      // sec to ms
-    elapsedTime += (endTime.tv_usec - startTime.tv_usec) / 1000.0;   // us to ms
-    elapsedTime /= 1000.;
-    cout << "multi_loss(N=" << N << ") elapsed time: " << elapsedTime << " seconds " << elapsedTime/60. << " minutes\n\n";
-    
-    return Res;
-}
-//============================================================
-
-
-//============================================================
-void multi_annealing( // dispatch_group_t group, 
-                     Universe* un[], 
-                     // dispatch_queue_t* CustomQueues, 
-                     double T_start, double T_end, 
-                     double Target_rejection, 
-                     int Annealing_repeats, 
-                     double starting_jump,
-                     double* Results,
-                     int*    Counters,
-                     double* params0,
-                     double annealing_cycles){
-    //.................................
-    // re-implement annealing
-    
-    double dx[NEVOPARAMS]={0.,0.,0.,0.,0};
-    double x[NEVOPARAMS]={0.,0.,0.,0.,0};
-    double rejection[NEVOPARAMS]={0., 0., 0., 0., 0.};
-    double curr_x, curr_err, x_tmp;
-    double temperature;
-    double ratio, r;
-    int cycle=10;
-    //boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
-    
-    // set up parameter for annealing
-
-    for(int i=0;i<NEVOPARAMS;i++){
-        x[i]=params0[i];
-        dx[i] = starting_jump;
-	for(int w=0; w<Nworkers; w++){
-	  un[w]->set_parameter(x[i], i);
-	}
-    }
-    
-    // establish the current value
-    std::pair<double,double>Res;
-    
-    if ( operation == 'm' ) {
-      // Nworkers = 1;
-    }
-    else if (operation == 'g') {
-      // generate params: not yet implemented - to be deprecated
-    }
-    else if (operation == 'a') {
-      // analyze multi_loss() results: not tested or used - to be deprecated
-
-      string line;
-      ifstream mlossdata ("multi_loss.data");
-      double d, Loss, LossSquare, two_std;
-      bool b;
-      int n=0;
-      if (mlossdata.is_open()) {
-	while ( getline (mlossdata,line) ) {
-	  b = from_string<double>(d, std::string(line), std::dec);
-	  cout << line << " d=" << d << endl;
-	  Loss += d;
-          LossSquare += (d*d);
-          n++;
-	}
-        Loss /= double(n);
-	LossSquare /= double(n);
-	two_std = ((LossSquare - Loss*Loss)/(double)n);
-	two_std = 2.*sqrt(two_std);
-	std::cout<<"n="<<n<<" Loss="<<Loss<<" LossSquare="<<LossSquare<<" two_std="<<two_std<<"\n\n\n";
-	mlossdata.close();
-	FILE *f=fopen("multi_loss_stats.txt","w");
-	fprintf(f,"%d n\n",n);
-	fprintf(f,"%.20e Loss\n",Loss);
-	fprintf(f,"%.20e LossSquare\n",LossSquare);
-	fprintf(f,"%.20e two_std\n",two_std);
-        fclose(f);
-	exit(0);
-      }
-      else {
-	cout << "Unable to open file multi_loss.data"; 
-	exit(1);
-      }
-    }
-
-    std::cout << "Calling initial multi_loss:\n";
-    Res = multi_loss( /* group,*/ un, /* CustomQueues,*/ Results, Counters, x);
-    std::cout << "Ret from initial multi_loss:\n";
-    std::cout << Res.first << " +- " << Res.second << std::endl;
-
-    if ( operation == 'm' ) {
-      FILE *f;
-      int N = un[0]->get_reruns();
-
-      f = fopen("multi_loss.data","w");
-      for(int i=0; i<N; i++) {
-	fprintf(f,"%.20e\n",Results[i]);
-      }
-      fclose(f);
-      exit(0);
-    }
-    
-    curr_x   = Res.first;
-    curr_err = Res.second;
-    
-    // optimization cycle
-    
-    for(int i=0; i<annealing_cycles; i++){
-        
-        temperature = T_start*exp( i*(log(T_end)-log(T_start))/(double)annealing_cycles);
-        std::cout  << std::endl << "....T = " << wrap_double(temperature,3) << std::endl << std::endl;
-        
-        if (i % cycle == 0 && i > 0){
-            
-            for (int k=0; k<NEVOPARAMS; k++){
-                rejection[k]/=(double)cycle;
-                
-                if (rejection[k] > 0){
-                    dx[k] = dx[k]/(rejection[k]/Target_rejection);
-                    rejection[k]=0.;
-                }
-                else{
-                    dx[k]*=2.;
-                }
-                std::cout  << dx[k] << " ";
-            }
-            std::cout  << std::endl;
-        }
-
-        for (int j=0; j<NEVOPARAMS; j++){
-            
-            ///////////////////////////////
-            if (FIX_VARIABLES==0 || var_fixed[j]==0){
-                
-                
-                
-                // get new value of x[j]
-                double x_hold=x[j];
-                x_tmp = get_new_x(x[j],dx[j]);
-                x[j]=x_tmp;
-                
-                std::cout << wrap_double(x_tmp,10) << " " << wrap_double(j,9) << "\n\n"; 
-                //=======================================
-                //.............................................
-                for(int w=0; w<Nworkers; w++){
-                    un[w]->set_parameter(x_tmp, j);
-                }
-                
-
-		// WRITE OUT PARAMS HERE; then exit.
-
-		std::cout << "Calling multi_loss: i=" << i << " j=" << j << "\n";
-                Res = multi_loss(/* group, */ un, /* CustomQueues, */ Results, Counters, x);
-		std::cout << "Ret from multi_loss: i=" << i << " j=" << j << "\n";
-                std::cout << Res.first << " +- " << Res.second << std::endl;
-                
-                ratio = min(1.,exp(-(Res.first-curr_x)/temperature));
-                r = rand()/(double)(pow(2.,31)-1.);
-                std::cout << r << " vs " << ratio << std::endl;
-                
-                double ALOT=100000000000.;
-                
-                if (Res.first < ALOT)
-                {
-                    ofstream filestr;
-                    
-                    filestr.open ("best_opt_some.txt", ofstream::app);
-                    
-                    // >> i/o operations here <<
-                    filestr << un[0]->get_target() << "," 
-                    << Res.first 
-                    << "," << un[0]->get_parameter(0) 
-                    << "," << un[0]->get_parameter(1) 
-                    << "," << un[0]->get_parameter(2) 
-                    << "," << un[0]->get_parameter(3) 
-                    << "," << un[0]->get_parameter(4) << "," << Res.second << ",\n";
-                    
-                    filestr.close();
-                    
-                    
-                    filestr.open ("max_dist.txt", ofstream::app);
-                    
-                    // >> i/o operations here <<
-                    filestr << max_dist << ",\n";
-                    
-                    filestr.close();
-                    
-                }
-                
-                
-                if (r > ratio){
-                    
-                    std::cout << " "<< (i+1) << ","<< (j) 
-                    <<" "<< (i+1) << " Did not accept " 
-                    << x_tmp << "(" << j << ")" << std::endl;
-                    std::cout << un[0]->get_parameter(0) 
-                    << " " << un[0]->get_parameter(1) 
-                    << " " << un[0]->get_parameter(2) 
-                    << " " << un[0]->get_parameter(3) 
-                    << " " << un[0]->get_parameter(4) << " " << std::endl;
-                    
-                    x[j]=x_hold;
-                    for(int w=0; w<Nworkers; w++){
-                        un[w]->set_parameter(x[j], j);
-                    }
-                    
-                    
-                    //set_parameter(x[j], j);     
-                    rejection[j]+=1.;
-                }
-                
-                else {
-                    
-                    curr_x   = Res.first;
-                    curr_err = Res.second;
-                    x[j] = x_tmp;
-                    
-                    for(int w=0; w<Nworkers; w++){
-                        un[w]->set_parameter(x[j], j);
-                    }
-                    
-                    std::cout << (i+1) << string_wrap((string) " Rejection counts: ", 8) 
-                    << wrap_double(rejection[0],2) << " " 
-                    << wrap_double(rejection[1],7) << " "
-                    << wrap_double(rejection[2],5) << " " 
-                    << wrap_double(rejection[3],9) << " " 
-                    << wrap_double(rejection[4],6) << " " 
-                    << std::endl << std::endl;
-                    
-                    std::cout << " "<< (i+1) <<","<< (j) 
-                    <<" "
-                    << string_wrap((string) "***** Did accept! ", 3) 
-                    << wrap_double(un[0]->get_parameter(0),2) << " "
-                    << wrap_double(un[0]->get_parameter(1),7) << " "
-                    << wrap_double(un[0]->get_parameter(2),5) << " " 
-                    << wrap_double(un[0]->get_parameter(3),9) << " " 
-                    << wrap_double(un[0]->get_parameter(4),6) << " " 
-                    << std::endl << std::endl;
-                    
-                    
-                    
-                }
-                //........................................................ 
-                
-            }
-        }
-        
-    }
-    
-}
-
-
-//================================================
-int
-main(int argc, char* argv[])
-{
-    
-    double params0[6] = {0., 0., 0., 0., 0., 0.2}, target=50., range;
-    string par_names0[6] = {"alpha_i", "alpha_m", "beta", "gamma", "delta", "target"};
-    string par_names1[4] = {"n_epochs", "n_steps", "n_reruns", "range"};
-    string par_names2[5] = {"T_start", "T_end", "Annealing_steps","Target_rejection","Starting_jump"};
-    string par_names3[5] = {"FREEZE_alpha_i", "FREEZE_alpha_m", "FREEZE_beta", "FREEZE_gamma", "FREEZE_delta"};    
-    string par_names4[2] = {"Operation", "Nworkers"};    
-    int params1[4] = {300, 50, 1000, 10};
-    int params3[5] = { 0, 0, 0, 0, 0};
-    
-    //          temperature_start,  temperature_end,  annealing_steps target_rejection  Starting_jump
-    double params2[5] = {1,             0.001,               100,              0.3,           1.5};
-    
-    int verbose_level = 2;
-    const std::string one="one", two="two";
-    static Universe* un[MAXNworkers];
-    // static dispatch_queue_t CustomQueues[MAXNworkers];
-    
-    static double* Results;
-    static int*    Counters;
-    
-    timeval t1, t2;
-    double elapsedTime;
-    // start timer
-    gettimeofday(&t1, NULL);
-    
-    
-    if (argc < 8) {
-        std::cout << "Usage: super_optimizer alpha_i alpha_m beta gamma delta target_innov [n_epochs n_steps n_reruns] [range] [verbose_level]\n"; 
-        std::cout << "         [T_start T_end Annealing_steps Target_rejection Starting_jump]\n"; 
-        std::cout << "         [FREEZE_alpha_i FREEZE_alpha_m FREEZE_beta FREEZE_gamma FREEZE_delta]\n"; 
-        
-        system("pwd");
-        
-        
-        return(1);
-    }
-    else {
-      std::cout << "argc=" << argc << std::endl;
-
-        for (int nArg=0; nArg < argc; nArg++){
-            //std::cout << nArg << " " << argv[nArg] << std::endl;
-            if (nArg > 0 && nArg < 7){
-                params0[nArg-1]= atof(argv[nArg]);
-                std::cout << par_names0[nArg-1] << ": " << params0[nArg-1] <<  std::endl;
-            }
-            if (nArg > 6 && nArg < 11){
-                params1[nArg-7]= atoi(argv[nArg]);
-                std::cout << par_names1[nArg-7] << ": " << params1[nArg-7] <<  std::endl;
-            }
-            if (nArg == 11){
-                verbose_level = atoi(argv[nArg]);
-                std::cout << "verbose level: " << verbose_level <<  std::endl;
-            }
-            if (nArg > 11 && nArg < 17){
-                params2[nArg-12]= atof(argv[nArg]);
-                std::cout << par_names2[nArg-12] << ": " << params2[nArg-12] <<  std::endl;
-            }
-            if (nArg > 16 && nArg < 22){
-                params3[nArg-17]= atof(argv[nArg]);
-                var_fixed[nArg-17]= atof(argv[nArg]);
-                std::cout << par_names3[nArg-17] << ": " << var_fixed[nArg-17] <<  std::endl;
-            }
-            if (nArg == 22 ){
-                operation = *argv[nArg];
-		std::cout << par_names4[0] << ": " << operation <<  std::endl;
-            }
-            if (nArg == 23 ){
-	        Nworkers = atoi(argv[nArg]);
-		std::cout << par_names4[1] << ": " << Nworkers <<  std::endl;
-            }
-        }
-    }
-    
-    /*
-    for target in range(58,1009,50):
-	s = ("%d" % target)
-	print s
-
-	for i in range(15):
-          # Param groups separated by "|" below for documentation. NOTE that | is not used on command line!
-	  os.system("./supe_duper_optimizer |0 0 4 50 -1 "+s+" | 40000 20 1000 2 | 1 | 2. 0.01 100 0.3 2.3 | 1 1 0 0 0")
-    */
-
-    /*   Parameters, re-iterated:
-
-                        "alpha_i", "alpha_m", "beta", "gamma", "delta", "target"};
-    double params0[6] = {0.,        0.,        0.,     0.,      0.,      0.2}, target=50., range;
-
-                    {"n_epochs", "n_steps", "n_reruns", "range"};
-    int params1[4] = {300,        50,       1000,        10};
-
-                       {"T_start",          "T_end",          "Annealing_steps","Target_rejection","Starting_jump"};
-                         temperature_start,  temperature_end,  annealing_steps target_rejection  Starting_jump
-    double params2[5] = {1,                  0.001,            100,            0.3,              1.5};
-
-                     {"FREEZE_alpha_i", "FREEZE_alpha_m", "FREEZE_beta", "FREEZE_gamma", "FREEZE_delta"};    
-    int params3[5] = { 0,                0,                0,             0,              0};
-
-
-    */
-
-    for (int j=0; j<NEVOPARAMS; j++){
-        
-        cout << j << " | " << var_fixed[j] << " (fixed) \n";
-    }
-	
-    target=params0[NEVOPARAMS];
-    range = (double)params1[3];
-	int identify_failed = 0;
-	char* filename= (char *)"movie_graph.txt";
- 	int n_ep=params1[0], n_st=params1[1], n_rep=params1[2];
-    
-    //...............................
-    
-    for(int i=0; i<Nworkers; i++){
-        un[i] = new Universe((char *)filename,n_ep,n_st,
-                             (int)n_rep,
-                             identify_failed, target, i2string(i));
-        // CustomQueues[i] = dispatch_queue_create(i2char(i), NULL);
-    }
-    
-    //...............................
-    // mw: n_rep == n_reruns = # times evolve is done within multi_loss, spread across Nworkers.
-    if(n_rep > 0){
-        
-        Results = new double[n_rep];
-        Counters = new int[n_rep];
-        
-    }else {
-        
-        Results =  NULL;
-        Counters = NULL;
-        std::cout << " Number of reruns should be positive! " <<  std::endl;
-        return 0;
-        
-    }
-    //...............................
-
-    //srand(time(0));
-    //srandomdev();
-    {
-      timeval t; 
-      gettimeofday(&t, NULL);
-      srand(t.tv_usec);
-    }
-    
-    {
-        double r=0;
-        for (int j=0; j<100; j++){
-            
-            
-            
-            r = rand()/(double)(pow(2.,31)-1.);
-            std::cout << r << " ";
-        }
-        std::cout << "\n ";
-    }
-  	//random initiation of starting parameters
-    
-    if (range > 0.){
-        
-        for (int i=0; i < 5; i++){
-            
-            if (params0[i]==-100.){
-                
-                double r1 = (rand()/(double)(pow(2.,31)-1.));
-                double r2 = (rand()/(double)(pow(2.,31)-1.));
-                double sign = 1.;
-                
-                if(r1 > 0.5){
-                    sign=-1.;
-                }
-                
-                params0[i] = sign*r2*range;
-                
-                std::cout << par_names0[i] << ": " << params0[i] <<  std::endl;
-            }
-        }
-        
-    }
-    
-    
-    double T_start=params2[0], T_end=params2[1], Target_rejection=params2[3], starting_jump=params2[4];
-    int Annealing_repeats = (int) params2[2];
-    
-    
-    // dispatch_group_t group = dispatch_group_create();
-    
-    //.............................
-    multi_annealing( /* group, */ un, /* CustomQueues, */ T_start, T_end, Target_rejection, Annealing_repeats, 
-		     starting_jump, Results, Counters, params0, Annealing_repeats);
-    
-    //dispatch_group_wait(group, DISPATCH_TIME_FOREVER);
-    // dispatch_release(group);
-    //.............................
-    
-    
-    // stop timer
-    gettimeofday(&t2, NULL);
-    
-    // compute and print the elapsed time in millisec
-    elapsedTime = (t2.tv_sec - t1.tv_sec) * 1000.0;      // sec to ms
-    elapsedTime += (t2.tv_usec - t1.tv_usec) / 1000.0;   // us to ms
-    elapsedTime /= 1000.;
-    cout << elapsedTime << " seconds " << elapsedTime/60. << " minutes\n\n";
-    
-    //.....................
-    
-    for(int i=0; i<Nworkers; i++){
-        delete un[i];
-    }
-    
-    //....................
-    if(n_rep > 0){
-        
-        delete [] Results;
-        delete [] Counters;
-        
-    }
-    
-    return 0;
-	
-	
-	
-}
-

Deleted: SwiftApps/SciColSim/optirun.swift
===================================================================
--- SwiftApps/SciColSim/optirun.swift	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/optirun.swift	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,41 +0,0 @@
-type file;
-
-app (file outfile, file best, file max) optimize ( string args[], file graph )
-{
-  optimizersh @best @max args stdout=@outfile ;
-}
-
-int minrange=58;
-int maxrange=1009;
-//int maxrange=209;
-int rangeinc=50;
-
-int nreps=2; # 15
-
-//      [alpha_i alpha_m beta gamma delta target_innov
-//      [n_epochs n_steps n_reruns] [range]
-//      [verbose_level]
-//      [T_start T_end Annealing_steps Target_rejection Starting_jump]
-//      [FREEZE_alpha_i FREEZE_alpha_m FREEZE_beta FREEZE_gamma FREEZE_delta]
-
-file graph <"movie_graph.txt">;
-
-foreach target in [minrange:maxrange:rangeinc] {
-  foreach rep in [1:nreps] {
-    file outfile <single_file_mapper; file=@strcat("output/T",target,".R",rep,".out")>;
-    // file errfile <single_file_mapper; file=@strcat("output/T",target,".R",rep,".err")>;
-    file bestfile <single_file_mapper; file=@strcat("output/T",target,".R",rep,".best_opt_some")>;
-    file maxfile <single_file_mapper; file=@strcat("output/T",target,".R",rep,".max_dist")>;
-
-    // string longargs[] = @strcat("0 0 4 50 -1 ",target," 40000 20 1000 2 1 2. 0.01 100 0.3 2.3 1 1 0 0 0");
-
-    string fastargs1[] = ["0", "0", "4", "50", "-1", @strcat(target), "40000", "20", "1000", "2", "1", "2.", "0.01", "100", "0.3", "2.3", "1", "1", "0", "0", "0"];
-    string fastargs2[] = [
-       "0", "0", "4", "50", "-1", @strcat(target),
-       "40000", "20", "1000", "2",
-       "1",
-       "2.", "0.01",  "5", "0.3", "2.3",
-       "1", "1", "0", "0", "0"];
-    (outfile, bestfile, maxfile) = optimize(fastargs2,graph);
-  }
-}

Deleted: SwiftApps/SciColSim/pads.similar.beagle.xml
===================================================================
--- SwiftApps/SciColSim/pads.similar.beagle.xml	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/pads.similar.beagle.xml	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,23 +0,0 @@
-<config>
-  <pool handle="pads">
-    <execution provider="coaster" jobmanager="ssh:pbs" url="login.pads.ci.uchicago.edu" />
-    <filesystem provider="local"/>
-
-     <profile namespace="globus" key="project">CI-CCR000013</profile>
-    <profile namespace="env" key="SWIFT_GEN_SCRIPTS">KEEP</profile>
-
-    <profile namespace="globus" key="ppn">8</profile>
-    <profile namespace="globus" key="jobsPerNode">1</profile>
-    <profile namespace="globus" key="lowOverAllocation">100</profile>
-    <profile namespace="globus" key="highOverAllocation">100</profile>
-    <profile namespace="globus" key="maxTime">3600</profile>
-    <profile namespace="globus" key="maxWallTime">00:02:00</profile>
-    <profile namespace="globus" key="slots">10</profile>
-    <profile namespace="globus" key="nodeGranularity">1</profile>
-    <profile namespace="globus" key="maxNodes">1</profile>
-    <profile namespace="globus" key="queue">fast</profile>
-    <profile namespace="karajan" key="jobThrottle">9.59</profile>
-    <profile namespace="karajan" key="initialScore">10000</profile>
-     <workdirectory>/gpfs/pads/swift/jonmon/Swift/work/pads</workdirectory>
-  </pool>
-</config>

Deleted: SwiftApps/SciColSim/paramtraceall.sh
===================================================================
--- SwiftApps/SciColSim/paramtraceall.sh	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/paramtraceall.sh	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,7 +0,0 @@
-for ti in $(seq 58 70); do
-  echo
-  echo "=======" $ti ; /home/wilde/AndreysOptimizer/src/getparamtrace.sh $ti
-  echo
-done
-
-

Deleted: SwiftApps/SciColSim/plot_active.txt
===================================================================
--- SwiftApps/SciColSim/plot_active.txt	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/plot_active.txt	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,7767 +0,0 @@
-0	1
-10.9679	3
-21.9359	3
-32.9038	4
-43.8717	3
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Deleted: SwiftApps/SciColSim/plot_cumulative.txt
===================================================================
--- SwiftApps/SciColSim/plot_cumulative.txt	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/plot_cumulative.txt	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,7989 +0,0 @@
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Deleted: SwiftApps/SciColSim/plot_ready_jobs.txt
===================================================================
--- SwiftApps/SciColSim/plot_ready_jobs.txt	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/plot_ready_jobs.txt	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,6239 +0,0 @@
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Deleted: SwiftApps/SciColSim/plotit
===================================================================
--- SwiftApps/SciColSim/plotit	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/plotit	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,42 +0,0 @@
-set terminal png enhanced 
-#set term postscript eps enhanced 
-#set terminal svg enhanced size 1000 1000
-#set style line 1 linecolor rgb "blue"
-set output "activeplot.png"
-set nokey
-set xlabel "Time in sec"
-set ylabel "number of active jobs"
-set title "Active jobs"
-plot "plot_active.txt" using 1:2 with line
-
-set output "cumulativeplot-openmp.png"
-set xlabel "Time in seconds"
-set ylabel "number of completed jobs"
-set title "Cumulative SciColSim-openMP jobs"
-plot "plot_cumulative.txt" using 1:($2*24) with lines
-
-set output "cumulativeplot.png"
-set xlabel "Time in seconds"
-set ylabel "number of completed jobs"
-set title "Cumulative jobs"
-plot "plot_cumulative.txt" using 1:2 with lines
-
-set output "scs.png"
-set xlabel "Evolution"
-set ylabel "Value of T"
-set title "SciColSim evolution Results"
-plot "T.data" using 1 with lines 
-
-set output "scs_loss.png"
-set title "SciColSim evolution loss Results"
-set xlabel "Evolution"
-set ylabel "Value of loss(AR)"
-plot "anneal.data" using 1 with lines 
-
-set output "multiloss.png"
-set title "SciColSim evolution loss Results"
-set key auto
-set yrange [0:200]
-set xlabel "Evolution"
-set ylabel "loss"
-plot "multiloss.txt" using 3 with lines title "multiloss mean val",  "multiloss.txt" using ($3+$4) with lines title "+stddev", "multiloss.txt" using ($3-$4) with lines title "-stddev"

Deleted: SwiftApps/SciColSim/sample.swift.output
===================================================================
--- SwiftApps/SciColSim/sample.swift.output	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/sample.swift.output	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,528 +0,0 @@
-Swift 0.93 swift-r5483 cog-r3339
-
-RunID: 20120130-1217-63kot916
-Progress:  time: Mon, 30 Jan 2012 12:17:12 -0600
-multi_loss appCalls=1
-multi_loss: entered: ci=0 cj=0 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,4.0,50.0,-1.0]
-multi_annealing: AR: i=1 ....T = 2.0
-multi_loss: i=1 calling evolve, args=[0,0,4,50,-1,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:17:16 -0600  Checking status:1
-multi_annealing: AR: initial: 124.861101 +- 3.21017
-multi_loss: returning: ci=0 cj=0 r.loss=124.861101 r.sdev=3.21017
-multi_loss appCalls=1
-multi_annealing: AR: 4.017889129124014 2
-multi_loss: entered: ci=1 cj=2 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,4.017889129124014,50.0,-1.0]
-multi_loss: i=1 calling evolve, args=[0,0,4.017889129124014,50,-1,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:17:19 -0600  Checking status:1  Finished successfully:2
-multi_annealing: AR: 124.117401 +- 3.306
-multi_loss: returning: ci=1 cj=2 r.loss=124.117401 r.sdev=3.306
-multi_annealing: AF: best_opt_some.txt: 58.0,124.117401,0.0,0.0,4.017889129124014,50.0,-1.0,3.306
-multi_annealing: AR: 0.3916097778073887 vs 1.0
-math/min: result=1.0
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: Accepting try_x[j], i=1 j=2
-multi_annealing: Accepting try_x[j], i=1 j=2 try_x[j]=4.017889129124014
-multi_annealing: AR: [1][2] Rejection counts: 0.0 0.0 0.0 0.0 0.0
-
-multi_annealing: AR: 1 ***** Did accept! 0.0 0.0 4.017889129124014 50.0 -1.0
-
-multi_loss appCalls=1
-multi_annealing: AR: 48.711529653164106 3
-multi_loss: entered: ci=1 cj=3 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,4.017889129124014,48.711529653164106,-1.0]
-multi_loss: i=1 calling evolve, args=[0,0,4.017889129124014,48.711529653164106,-1,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:17:22 -0600  Checking status:1  Finished successfully:4
-multi_annealing: AR: 125.18271 +- 3.599503
-multi_loss: returning: ci=1 cj=3 r.loss=125.18271 r.sdev=3.599503
-multi_annealing: AF: best_opt_some.txt: 58.0,125.18271,0.0,0.0,4.017889129124014,48.711529653164106,-1.0,3.599503
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: AR: 0.29338527075939624 vs 0.5870445897168992
-math/min: result=0.5870445897168992
-multi_annealing: Accepting try_x[j], i=1 j=3 try_x[j]=48.711529653164106
-multi_annealing: Accepting try_x[j], i=1 j=3
-multi_annealing: AR: 1 ***** Did accept! 0.0 0.0 4.017889129124014 48.711529653164106 -1.0
-
-multi_annealing: AR: [1][3] Rejection counts: 0.0 0.0 0.0 0.0 0.0
-
-multi_loss appCalls=1
-multi_annealing: AR: -0.0033614536445253362 4
-multi_loss: entered: ci=1 cj=4 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,4.017889129124014,48.711529653164106,-0.0033614536445253362]
-multi_loss: i=1 calling evolve, args=[0,0,4.017889129124014,48.711529653164106,-0.0033614536445253362,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:17:26 -0600  Checking status:1  Finished successfully:6
-multi_annealing: AR: 125.933179 +- 3.36805
-multi_loss: returning: ci=1 cj=4 r.loss=125.933179 r.sdev=3.36805
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: AF: best_opt_some.txt: 58.0,125.933179,0.0,0.0,4.017889129124014,48.711529653164106,-0.0033614536445253362,3.36805
-multi_annealing: AR: 0.6647548795660121 vs 0.6871281283507249
-math/min: result=0.6871281283507249
-multi_annealing: Accepting try_x[j], i=1 j=4
-multi_annealing: Accepting try_x[j], i=1 j=4 try_x[j]=-0.0033614536445253362
-multi_annealing: AR: 1 ***** Did accept! 0.0 0.0 4.017889129124014 48.711529653164106 -0.0033614536445253362
-
-multi_annealing: AR: [1][4] Rejection counts: 0.0 0.0 0.0 0.0 0.0
-
-multi_annealing: AR: i=2 ....T = 1.6762121943865207
-multi_loss appCalls=1
-multi_annealing: AR: 5.284753614883805 2
-multi_loss: entered: ci=2 cj=2 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,48.711529653164106,-0.0033614536445253362]
-multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,48.711529653164106,-0.0033614536445253362,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:17:29 -0600  Checking status:1  Finished successfully:8
-multi_annealing: AR: 126.145367 +- 3.119133
-multi_loss: returning: ci=2 cj=2 r.loss=126.145367 r.sdev=3.119133
-multi_annealing: AF: best_opt_some.txt: 58.0,126.145367,0.0,0.0,5.284753614883805,48.711529653164106,-0.0033614536445253362,3.119133
-multi_annealing: AR: 0.8343453829181197 vs 0.8810967932193764
-math/min: result=0.8810967932193764
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: Accepting try_x[j], i=2 j=2 try_x[j]=5.284753614883805
-multi_annealing: Accepting try_x[j], i=2 j=2
-multi_annealing: AR: 2 ***** Did accept! 0.0 0.0 5.284753614883805 48.711529653164106 -0.0033614536445253362
-
-multi_annealing: AR: [2][2] Rejection counts: 0.0 0.0 0.0 0.0 0.0
-
-multi_loss appCalls=1
-multi_annealing: AR: 47.49006278600924 3
-multi_loss: entered: ci=2 cj=3 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,47.49006278600924,-0.0033614536445253362]
-multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,47.49006278600924,-0.0033614536445253362,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:17:33 -0600  Checking status:1  Finished successfully:10
-multi_annealing: AR: 125.831434 +- 3.344003
-multi_loss: returning: ci=2 cj=3 r.loss=125.831434 r.sdev=3.344003
-multi_annealing: AF: best_opt_some.txt: 58.0,125.831434,0.0,0.0,5.284753614883805,47.49006278600924,-0.0033614536445253362,3.344003
-multi_annealing: AR: 0.7961447191161577 vs 1.0
-math/min: result=1.0
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: Accepting try_x[j], i=2 j=3
-multi_annealing: Accepting try_x[j], i=2 j=3 try_x[j]=47.49006278600924
-multi_annealing: AR: 2 ***** Did accept! 0.0 0.0 5.284753614883805 47.49006278600924 -0.0033614536445253362
-
-multi_annealing: AR: [2][3] Rejection counts: 0.0 0.0 0.0 0.0 0.0
-
-multi_loss appCalls=1
-multi_annealing: AR: 0.2830038287653472 4
-multi_loss: entered: ci=2 cj=4 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,47.49006278600924,0.2830038287653472]
-multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,47.49006278600924,0.2830038287653472,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:17:36 -0600  Checking status:1  Finished successfully:12
-multi_annealing: AR: 123.989674 +- 3.372759
-multi_loss: returning: ci=2 cj=4 r.loss=123.989674 r.sdev=3.372759
-multi_annealing: AF: best_opt_some.txt: 58.0,123.989674,0.0,0.0,5.284753614883805,47.49006278600924,0.2830038287653472,3.372759
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: AR: 0.19911890147442235 vs 1.0
-math/min: result=1.0
-multi_annealing: Accepting try_x[j], i=2 j=4
-multi_annealing: Accepting try_x[j], i=2 j=4 try_x[j]=0.2830038287653472
-multi_annealing: AR: [2][4] Rejection counts: 0.0 0.0 0.0 0.0 0.0
-
-multi_annealing: AR: 2 ***** Did accept! 0.0 0.0 5.284753614883805 47.49006278600924 0.2830038287653472
-
-multi_annealing: AR: i=3 ....T = 1.4048436603050374
-multi_loss appCalls=1
-multi_annealing: AR: 6.751519052449282 2
-multi_loss: entered: ci=3 cj=2 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,6.751519052449282,47.49006278600924,0.2830038287653472]
-multi_loss: i=1 calling evolve, args=[0,0,6.751519052449282,47.49006278600924,0.2830038287653472,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:17:39 -0600  Checking status:1  Finished successfully:14
-multi_loss: returning: ci=3 cj=2 r.loss=125.83918 r.sdev=3.276288
-multi_annealing: AR: 125.83918 +- 3.276288
-multi_annealing: AF: best_opt_some.txt: 58.0,125.83918,0.0,0.0,6.751519052449282,47.49006278600924,0.2830038287653472,3.276288
-math/min: result=0.2680663186298088
-multi_annealing: AR: 3,2 3 Did not accept: 6.751519052449282 (2)
-multi_annealing: AR: 0.6852396383450929 vs 0.2680663186298088
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: AR: 0.0 0.0 6.751519052449282 47.49006278600924 0.2830038287653472
-multi_loss appCalls=1
-multi_annealing: AR: 49.01109219848105 3
-multi_loss: entered: ci=3 cj=3 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,49.01109219848105,0.2830038287653472]
-multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,49.01109219848105,0.2830038287653472,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:17:42 -0600  Checking status:1  Finished successfully:16
-multi_loss: returning: ci=3 cj=3 r.loss=126.260409 r.sdev=3.474799
-multi_annealing: AF: best_opt_some.txt: 58.0,126.260409,0.0,0.0,5.284753614883805,49.01109219848105,0.2830038287653472,3.474799
-multi_annealing: AR: 126.260409 +- 3.474799
-math/min: result=0.1986200941793068
-multi_annealing: AR: 0.6989515725257741 vs 0.1986200941793068
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: AR: 3,3 3 Did not accept: 49.01109219848105 (3)
-multi_annealing: AR: 0.0 0.0 5.284753614883805 49.01109219848105 0.2830038287653472
-multi_loss appCalls=1
-multi_annealing: AR: 0.7184988677898592 4
-multi_loss: entered: ci=3 cj=4 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,47.49006278600924,0.7184988677898592]
-multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,47.49006278600924,0.7184988677898592,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:17:45 -0600  Checking status:1  Finished successfully:18
-multi_loss: returning: ci=3 cj=4 r.loss=123.593465 r.sdev=3.286319
-multi_annealing: AR: 123.593465 +- 3.286319
-multi_annealing: AF: best_opt_some.txt: 58.0,123.593465,0.0,0.0,5.284753614883805,47.49006278600924,0.7184988677898592,3.286319
-multi_annealing: AR: 0.10970841947076293 vs 1.0
-multi_annealing: AF: max_dist.txt - tbd
-math/min: result=1.0
-multi_annealing: Accepting try_x[j], i=3 j=4
-multi_annealing: Accepting try_x[j], i=3 j=4 try_x[j]=0.7184988677898592
-multi_annealing: AR: 3 ***** Did accept! 0.0 0.0 5.284753614883805 47.49006278600924 0.7184988677898592
-
-multi_annealing: AR: [3][4] Rejection counts: 0.0 0.0 1.0 1.0 0.0
-
-multi_annealing: AR: i=4 ....T = 1.1774080373049494
-multi_loss appCalls=1
-multi_annealing: AR: 4.4255186274220115 2
-multi_loss: entered: ci=4 cj=2 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,4.4255186274220115,47.49006278600924,0.7184988677898592]
-multi_loss: i=1 calling evolve, args=[0,0,4.4255186274220115,47.49006278600924,0.7184988677898592,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:17:49 -0600  Checking status:1  Finished successfully:20
-multi_loss: returning: ci=4 cj=2 r.loss=126.444622 r.sdev=3.25147
-multi_annealing: AR: 126.444622 +- 3.25147
-multi_annealing: AF: best_opt_some.txt: 58.0,126.444622,0.0,0.0,4.4255186274220115,47.49006278600924,0.7184988677898592,3.25147
-multi_annealing: AR: 0.9535428038358379 vs 0.08878355131476454
-math/min: result=0.08878355131476454
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: AR: 4,2 4 Did not accept: 4.4255186274220115 (2)
-multi_annealing: AR: 0.0 0.0 4.4255186274220115 47.49006278600924 0.7184988677898592
-multi_loss appCalls=1
-multi_annealing: AR: 46.92686166375319 3
-multi_loss: entered: ci=4 cj=3 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,46.92686166375319,0.7184988677898592]
-multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,46.92686166375319,0.7184988677898592,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:17:52 -0600  Checking status:1  Finished successfully:22
-multi_annealing: AR: 124.963375 +- 3.41926
-multi_loss: returning: ci=4 cj=3 r.loss=124.963375 r.sdev=3.41926
-multi_annealing: AF: best_opt_some.txt: 58.0,124.963375,0.0,0.0,5.284753614883805,46.92686166375319,0.7184988677898592,3.41926
-multi_annealing: AR: 0.4500207377949753 vs 0.312392025102438
-math/min: result=0.312392025102438
-multi_annealing: AR: 4,3 4 Did not accept: 46.92686166375319 (3)
-multi_annealing: AR: 0.0 0.0 5.284753614883805 46.92686166375319 0.7184988677898592
-multi_annealing: AF: max_dist.txt - tbd
-multi_loss appCalls=1
-multi_annealing: AR: -0.3320316742658884 4
-multi_loss: entered: ci=4 cj=4 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,47.49006278600924,-0.3320316742658884]
-multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,47.49006278600924,-0.3320316742658884,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:17:55 -0600  Checking status:1  Finished successfully:24
-multi_annealing: AR: 125.407828 +- 3.018498
-multi_loss: returning: ci=4 cj=4 r.loss=125.407828 r.sdev=3.018498
-multi_annealing: AF: best_opt_some.txt: 58.0,125.407828,0.0,0.0,5.284753614883805,47.49006278600924,-0.3320316742658884,3.018498
-multi_annealing: AR: 0.8555641933973892 vs 0.21417097212905944
-math/min: result=0.21417097212905944
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: AR: 0.0 0.0 5.284753614883805 47.49006278600924 -0.3320316742658884
-multi_annealing: AR: 4,4 4 Did not accept: -0.3320316742658884 (4)
-multi_annealing: AR: i=5 ....T = 0.9867928549496278
-multi_loss appCalls=1
-multi_annealing: AR: 6.833327620921641 2
-multi_loss: entered: ci=5 cj=2 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,6.833327620921641,47.49006278600924,0.7184988677898592]
-multi_loss: i=1 calling evolve, args=[0,0,6.833327620921641,47.49006278600924,0.7184988677898592,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:17:58 -0600  Checking status:1  Finished successfully:26
-multi_annealing: AR: 125.250487 +- 3.078819
-multi_loss: returning: ci=5 cj=2 r.loss=125.250487 r.sdev=3.078819
-multi_annealing: AF: best_opt_some.txt: 58.0,125.250487,0.0,0.0,6.833327620921641,47.49006278600924,0.7184988677898592,3.078819
-multi_annealing: AR: 0.44288167684075663 vs 0.18652324111077032
-math/min: result=0.18652324111077032
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: AR: 5,2 5 Did not accept: 6.833327620921641 (2)
-multi_annealing: AR: 0.0 0.0 6.833327620921641 47.49006278600924 0.7184988677898592
-multi_loss appCalls=1
-multi_annealing: AR: 46.49027442733761 3
-multi_loss: entered: ci=5 cj=3 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,46.49027442733761,0.7184988677898592]
-multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,46.49027442733761,0.7184988677898592,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:18:01 -0600  Checking status:1  Finished successfully:28
-multi_annealing: AR: 124.663101 +- 3.485893
-multi_loss: returning: ci=5 cj=3 r.loss=124.663101 r.sdev=3.485893
-multi_annealing: AF: best_opt_some.txt: 58.0,124.663101,0.0,0.0,5.284753614883805,46.49027442733761,0.7184988677898592,3.485893
-multi_annealing: AR: 0.42913823795768913 vs 0.33825612299835406
-math/min: result=0.33825612299835406
-multi_annealing: AR: 0.0 0.0 5.284753614883805 46.49027442733761 0.7184988677898592
-multi_annealing: AR: 5,3 5 Did not accept: 46.49027442733761 (3)
-multi_annealing: AF: max_dist.txt - tbd
-multi_loss appCalls=1
-multi_annealing: AR: 2.2333217492423234 4
-multi_loss: entered: ci=5 cj=4 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,47.49006278600924,2.2333217492423234]
-multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,47.49006278600924,2.2333217492423234,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:18:05 -0600  Checking status:1  Finished successfully:30
-multi_annealing: AR: 125.856136 +- 3.415745
-multi_loss: returning: ci=5 cj=4 r.loss=125.856136 r.sdev=3.415745
-multi_annealing: AF: best_opt_some.txt: 58.0,125.856136,0.0,0.0,5.284753614883805,47.49006278600924,2.2333217492423234,3.415745
-multi_annealing: AR: 0.5684553851797784 vs 0.10096772364295076
-math/min: result=0.10096772364295076
-multi_annealing: AR: 5,4 5 Did not accept: 2.2333217492423234 (4)
-multi_annealing: AR: 0.0 0.0 5.284753614883805 47.49006278600924 2.2333217492423234
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: AR: i=6 ....T = 0.8270371084000275
-multi_loss appCalls=1
-multi_annealing: AR: 3.928981105935937 2
-multi_loss: entered: ci=6 cj=2 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,3.928981105935937,47.49006278600924,0.7184988677898592]
-multi_loss: i=1 calling evolve, args=[0,0,3.928981105935937,47.49006278600924,0.7184988677898592,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:18:08 -0600  Checking status:1  Finished successfully:32
-multi_annealing: AR: 125.944813 +- 3.343539
-multi_loss: returning: ci=6 cj=2 r.loss=125.944813 r.sdev=3.343539
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: AR: 0.0737231873586417 vs 0.05824491156028037
-multi_annealing: AF: best_opt_some.txt: 58.0,125.944813,0.0,0.0,3.928981105935937,47.49006278600924,0.7184988677898592,3.343539
-multi_annealing: AR: 6,2 6 Did not accept: 3.928981105935937 (2)
-math/min: result=0.05824491156028037
-multi_annealing: AR: 0.0 0.0 3.928981105935937 47.49006278600924 0.7184988677898592
-multi_loss appCalls=1
-multi_annealing: AR: 47.456980868107266 3
-multi_loss: entered: ci=6 cj=3 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,47.456980868107266,0.7184988677898592]
-multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,47.456980868107266,0.7184988677898592,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:18:11 -0600  Checking status:1  Finished successfully:34
-multi_annealing: AR: 126.604462 +- 3.183016
-multi_loss: returning: ci=6 cj=3 r.loss=126.604462 r.sdev=3.183016
-multi_annealing: AR: 0.5487345599809303 vs 0.02623387861162342
-math/min: result=0.02623387861162342
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: AF: best_opt_some.txt: 58.0,126.604462,0.0,0.0,5.284753614883805,47.456980868107266,0.7184988677898592,3.183016
-multi_annealing: AR: 6,3 6 Did not accept: 47.456980868107266 (3)
-multi_annealing: AR: 0.0 0.0 5.284753614883805 47.456980868107266 0.7184988677898592
-multi_loss appCalls=1
-multi_loss: entered: ci=6 cj=4 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,47.49006278600924,-1.5302000464747076]
-multi_annealing: AR: -1.5302000464747076 4
-multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,47.49006278600924,-1.5302000464747076,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:18:12 -0600  Active:1  Finished successfully:36
-Progress:  time: Mon, 30 Jan 2012 12:18:14 -0600  Checking status:1  Finished successfully:36
-multi_loss: returning: ci=6 cj=4 r.loss=125.062511 r.sdev=3.22833
-multi_annealing: AR: 125.062511 +- 3.22833
-multi_annealing: AF: best_opt_some.txt: 58.0,125.062511,0.0,0.0,5.284753614883805,47.49006278600924,-1.5302000464747076,3.22833
-multi_annealing: AR: 0.4295446121391554 vs 0.16926736066405584
-multi_annealing: AF: max_dist.txt - tbd
-math/min: result=0.16926736066405584
-multi_annealing: AR: 6,4 6 Did not accept: -1.5302000464747076 (4)
-multi_annealing: AR: 0.0 0.0 5.284753614883805 47.49006278600924 -1.5302000464747076
-multi_annealing: AR: i=7 ....T = 0.6931448431551464
-multi_loss appCalls=1
-multi_annealing: AR: 5.698890855198198 2
-multi_loss: entered: ci=7 cj=2 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.698890855198198,47.49006278600924,0.7184988677898592]
-multi_loss: i=1 calling evolve, args=[0,0,5.698890855198198,47.49006278600924,0.7184988677898592,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:18:17 -0600  Checking status:1  Finished successfully:38
-multi_annealing: AR: 124.92233 +- 3.519439
-multi_loss: returning: ci=7 cj=2 r.loss=124.92233 r.sdev=3.519439
-multi_annealing: AF: best_opt_some.txt: 58.0,124.92233,0.0,0.0,5.698890855198198,47.49006278600924,0.7184988677898592,3.519439
-multi_annealing: AR: 0.26061833782383614 vs 0.14702488652213752
-multi_annealing: AF: max_dist.txt - tbd
-math/min: result=0.14702488652213752
-multi_annealing: AR: 7,2 7 Did not accept: 5.698890855198198 (2)
-multi_annealing: AR: 0.0 0.0 5.698890855198198 47.49006278600924 0.7184988677898592
-multi_loss appCalls=1
-multi_annealing: AR: 48.00451051760781 3
-multi_loss: entered: ci=7 cj=3 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,48.00451051760781,0.7184988677898592]
-multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,48.00451051760781,0.7184988677898592,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:18:21 -0600  Checking status:1  Finished successfully:40
-multi_annealing: AR: 123.700997 +- 3.061641
-multi_loss: returning: ci=7 cj=3 r.loss=123.700997 r.sdev=3.061641
-multi_annealing: AF: best_opt_some.txt: 58.0,123.700997,0.0,0.0,5.284753614883805,48.00451051760781,0.7184988677898592,3.061641
-multi_annealing: AR: 0.04383216320955852 vs 0.8562983650405412
-math/min: result=0.8562983650405412
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: Accepting try_x[j], i=7 j=3
-multi_annealing: Accepting try_x[j], i=7 j=3 try_x[j]=48.00451051760781
-multi_annealing: AR: 7 ***** Did accept! 0.0 0.0 5.284753614883805 48.00451051760781 0.7184988677898592
-
-multi_annealing: AR: [7][3] Rejection counts: 0.0 0.0 5.0 4.0 3.0
-
-multi_loss appCalls=1
-multi_annealing: AR: -0.41452399887346414 4
-multi_loss: entered: ci=7 cj=4 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,48.00451051760781,-0.41452399887346414]
-multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,48.00451051760781,-0.41452399887346414,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:18:24 -0600  Checking status:1  Finished successfully:42
-multi_annealing: AR: 122.427501 +- 3.484433
-multi_loss: returning: ci=7 cj=4 r.loss=122.427501 r.sdev=3.484433
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: AR: 0.6794911655574871 vs 1.0
-math/min: result=1.0
-multi_annealing: AF: best_opt_some.txt: 58.0,122.427501,0.0,0.0,5.284753614883805,48.00451051760781,-0.41452399887346414,3.484433
-multi_annealing: Accepting try_x[j], i=7 j=4
-multi_annealing: Accepting try_x[j], i=7 j=4 try_x[j]=-0.41452399887346414
-multi_annealing: AR: [7][4] Rejection counts: 0.0 0.0 5.0 4.0 3.0
-
-multi_annealing: AR: 7 ***** Did accept! 0.0 0.0 5.284753614883805 48.00451051760781 -0.41452399887346414
-
-multi_annealing: AR: i=8 ....T = 0.5809289192863943
-multi_loss appCalls=1
-multi_annealing: AR: 4.228373615739721 2
-multi_loss: entered: ci=8 cj=2 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,4.228373615739721,48.00451051760781,-0.41452399887346414]
-multi_loss: i=1 calling evolve, args=[0,0,4.228373615739721,48.00451051760781,-0.41452399887346414,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:18:27 -0600  Checking status:1  Finished successfully:44
-multi_loss: returning: ci=8 cj=2 r.loss=125.825434 r.sdev=3.06081
-multi_annealing: AR: 125.825434 +- 3.06081
-multi_annealing: AF: best_opt_some.txt: 58.0,125.825434,0.0,0.0,4.228373615739721,48.00451051760781,-0.41452399887346414,3.06081
-multi_annealing: AR: 0.43307949429152304 vs 0.0028823847709179276
-math/min: result=0.0028823847709179276
-multi_annealing: AR: 0.0 0.0 4.228373615739721 48.00451051760781 -0.41452399887346414
-multi_annealing: AR: 8,2 8 Did not accept: 4.228373615739721 (2)
-multi_annealing: AF: max_dist.txt - tbd
-multi_loss appCalls=1
-multi_annealing: AR: 46.894163840589215 3
-multi_loss: entered: ci=8 cj=3 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,46.894163840589215,-0.41452399887346414]
-multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,46.894163840589215,-0.41452399887346414,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:18:30 -0600  Checking status:1  Finished successfully:46
-multi_annealing: AR: 124.871719 +- 3.122439
-multi_loss: returning: ci=8 cj=3 r.loss=124.871719 r.sdev=3.122439
-multi_annealing: AF: best_opt_some.txt: 58.0,124.871719,0.0,0.0,5.284753614883805,46.894163840589215,-0.41452399887346414,3.122439
-multi_annealing: AR: 0.34644774801935974 vs 0.014884566500466245
-math/min: result=0.014884566500466245
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: AR: 0.0 0.0 5.284753614883805 46.894163840589215 -0.41452399887346414
-multi_annealing: AR: 8,3 8 Did not accept: 46.894163840589215 (3)
-multi_loss appCalls=1
-multi_annealing: AR: -2.2286308752011443 4
-multi_loss: entered: ci=8 cj=4 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,48.00451051760781,-2.2286308752011443]
-multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,48.00451051760781,-2.2286308752011443,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:18:33 -0600  Checking status:1  Finished successfully:48
-multi_annealing: AR: 125.200576 +- 3.137099
-multi_loss: returning: ci=8 cj=4 r.loss=125.200576 r.sdev=3.137099
-multi_annealing: AF: best_opt_some.txt: 58.0,125.200576,0.0,0.0,5.284753614883805,48.00451051760781,-2.2286308752011443,3.137099
-multi_annealing: AR: 0.47163119315783886 vs 0.008450593550370632
-math/min: result=0.008450593550370632
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: AR: 8,4 8 Did not accept: -2.2286308752011443 (4)
-multi_annealing: AR: 0.0 0.0 5.284753614883805 48.00451051760781 -2.2286308752011443
-multi_annealing: AR: i=9 ....T = 0.48688006928981853
-multi_loss appCalls=1
-multi_annealing: AR: 5.695013169826046 2
-multi_loss: entered: ci=9 cj=2 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.695013169826046,48.00451051760781,-0.41452399887346414]
-multi_loss: i=1 calling evolve, args=[0,0,5.695013169826046,48.00451051760781,-0.41452399887346414,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:18:37 -0600  Checking status:1  Finished successfully:50
-multi_annealing: AR: 125.017227 +- 3.237815
-multi_loss: returning: ci=9 cj=2 r.loss=125.017227 r.sdev=3.237815
-multi_annealing: AF: best_opt_some.txt: 58.0,125.017227,0.0,0.0,5.695013169826046,48.00451051760781,-0.41452399887346414,3.237815
-multi_annealing: AR: 0.17076495981302242 vs 0.004897539166396153
-math/min: result=0.004897539166396153
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: AR: 9,2 9 Did not accept: 5.695013169826046 (2)
-multi_annealing: AR: 0.0 0.0 5.695013169826046 48.00451051760781 -0.41452399887346414
-multi_loss appCalls=1
-multi_annealing: AR: 48.00679484464189 3
-multi_loss: entered: ci=9 cj=3 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,48.00679484464189,-0.41452399887346414]
-multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,48.00679484464189,-0.41452399887346414,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:18:40 -0600  Checking status:1  Finished successfully:52
-multi_annealing: AR: 125.213924 +- 3.333561
-multi_loss: returning: ci=9 cj=3 r.loss=125.213924 r.sdev=3.333561
-multi_annealing: AF: best_opt_some.txt: 58.0,125.213924,0.0,0.0,5.284753614883805,48.00679484464189,-0.41452399887346414,3.333561
-multi_annealing: AR: 0.15928070563724706 vs 0.003269830345111401
-math/min: result=0.003269830345111401
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: AR: 0.0 0.0 5.284753614883805 48.00679484464189 -0.41452399887346414
-multi_annealing: AR: 9,3 9 Did not accept: 48.00679484464189 (3)
-multi_loss appCalls=1
-multi_annealing: AR: -0.7507963173758503 4
-multi_loss: entered: ci=9 cj=4 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,48.00451051760781,-0.7507963173758503]
-multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,48.00451051760781,-0.7507963173758503,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:18:42 -0600  Active:1  Finished successfully:54
-multi_loss: returning: ci=9 cj=4 r.loss=125.856406 r.sdev=3.764608
-multi_annealing: AR: 125.856406 +- 3.764608
-multi_annealing: AF: best_opt_some.txt: 58.0,125.856406,0.0,0.0,5.284753614883805,48.00451051760781,-0.7507963173758503,3.764608
-math/min: result=8.738454465360378E-4
-multi_annealing: AR: 0.061628429129563234 vs 8.738454465360378E-4
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: AR: 9,4 9 Did not accept: -0.7507963173758503 (4)
-multi_annealing: AR: 0.0 0.0 5.284753614883805 48.00451051760781 -0.7507963173758503
-multi_annealing: AR: i=10 ....T = 0.408057154673674
-multi_loss appCalls=1
-multi_annealing: AR: 3.29267604969456 2
-multi_loss: entered: ci=10 cj=2 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,3.29267604969456,48.00451051760781,-0.41452399887346414]
-multi_loss: i=1 calling evolve, args=[0,0,3.29267604969456,48.00451051760781,-0.41452399887346414,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:18:46 -0600  Checking status:1  Finished successfully:56
-multi_annealing: AR: 125.360364 +- 3.317013
-multi_loss: returning: ci=10 cj=2 r.loss=125.360364 r.sdev=3.317013
-multi_annealing: AF: best_opt_some.txt: 58.0,125.360364,0.0,0.0,3.29267604969456,48.00451051760781,-0.41452399887346414,3.317013
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: AR: 0.27027335203696023 vs 7.56065260977875E-4
-math/min: result=7.56065260977875E-4
-multi_annealing: AR: 0.0 0.0 3.29267604969456 48.00451051760781 -0.41452399887346414
-multi_annealing: AR: 10,2 10 Did not accept: 3.29267604969456 (2)
-multi_loss appCalls=1
-multi_annealing: AR: 46.275556353155956 3
-multi_loss: entered: ci=10 cj=3 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,46.275556353155956,-0.41452399887346414]
-multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,46.275556353155956,-0.41452399887346414,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:18:49 -0600  Checking status:1  Finished successfully:58
-multi_annealing: AR: 126.434478 +- 3.246686
-multi_loss: returning: ci=10 cj=3 r.loss=126.434478 r.sdev=3.246686
-multi_annealing: AF: best_opt_some.txt: 58.0,126.434478,0.0,0.0,5.284753614883805,46.275556353155956,-0.41452399887346414,3.246686
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: AR: 0.14920802306232583 vs 5.4372803009239825E-5
-math/min: result=5.4372803009239825E-5
-multi_annealing: AR: 10,3 10 Did not accept: 46.275556353155956 (3)
-multi_annealing: AR: 0.0 0.0 5.284753614883805 46.275556353155956 -0.41452399887346414
-multi_loss appCalls=1
-multi_annealing: AR: 1.5046793086101826 4
-multi_loss: entered: ci=10 cj=4 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,48.00451051760781,1.5046793086101826]
-multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,48.00451051760781,1.5046793086101826,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:18:53 -0600  Checking status:1  Finished successfully:60
-multi_loss: returning: ci=10 cj=4 r.loss=125.785376 r.sdev=3.346594
-multi_annealing: AR: 125.785376 +- 3.346594
-multi_annealing: AF: best_opt_some.txt: 58.0,125.785376,0.0,0.0,5.284753614883805,48.00451051760781,1.5046793086101826,3.346594
-multi_annealing: AR: 0.6250655950503741 vs 2.668208671281028E-4
-math/min: result=2.668208671281028E-4
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: AR: 0.0 0.0 5.284753614883805 48.00451051760781 1.5046793086101826
-multi_annealing: AR: 10,4 10 Did not accept: 1.5046793086101826 (4)
-multi_annealing: new cycle at i=11
-multi_annealing: AR: New cycle at 11: prev dx[0-4]=[2.3 2.3 2.3 2.3 2.3]
-multi_annealing: AR: i=11 ....T = 0.34199518933533946
-multi_annealing: AR: New cycle at 11: dx[0-4]=[4.6 4.6 0.8624999999999998 0.9857142857142855 1.15]
-multi_loss appCalls=1
-multi_annealing: AR: 3.791705563626845 2
-multi_loss: entered: ci=11 cj=2 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,3.791705563626845,48.00451051760781,-0.41452399887346414]
-multi_loss: i=1 calling evolve, args=[0,0,3.791705563626845,48.00451051760781,-0.41452399887346414,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:18:56 -0600  Checking status:1  Finished successfully:62
-multi_annealing: AF: max_dist.txt - tbd
-multi_loss: returning: ci=11 cj=2 r.loss=124.596526 r.sdev=3.176561
-multi_annealing: AR: 124.596526 +- 3.176561
-multi_annealing: AF: best_opt_some.txt: 58.0,124.596526,0.0,0.0,3.791705563626845,48.00451051760781,-0.41452399887346414,3.176561
-multi_annealing: AR: 0.9715442940965933 vs 0.001760306082349071
-math/min: result=0.001760306082349071
-multi_annealing: AR: 0.0 0.0 3.791705563626845 48.00451051760781 -0.41452399887346414
-multi_annealing: AR: 11,2 11 Did not accept: 3.791705563626845 (2)
-multi_loss appCalls=1
-multi_annealing: AR: 49.92607616034099 3
-multi_loss: entered: ci=11 cj=3 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,49.92607616034099,-0.41452399887346414]
-multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,49.92607616034099,-0.41452399887346414,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:18:59 -0600  Checking status:1  Finished successfully:64
-multi_annealing: AR: 124.00716 +- 3.111738
-multi_loss: returning: ci=11 cj=3 r.loss=124.00716 r.sdev=3.111738
-multi_annealing: AF: best_opt_some.txt: 58.0,124.00716,0.0,0.0,5.284753614883805,49.92607616034099,-0.41452399887346414,3.111738
-multi_annealing: AR: 0.38818246796613753 vs 0.009863137695174688
-math/min: result=0.009863137695174688
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: AR: 11,3 11 Did not accept: 49.92607616034099 (3)
-multi_annealing: AR: 0.0 0.0 5.284753614883805 49.92607616034099 -0.41452399887346414
-multi_loss appCalls=1
-multi_annealing: AR: -0.7771568808805009 4
-multi_loss: entered: ci=11 cj=4 target_innov=58.0 evolve_reruns=100 x=[0.0,0.0,5.284753614883805,48.00451051760781,-0.7771568808805009]
-multi_loss: i=1 calling evolve, args=[0,0,5.284753614883805,48.00451051760781,-0.7771568808805009,58,40000,20,100,2,1,2.,0.01,2,0.3,2.3,1,1,0,0,0,m,4,1234567]
-Progress:  time: Mon, 30 Jan 2012 12:19:02 -0600  Checking status:1  Finished successfully:66
-multi_annealing: AR: 123.567412 +- 3.66859
-multi_loss: returning: ci=11 cj=4 r.loss=123.567412 r.sdev=3.66859
-multi_annealing: AF: best_opt_some.txt: 58.0,123.567412,0.0,0.0,5.284753614883805,48.00451051760781,-0.7771568808805009,3.66859
-multi_annealing: AR: 0.5338319788181374 vs 0.03568160518248377
-math/min: result=0.03568160518248377
-multi_annealing: AF: max_dist.txt - tbd
-multi_annealing: AR: 11,4 11 Did not accept: -0.7771568808805009 (4)
-multi_annealing: AR: 0.0 0.0 5.284753614883805 48.00451051760781 -0.7771568808805009
-Progress:  time: Mon, 30 Jan 2012 12:19:12 -0600  Finished successfully:68
-No events in 10s.
-
-Registered futures:
-string[] args  Closed, 24 elements, no listeners
-string[] args  Closed, 24 elements, no listeners
-string[] args  Closed, 24 elements, no listeners
-string[] args  Closed, 24 elements, no listeners
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-Waiting threads:
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-No events in 10s.
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-Registered futures:
-string[] args  Closed, 24 elements, no listeners
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-Registered futures:
-string[] args  Closed, 24 elements, no listeners
-string[] args  Closed, 24 elements, no listeners
-string[] args  Closed, 24 elements, no listeners
-string[] args  Closed, 24 elements, no listeners
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-Waiting threads:
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-Progress:  time: Mon, 30 Jan 2012 12:19:42 -0600  Finished successfully:68

Deleted: SwiftApps/SciColSim/sample.testopt.py.output
===================================================================
--- SwiftApps/SciColSim/sample.testopt.py.output	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/sample.testopt.py.output	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,243 +0,0 @@
-
-**** Calling optimizer: OMP_NUM_THREADS=4 ./openmp-optimizer 0 0 4 50 -1 58 40000 20 100 2 1 2. 0.01 5 0.3 2.3 1 1 0 0 0 n 4 1234567
-
-alpha_i: 0
-alpha_m: 0
-beta: 4
-gamma: 50
-delta: -1
-target: 58
-n_epochs: 40000
-n_steps: 20
-n_reruns: 100
-range: 2
-verbose level: 1
-T_start: 2
-T_end: 0.01
-Annealing_steps: 5
-Target_rejection: 0.3
-Starting_jump: 2.3
-FREEZE_alpha_i: 1
-FREEZE_alpha_m: 1
-FREEZE_beta: 0
-FREEZE_gamma: 0
-FREEZE_delta: 0
-Operation: n
-Nworkers: 4
-initSeed: 1234567
-0 | 1 (fixed) 
-1 | 1 (fixed) 
-2 | 0 (fixed) 
-3 | 0 (fixed) 
-4 | 0 (fixed) 
-0.742788 0.631704 0.118309 0.922271 0.141282 0.80831 0.961468 0.363704 0.665483 0.683465 0.771216 0.267925 0.224677 0.153439 0.23455 0.816502 0.718347 0.371612 0.948727 0.404154 0.600908 0.766305 0.493219 0.82445 0.100233 0.672304 0.157299 0.53804 0.6794 0.57498 0.758357 0.422188 0.206684 0.876666 0.344459 0.347966 0.684976 0.305927 0.71167 0.350459 0.989392 0.482885 0.618384 0.214069 0.636325 0.852934 0.0305713 0.354672 0.224546 0.979299 0.758825 0.825454 0.745604 0.252044 0.649904 0.845837 0.924348 0.807203 0.383877 0.603748 0.382183 0.142234 0.0259353 0.588867 0.0189005 0.370394 0.936833 0.703877 0.676321 0.648502 0.0543356 0.665713 0.131387 0.672719 0.879782 0.767712 0.525653 0.910353 0.122384 0.750199 0.889652 0.881209 0.575653 0.635256 0.133253 0.225557 0.481092 0.0576008 0.0327595 0.864969 0.661348 0.414942 0.00720323 0.687284 0.00380882 0.0261037 0.0576777 0.940641 0.72998 0.733998 
-multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
-multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
-multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
-multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
-multi_loss(N=100, target=58) elapsed time: 2.9134 seconds 0.0485567 minutes
-
-126.43 +- 3.11244
-
-....T = 2
-
-3.77342 2
-
-multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
-multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
-multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
-multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
-multi_loss(N=100, target=58) elapsed time: 2.90873 seconds 0.0484788 minutes
-
-126.523 +- 3.32417
-0.82704 vs 0.954153
-1 Rejection counts: 0 0 0 0 0 
-
- 1,2 ***** Did accept! 0 0 3.77342 50 -1 
-
-52.2335 3
-
-multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
-multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
-multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
-multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
-multi_loss(N=100, target=58) elapsed time: 2.93802 seconds 0.0489671 minutes
-
-130.173 +- 3.67201
-0.188468 vs 0.161259
- 1,3 1 Did not accept 52.2335(3)
-0 0 3.77342 52.2335 -1 
--2.00667 4
-
-multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
-multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
-multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
-multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
-multi_loss(N=100, target=58) elapsed time: 2.95626 seconds 0.049271 minutes
-
-129.554 +- 3.1612
-0.88704 vs 0.219698
- 1,4 1 Did not accept -2.00667(4)
-0 0 3.77342 50 -2.00667 
-
-....T = 0.693145
-
-4.07035 2
-
-multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
-multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
-multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
-multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
-multi_loss(N=100, target=58) elapsed time: 2.99097 seconds 0.0498495 minutes
-
-130.603 +- 3.25174
-0.0501227 vs 0.00277796
- 2,2 2 Did not accept 4.07035(2)
-0 0 4.07035 50 -1 
-48.5261 3
-
-multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
-multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
-multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
-multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
-multi_loss(N=100, target=58) elapsed time: 2.94022 seconds 0.0490036 minutes
-
-128.508 +- 3.34913
-0.685387 vs 0.0571204
- 2,3 2 Did not accept 48.5261(3)
-0 0 3.77342 48.5261 -1 
--2.2438 4
-
-multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
-multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
-multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
-multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
-multi_loss(N=100, target=58) elapsed time: 2.95557 seconds 0.0492594 minutes
-
-128.768 +- 3.41509
-0.579097 vs 0.0392132
- 2,4 2 Did not accept -2.2438(4)
-0 0 3.77342 50 -2.2438 
-
-....T = 0.240225
-
-2.54662 2
-
-multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
-multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
-multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
-multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
-multi_loss(N=100, target=58) elapsed time: 3.02841 seconds 0.0504735 minutes
-
-127.262 +- 3.06273
-0.274094 vs 0.0462924
- 3,2 3 Did not accept 2.54662(2)
-0 0 2.54662 50 -1 
-51.7319 3
-
-multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
-multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
-multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
-multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
-multi_loss(N=100, target=58) elapsed time: 3.00121 seconds 0.0500201 minutes
-
-128.927 +- 3.75415
-0.00641465 vs 4.52253e-05
- 3,3 3 Did not accept 51.7319(3)
-0 0 3.77342 51.7319 -1 
-0.232896 4
-
-multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
-multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
-multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
-multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
-multi_loss(N=100, target=58) elapsed time: 2.98499 seconds 0.0497499 minutes
-
-128.55 +- 3.56206
-0.941162 vs 0.000216463
- 3,4 3 Did not accept 0.232896(4)
-0 0 3.77342 50 0.232896 
-
-....T = 0.0832553
-
-4.37082 2
-
-multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
-multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
-multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
-multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
-multi_loss(N=100, target=58) elapsed time: 2.95775 seconds 0.0492958 minutes
-
-128.816 +- 3.45615
-0.803355 vs 1.09972e-12
- 4,2 4 Did not accept 4.37082(2)
-0 0 4.37082 50 -1 
-49.3468 3
-
-multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
-multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
-multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
-multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
-multi_loss(N=100, target=58) elapsed time: 2.9872 seconds 0.0497866 minutes
-
-128.915 +- 3.17434
-0.126163 vs 3.35758e-13
- 4,3 4 Did not accept 49.3468(3)
-0 0 3.77342 49.3468 -1 
--0.0551126 4
-
-multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
-multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
-multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
-multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
-multi_loss(N=100, target=58) elapsed time: 2.93849 seconds 0.0489748 minutes
-
-125.1 +- 3.35383
-0.17234 vs 1
-4 Rejection counts: 0 0 3 4 3 
-
- 4,4 ***** Did accept! 0 0 3.77342 50 -0.0551126 
-
-
-....T = 0.028854
-
-5.90757 2
-
-multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
-multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
-multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
-multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
-multi_loss(N=100, target=58) elapsed time: 3.04963 seconds 0.0508272 minutes
-
-131.481 +- 3.49648
-0.318332 vs 9.27549e-97
- 5,2 5 Did not accept 5.90757(2)
-0 0 5.90757 50 -0.0551126 
-49.5818 3
-
-multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
-multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
-multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
-multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
-multi_loss(N=100, target=58) elapsed time: 3.0051 seconds 0.050085 minutes
-
-131.054 +- 3.3899
-0.854774 vs 2.47481e-90
- 5,3 5 Did not accept 49.5818(3)
-0 0 3.77342 49.5818 -0.0551126 
--1.93165 4
-
-multi_loss: Calling evolve_to_target_and_save i=0 N=100 step=25 istart=0 iend=25
-multi_loss: Calling evolve_to_target_and_save i=1 N=100 step=25 istart=25 iend=50
-multi_loss: Calling evolve_to_target_and_save i=2 N=100 step=25 istart=50 iend=75
-multi_loss: Calling evolve_to_target_and_save i=3 N=100 step=25 istart=75 iend=100
-multi_loss(N=100, target=58) elapsed time: 2.95204 seconds 0.0492006 minutes
-
-127.676 +- 3.04749
-0.356781 vs 1.74605e-39
- 5,4 5 Did not accept -1.93165(4)
-0 0 3.77342 50 -1.93165 
-
-*** optimizer completed, elapsed time=47.6158 seconds 0.793596 minutes)
-
-./mw.py Done!

Deleted: SwiftApps/SciColSim/showbest.sh
===================================================================
--- SwiftApps/SciColSim/showbest.sh	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/showbest.sh	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,8 +0,0 @@
-#! /bin/sh
-
-awk '{
-  printf( "N %2d %2d %10.5f %5.2f | %5.2f %10.5f [ %5.2f %5.2f %10.5f %10.5f %10.5f ] %10.5f\n",
-  $2, $3, $4, $5, $7, $8, $10, $11, $12, $13, $14, $16);
-}'
-
-

Deleted: SwiftApps/SciColSim/sites.beagle.quick.xml
===================================================================
--- SwiftApps/SciColSim/sites.beagle.quick.xml	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/sites.beagle.quick.xml	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,25 +0,0 @@
-<config>
-  <pool handle="beagle">
-    <execution provider="coaster" jobmanager="local:pbs"/>
-    <profile namespace="globus" key="project">CI-MCB000119</profile>
-
-    <profile namespace="env" key="SWIFT_GEN_SCRIPTS">KEEP</profile>
-
-    <profile namespace="globus" key="jobsPerNode">1</profile>
-    <profile namespace="env" key="OMP_NUM_THREADS">24</profile>
-    <profile namespace="globus" key="workerLoggingLevel">DEBUG</profile>
-    <profile namespace="globus" key="lowOverAllocation">100</profile>
-    <profile namespace="globus" key="highOverAllocation">100</profile>
-    <profile namespace="globus" key="providerAttributes">pbs.aprun;pbs.mpp;depth=24</profile>
-    <profile namespace="globus" key="maxTime">10000</profile>
-    <profile namespace="globus" key="maxWallTime">01:30:00</profile>
-    <profile namespace="globus" key="slots">50</profile>
-    <profile namespace="globus" key="nodeGranularity">2</profile>
-    <profile namespace="globus" key="maxNodes">2</profile>
-    <profile namespace="globus" key="queue">route</profile>
-    <profile namespace="karajan" key="jobThrottle">9.59</profile>
-    <profile namespace="karajan" key="initialScore">10000</profile>
-    <filesystem provider="local"/>
-    <workdirectory >/lustre/beagle/ketan/labs/SciColSim-Bgl/swift.workdir</workdirectory>
-  </pool>
-</config>

Deleted: SwiftApps/SciColSim/sites.beagle.xml
===================================================================
--- SwiftApps/SciColSim/sites.beagle.xml	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/sites.beagle.xml	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,24 +0,0 @@
-<config>
-  <pool handle="beagle">
-    <execution provider="coaster" jobmanager="local:pbs"/>
-    <profile namespace="globus" key="project">CI-MCB000119</profile>
-
-    <profile namespace="env" key="SWIFT_GEN_SCRIPTS">KEEP</profile>
-    <!--<profile namespace="globus" key="jobsPerNode">24</profile>-->
-    <profile namespace="env" key="OMP_NUM_THREADS">24</profile>
-    <profile namespace="globus" key="workerLoggingLevel">DEBUG</profile>
-    <profile namespace="globus" key="jobsPerNode">1</profile>
-    <profile namespace="globus" key="providerAttributes">pbs.aprun;pbs.mpp;depth=24</profile>
-    <profile namespace="globus" key="maxTime">37000</profile>
-    <profile namespace="globus" key="maxWallTime">10:00:00</profile>
-    <profile namespace="globus" key="slots">20</profile>
-    <profile namespace="globus" key="lowOverAllocation">100</profile>
-    <profile namespace="globus" key="highOverAllocation">100</profile>
-    <profile namespace="globus" key="nodeGranularity">2</profile>
-    <profile namespace="globus" key="maxNodes">2</profile>
-    <profile namespace="karajan" key="jobThrottle">9.59</profile>
-    <profile namespace="karajan" key="initialScore">10000</profile>
-    <filesystem provider="local"/>
-    <workdirectory >/lustre/beagle/ketan/labs/SciColSim-Bgl/swift.workdir</workdirectory>
-  </pool>
-</config>

Deleted: SwiftApps/SciColSim/t1.py
===================================================================
--- SwiftApps/SciColSim/t1.py	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/t1.py	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,22 +0,0 @@
-#! /usr/bin/env python
-import os
-
-# FULL for target in range(58,1009,50):
-# FAST for target in range(58,209,50):
-for target in range(58,59,50):
-	s = ("%d" % target)
-	print s
-
-# FULL	for i in range(15):
-# FAST	for i in range(2):
-	for i in range(1):
-		args="./toptimizer 0 0 4 50 -1 "+s+" 40000 20 1000 2 1 2. 0.01 100 0.3 2.3 1 1 0 0 0 # > out.T"+str(target)+".i"+str(i)
-		args="OMP_NUM_THREADS=24 ./toptimizer 0 0 4 50 -1 "+s+" 40000 20 100 2 1 2. 0.01 2 0.3 2.3 1 1 0 0 0 # > out.T"+str(target)+".i"+str(i)
-		args="OMP_NUM_THREADS=24 ./toptimizer 0 0 4 50 -1 "+s+" 40000 20 100 2 1 2. 0.01 2 0.3 2.3 1 1 1 0 0 # > out.T"+str(target)+".i"+str(i)
-		print("\n\n **** CALLING APP: "+args+"\n\n\n")
-	        os.system(args);
-#		print("\n\n **** CALLING APP: ./optimizer 0 0 4 50 -1 "+s+" 40000 20 10 2 1 2. 0.01 100 0.3 2.3 1 1 0 0 0\n\n\n")
-#		FAST: os.system("./optimizer 0 0 4 50 -1 "+s+" 40000 20 10 2 1 2. 0.01 2 0.3 2.3 1 1 0 0 0")
-
-
-print "Done!"

Deleted: SwiftApps/SciColSim/t2.py
===================================================================
--- SwiftApps/SciColSim/t2.py	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/t2.py	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,23 +0,0 @@
-#! /usr/bin/env python
-import os
-
-# FULL for target in range(58,1009,50):
-# FAST for target in range(58,209,50):
-for target in range(58,59,50):
-	s = ("%d" % target)
-	print s
-
-# FULL	for i in range(15):
-# FAST	for i in range(2):
-	for i in range(1):
-		args="./toptimizer 0 0 4 50 -1 "+s+" 40000 20 1000 2 1 2. 0.01 100 0.3 2.3 1 1 0 0 0 # > out.T"+str(target)+".i"+str(i)
-		args="OMP_NUM_THREADS=24 ./toptimizer 0 0 4 50 -1 "+s+" 40000 20 100 2 1 2. 0.01 2 0.3 2.3 1 1 0 0 0 # > out.T"+str(target)+".i"+str(i)
-		args="./toptimizer 0 0 4 50 -1 "+s+" 40000 20 75    2 1 2. 0.01 2 0.3 2.3 1 1 1 0 0 m # > out.T"+str(target)+".i"+str(i)
-		args="OMP_NUM_THREADS=24 ./toptimizer 0 0 4 50 -1 "+s+" 40000 20 96    2 1 2. 0.01 2 0.3 2.3 1 1 0 0 0 m 24 # > out.T"+str(target)+".i"+str(i)
-		print("\n\n **** CALLING APP: "+args+"\n\n\n")
-	        os.system(args);
-#		print("\n\n **** CALLING APP: ./optimizer 0 0 4 50 -1 "+s+" 40000 20 10 2 1 2. 0.01 100 0.3 2.3 1 1 0 0 0\n\n\n")
-#		FAST: os.system("./optimizer 0 0 4 50 -1 "+s+" 40000 20 10 2 1 2. 0.01 2 0.3 2.3 1 1 0 0 0")
-
-
-print "Done!"

Deleted: SwiftApps/SciColSim/t3.py
===================================================================
--- SwiftApps/SciColSim/t3.py	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/t3.py	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,22 +0,0 @@
-#! /usr/bin/env python
-import os
-
-# FULL for target in range(58,1009,50):
-# FAST for target in range(58,209,50):
-for target in range(58,59,50):
-	s = ("%d" % target)
-	print s
-
-# FULL	for i in range(15):
-# FAST	for i in range(2):
-	for i in range(1):
-		args="./toptimizer 0 0 4 50 -1 "+s+" 40000 20 1000 2 1 2. 0.01 100 0.3 2.3 1 1 0 0 0 # > out.T"+str(target)+".i"+str(i)
-		args="OMP_NUM_THREADS=24 ./toptimizer 0 0 4 50 -1 "+s+" 40000 20 100 2 1 2. 0.01 2 0.3 2.3 1 1 0 0 0 # > out.T"+str(target)+".i"+str(i)
-		args="./toptimizer 0 0 4 50 -1 "+s+" 40000 20 75    2 1 2. 0.01 2 0.3 2.3 1 1 1 0 0 a # > out.T"+str(target)+".i"+str(i)
-		print("\n\n **** CALLING APP: "+args+"\n\n\n")
-	        os.system(args);
-#		print("\n\n **** CALLING APP: ./optimizer 0 0 4 50 -1 "+s+" 40000 20 10 2 1 2. 0.01 100 0.3 2.3 1 1 0 0 0\n\n\n")
-#		FAST: os.system("./optimizer 0 0 4 50 -1 "+s+" 40000 20 10 2 1 2. 0.01 2 0.3 2.3 1 1 0 0 0")
-
-
-print "Done!"

Deleted: SwiftApps/SciColSim/tc
===================================================================
--- SwiftApps/SciColSim/tc	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/tc	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,15 +0,0 @@
-localhost sh /bin/sh null null null
-localhost cat /bin/cat null null null
-
-pbs cat /bin/cat null null null
-mcs cat /bin/cat null null null
-localhost catnap /home/wilde/swift/lab/catnap.sh null null GLOBUS::maxwalltime="00:01:00"
-
-beagle optimizer /home/wilde/AndreysOptimizer/src/Optimizer null null null
-beagle optimizersh /home/wilde/AndreysOptimizer/src/optimizer.sh null null null
-
-beagle evolve /home/wilde/AndreysOptimizer/src/evolve.sh null null null
-localhost evolve /home/wilde/AndreysOptimizer/src/evolve.sh null null GLOBUS::maxwalltime="02:00:00"
-
-beagle sumloss /home/wilde/AndreysOptimizer/src/sumloss.sh null null null
-localhost sumloss /home/wilde/AndreysOptimizer/src/sumloss.sh null null GLOBUS::maxwalltime="02:00:00"

Deleted: SwiftApps/SciColSim/test-orig.sh
===================================================================
--- SwiftApps/SciColSim/test-orig.sh	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/test-orig.sh	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,21 +0,0 @@
-#! /bin/bash
-
-python <<END
-import os
-
-target = 58
-s = ("%d" % target)
-
-args="0 0 4 50 -1 "+s+" 40000 20 1000 2 1 2. 0.01 100 0.3 2.3 1 1 0 0 0 "
-args="0 0 4 50 -1 "+s+" 40000 20  100 2 1 2. 0.01   2 0.3 2.3 1 1 0 0 0 "
-
-print("\n\n **** CALLING APP: "+args+"\n\n\n")
-os.system("./optimizer "+args+" >out.o.T"+s);
-os.system("./Optimizer "+args+" >out.O.T"+s);
-
-#		print("\n\n **** CALLING APP: ./optimizer 0 0 4 50 -1 "+s+" 40000 20 10 2 1 2. 0.01 100 0.3 2.3 1 1 0 0 0\n\n\n")
-#		FAST: os.system("./optimizer 0 0 4 50 -1 "+s+" 40000 20 10 2 1 2. 0.01 2 0.3 2.3 1 1 0 0 0")
-
-print "Done!"
-END
-

Deleted: SwiftApps/SciColSim/test-swift.sh
===================================================================
--- SwiftApps/SciColSim/test-swift.sh	2012-02-16 20:22:37 UTC (rev 5638)
+++ SwiftApps/SciColSim/test-swift.sh	2012-02-16 20:59:48 UTC (rev 5639)
@@ -1,7 +0,0 @@
-escapecode=$(echo -n -e '\033')
-
-swift -config cf -tc.file tc -sites.file local.xml annealing.swift \
-  -e33="$escapecode" 
-
-#  -evolveReruns=20  \ 
-#  -annealingCycles=10




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