Hello everyone I want to ask for your valuable help to see if it is possible to add a SUMO scenario I have into NS2 to be able to simulate an ad-hoc network between cars and also allowed them to communicate with an LTE radio base station (eNodeB).
I already have SUMO and NS2 with the LTE patch running, I was able to add the LTE features thanks to an answer given by Knud Larsen in this post:
invalid command name "Queue/LTEQueue"
Ref. your note "7" https://drive.google.com/drive/folders/0B0KAEW9TOkDvekZxRkFWa3ZnLXM
Your file unam.tcl :
set val(nn) 9
... change to
set val(nn) 11 ;# maximum number of nodes
# nn must be higher than the node number in traffic, movement files :
# any higher number can be used
It is forbidden to have two files with the same file name. unam.tcl is now named unam11.tcl, and mobility.tcl is mobility9.tcl .
Your file unam.tcl (unam11.tcl): No node higher than $node_(10) can be used. Your file line 111
$ns_ attach-agent $node_(22) $sink
... change to e.g.
$ns_ attach-agent $node_(3) $sink
Traffic file and movement files can also not exceed node numbers of 10 :
57 set opt(cp) "flowmod-teatinos-M40.tcl" ;# Network workload
.... must be e.g.
57 set opt(cp) "flowmod-urban-malaga-S10.tcl" ;# Network workload
... And
103 source mobility.tcl
104 source flowmod-teatinos-M40.tcl
.... can be
103 source mobility9.tcl
104 source flowmod-urban-malaga-S10.tcl
(The numbers are line numbers.) Other traffic files with max 10 nodes : cbr10, cbr-10-1-10-20 . And new traffic files with 9 .. 10 nodes can be created with e.g. $ ns cbrgen.tcl [options]
Traffic files etc. https://drive.google.com/drive/folders/0B7S255p3kFXNa3ZPQjB3UHo3cWc?usp=sharing
My test : $ ns235-LTE-64 unam11.tcl : 'nam' opens OK, the files unam.nam 43.1MB, unam.tr 31.9MB are created.
EDIT : An 802.11p file was omitted in your example, unam.tcl . New example = unam11-802.11p.tcl, with the connected 802.11p file = 802.11p-2.tcl. Link → https://www.dropbox.com/s/yofht7p5h2kk068/unam11-802.11p.tcl_files.tar.gz?dl=0
What is left out may be some LTE communication, see LTE examples https://drive.google.com/file/d/0B7S255p3kFXNSmd4Q3h3dXp1QWc/view?usp=sharing
I am attempting to run "random" or non-deterministic simulations of a NuSMV model I have created. However between subsequent runs the trace that is produced is exactly the same.
Here is the model:
MODULE main
VAR x : 0..4;
VAR clk : 0..10;
DEFINE next_x :=
case
x = 0 : {0,1};
x = 1 : {1,2};
x = 2 : {1,0};
TRUE : {0};
esac;
DEFINE next_clk :=
case
(clk < 10) : (clk+1);
TRUE : clk;
esac;
INIT (x = 0);
INIT (clk = 0);
TRANS (next(x) in next_x);
TRANS next(clk) = next_clk;
CTLSPEC AG(clk < 10);
I am running this using the following commands in the interactive shell:
go
pick_state -r
simulate -k -r 30
show_traces 1
quit
Perhaps I have a mistake in my model? Or I am not running the correct commands in the shell.
Thanks in advance!
As far as I can tell after playing around with the tool, I would say that what you experience is a common behaviour due to using pseudo-random generators in a certain way.
Basically, I posit that each time one starts NuSMV void srand(unsigned int seed) is initialised with the same seed value. The obvious result is that NuSMV performs the exact same non-deterministic choices among independent runs, provided that you load the exact same model and perform exactly the same sequence of commands.
This kind of design is common among model checkers because it allows to reproduce potential bug traces reported by users more easily.
After looking at NuSMV -help and NuSMV documentation, it appears to me that the program has no option to manually set an arbitrary seed for the pseudo-random generator. (Note: you might want to contact NuSMV mailing list about this, it may be possible that there exists some internal variable to configure the random seed with the aid of the set command)
Therefore, I would like to propose the following work-around to help you achieving your goal of collecting different, non-deterministic execution traces from the same model. Try:
go
pick_state -r
simulate -r RANDOM_SEED
pick_state -r
simulate -r 30
show_traces 2
quit
Basically, the idea is to exploit the first simulation in order to move forward the pseudo-random generator to an arbitrary point in the pseudo-random chain. Each time you execute this script, you change the value of RANDOM_SEED, so that any two executions of NuSMV have a different starting-point in the pseudo-random generator for the second trace. In this way, NuSMV no longer repeats the same choices it has done in other executions for the second trace, unless that happens by pure chance.
Alternatively, you may obtain all the non-deterministic execution traces you want from a single run of the NuSMV solver:
go
pick_state -r
simulate -r 30
show_traces 1
pick_sate -r
simulate -r 30
show_traces 2
...
pick_state -r
simulate -r 30
show_traces N
quit
Note 1: your model has only one initial state, so pick_state -r always chooses the same initial state.
Note 2: your model reports the following error on my system:
TYPE ERROR file test.smv: line 23 :
illegal operand types of "=" : integer-set and integer
when I type pick_state -i.
Note 3: since NuSMV source code is available, another possible solution is to patch it so as to accept a novel option for setting an arbitrary seed to initialise the pseudo-random generator.
I have an aggregate report:
Do I have to add another listener or add anything else to the testplan?
For ex: consider the above snapshot:
I'm running 9002 samples, I just need to know the total time required for all the 9002 samples?
If your 9002 Samples are played in a sequence by only 1 user, just use a TransactionController that will contain your HTTP Request and you will have this.
If 9002 samples are played by different users then you can export the CSV file that contains the test results and use Excel to sum up the elapsed column.
To generate a CSV file, run test in NON GUI Mode using:
jmeter -n -l results.csv -t test.jmx
I am trying to classify binary data. In the data file, class [0,1] is converted to [-1,1]. Data has 21 features. All features are categorical. I am using neural network for training. The training command is:
vw -d train.vw --cache_file data --passes 5 -q sd -q ad -q do -q fd --binary -f model --nn 22
I create raw prediction file as:
vw -d test.vw -t -i neuralmodel -r raw.txt
And normal prediction file as:
vw -d test.vw -t -i neuralmodel -p out.txt
First five lines of raw file are:
0:-0.861075,-0.696812 1:-0.841357,-0.686527 2:0.796014,0.661809 3:1.06953,0.789289 4:-1.23823,-0.844951 5:0.886767,0.709793 6:2.02206,0.965555 7:-2.40753,-0.983917 8:-1.09056,-0.797075 9:1.22141,0.84007 10:2.69466,0.990912 11:2.64134,0.989894 12:-2.33309,-0.981359 13:-1.61462,-0.923839 14:1.54888,0.913601 15:3.26275,0.995055 16:2.17991,0.974762 17:0.750114,0.635229 18:2.91698,0.994164 19:1.15909,0.820746 20:-0.485593,-0.450708 21:2.00432,0.964333 -0.496912
0:-1.36519,-0.877588 1:-2.83699,-0.993155 2:-0.257558,-0.251996 3:-2.12969,-0.97213 4:-2.29878,-0.980048 5:2.70791,0.991148 6:1.31337,0.865131 7:-2.00127,-0.964116 8:-2.14167,-0.972782 9:2.50633,0.986782 10:-1.09253,-0.797788 11:2.29477,0.97989 12:-1.67385,-0.932057 13:-0.740598,-0.629493 14:0.829695,0.680313 15:3.31954,0.995055 16:3.44069,0.995055 17:2.48612,0.986241 18:1.32241,0.867388 19:1.97189,0.961987 20:1.19584,0.832381 21:1.65151,0.929067 -0.588528
0:0.908454,0.72039 1:-2.48134,-0.986108 2:-0.557337,-0.505996 3:-2.15072,-0.973263 4:-1.77706,-0.944375 5:0.202272,0.199557 6:2.37479,0.982839 7:-1.97478,-0.962201 8:-1.78124,-0.944825 9:1.94016,0.959547 10:-1.67845,-0.932657 11:2.54895,0.987855 12:-1.60502,-0.92242 13:-2.32369,-0.981008 14:1.59895,0.921511 15:2.02658,0.96586 16:2.55443,0.987987 17:3.47049,0.995055 18:1.92482,0.958313 19:1.47773,0.901044 20:-3.60913,-0.995055 21:3.56413,0.995055 -0.809399
0:-2.11677,-0.971411 1:-1.32759,-0.868656 2:2.59003,0.988807 3:-0.198721,-0.196146 4:-2.51631,-0.987041 5:0.258549,0.252956 6:1.60134,0.921871 7:-2.28731,-0.97959 8:-2.89953,-0.993958 9:-0.0972349,-0.0969177 10:3.1409,0.995055 11:1.62083,0.924746 12:-2.30097,-0.980134 13:-2.05674,-0.967824 14:1.6744,0.932135 15:1.85612,0.952319 16:2.7231,0.991412 17:1.97199,0.961995 18:3.47125,0.995055 19:0.603527,0.539567 20:1.25539,0.84979 21:2.15267,0.973368 -0.494474
0:-2.21583,-0.97649 1:-2.16823,-0.974171 2:2.00711,0.964528 3:-1.84079,-0.95087 4:-1.27159,-0.854227 5:-0.0841799,-0.0839635 6:2.24566,0.977836 7:-2.19458,-0.975482 8:-2.42779,-0.98455 9:0.39883,0.378965 10:1.32133,0.86712 11:1.87572,0.95411 12:-2.22585,-0.976951 13:-2.04512,-0.96708 14:1.52652,0.909827 15:1.98228,0.962755 16:2.37265,0.982766 17:1.73726,0.939908 18:2.315,0.980679 19:-0.08135,-0.081154 20:1.39248,0.883717 21:1.5889,0.919981 -0.389856
First five lines of (normal) prediction file are:
-0.496912
-0.588528
-0.809399
-0.494474
-0.389856
I have tallied this (normal) output with raw output. I notice that the (last or) ending float value in each of the five raw lines is the same as above.
I would please like to understand the raw output as also the normal output. That each line holds 22 pairs of values is something to do with 22 neurons? How to interpret the output as [-1,1] and why a sigmoid function is needed to convert either of the above to probabilities. Will be grateful for help.
For binary classification, you should use a suitable loss function (--loss_function=logistic or --loss_function=hinge). The --binary switch just makes sure that the reported loss is the 0/1 loss (but you cannot optimize for 0/1 loss directly, the default loss function is --loss_function=squared).
I recommend trying the --nn as one of the last steps when tuning the VW parameters. Usually, it improves the results only a little bit and the optimal number of units in the hidden layer is quite small (--nn 1, --nn 2 or --nn 3). You can also try adding a direct connections between the input and output layer with --inpass.
Note that --nn uses always tanh as the sigmoid function for the hidden layer and only one hidden layer is possible (it is hardcoded in nn.cc).
If you want to get probabilities (real number from [0,1]), use vw -d test.vw -t -i neuralmodel --link=logistic -p probabilities.txt. If you want the output to a be real number from [-1,1], use --link=glf1.
Without --link and --binary, the --pred output are the internal predictions (in range [-50, 50] when logistic or hinge loss function is used).
As for the --nn --raw question, your guess is correct:
The 22 pairs of numbers correspond to the 22 neurons and the last number is the final (internal) prediction. My guess is that each pair corresponds to the bias and output of each unit on the hidden layer.
I'm using ab to do some load testing, and it's important that the supplied querystring (or POST) parameters change between requests.
I.e. I need to make requests to URLs like:
http://127.0.0.1:9080/meth?param=0
http://127.0.0.1:9080/meth?param=1
http://127.0.0.1:9080/meth?param=2
...
to properly exercise the application.
ab seems to only read the supplied POST data file once, at startup, so changing its content during the test run is not an option.
Any suggestions?
You're going to need to use a more full-featured benchmarking tool like jMeter for this.
Add my recommendation for jMeter...it works very well!
You could also create a script that creates a second script with something like:
ab -n 1 -c 1 'http://yoursever.com/method?param=0' &
ab -n 1 -c 1 'http://yoursever.com/method?param=1' &
ab -n 1 -c 1 'http://yoursever.com/method?param=2' &
ab -n 1 -c 1 'http://yoursever.com/method?param=3' &
ab -n 1 -c 1 'http://yoursever.com/method?param=4' &
But that's only really useful if you're trying to simulate load and observe your server. The actual benchmarks will have to be collated if you want to check ab performance. At that point I'd just use jMeter. For my use, I just need to simulate load and the ab processes are light enough that running 100 like this is no problem.
Here is patched version of ab or patch:
http://www.andboson.com/?p=1372
this version is included that patch http://chrismiles.info/dev/testing/ab
also can read many post-data line by line
upd:
sample request:
./ab -v1 -n2 -c1 -T'application/json' -ppostfile http://api.webhookinbox.com/i/HX6mC1WS/in/
postfile content:
{"data1":1, "data2":"4"}
{"data0":0, "x":"y"}
upd2:
also alternative
https://github.com/andboson/ab-go