معرفی شرکت ها

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

توضیحات

A package for learning LTL formulas from a sample consisting of traces partitioned into positive and negative

ویژگی	مقدار
سیستم عامل	OS Independent
نام فایل	Scarlet-ltl-0.0.1
نام	Scarlet-ltl
نسخه کتابخانه	0.0.1
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	-
ایمیل نویسنده	Ritam Raha <ritam.raha18@gmail.com>, Rajarshi Roy <rajarshi008@gmail.com>, Nathanael Fijalkow <nathanael.fijalkow@gmail.com>, Daniel Neider <daniel.neider@uni-oldenburg.de>
آدرس صفحه اصلی	-
آدرس اینترنتی	https://pypi.org/project/Scarlet-ltl/
مجوز	-

<h1 align="center"> <img src="https://rajarshi008.github.io/assets/images/scarlet-logo.png" width="60%"> </h1> --- ## SCARLET We solve the problem of learning LTL formulas from a sample consisting of traces partitioned into positive and negative. A [paper](https://link.springer.com/chapter/10.1007/978-3-030-99524-9_14) presenting the algorithms behind `Scarlet` was published in TACAS'2022. ## Installation ### Creating Virtual Environments It is recommended to install `Scarlet` inside a virtual environment as otherwise the dependencies have to be installed in your machine. Usually, a virtual environment can be created and activated using the following command: ``` python3 -m venv venv source venv/bin/activate ``` ### Installing the tool Now, you can install the tool, as python package using pip command as follows: ``` python3 -m pip install Scarlet-ltl ``` ### Input File format: The input files consist of traces separated as positives and negatives, separated by `---`. Each trace is a sequence of letter separated by `;`. Each letter represents the truth value of atomic propositions. An example of a trace is `1,0,1;0,0,0` which consists of two letters each of which define the values of three propositions (which by default consider to be `p,q,r`). An example sample looks like the following: ``` 0,0,0;0,1,1;1,0,0;0,0,1;0,1,0 1,1,0;1,0,1;1,0,0;1,1,1;1,0,1 1,1,0;0,1,1;1,1,1;1,0,0;1,0,1 --- 1,0,0;1,0,0;0,1,0;1,1,0;1,1,1 1,0,0;1,0,0;0,1,0;1,1,0;1,0,0 0,0,1;1,0,0;1,1,0;1,1,1;1,0,0 ``` ## How to run Scarlet: ### Create input file To run Scarlet, you have to create an input file with `.trace` extension in the same directory where `venv` folder is located. The input file format is described in the above section. ### Run Scarlet on a particular input file ``` from Scarlet.ltllearner import LTLlearner learner = LTLlearner(input_file = "input_file_name.trace") learner.learn() ``` This will run Scarlet on the input trace file. ### Parameters You can call the `LTLlearner` class with additional parameters as follows: * input_file = the path of the file containing LTL formuas, i.e., `= 'input_file_name.trace'` * verbosity = specifying the logging level, i.e., 0 for the basic formula and time, 1 for a bit detailed, 2 for fully detailed execution, `default = 2` * timeout = For specifying the timeout, `default = 900` * csvname = the name of the output csv file, i.e., `= 'output_file_name.csv'` * thres = the bound on loss function for noisy data, `default = 0` for perfect classification, has to be a number between zero and one ## How to generate trace files from LTL formulas You can also generate trace files from given LTL formulas following the instructions below: ### Install dependencies For generating benchmarks from a given set of LTL formula, we rely on a python package LTLf2DFA that uses [MONA](https://www.brics.dk/mona/) in its backend. As a result, one needs to install MONA first in order to be able to use this procedure (instructions can be found in the MONA website). ### Create input formula file For generating benchmarks, you have to create an input file named `formulas.txt` in the same directory where `venv` folder is located. The formula file should contain a list of formulas (in prefix notation) along with the alphabet. An example of this file is as follows: ``` G(!(p));p ->(F(q), U(!(p),q));p,q G(->(q, G(!(p))));p,q ``` ### Generate trace files from `formulas.txt` ``` from Scarlet.genBenchmarks import SampleGenerator generator = SampleGenerator(formula_file= "formulas.txt") generator.generate() ``` ### Parameters You can call the `SampleGenerator` class with additional parameters as follows: * formula_file = the path of the file containing LTL formuas, `example = 'formulas.txt'` * sample_sizes = list of sample_size, i.e., number of positive traces and number of negative traces (separated by comma) in each sample, `default = [(10,10),(50,50)]` * trace_lengths = For specifying the length range for each trace in the samples, `default = [(6,6)]` * output_folder = For specifying the name of the folder in which samples are generated

نیازمندی

مقدار	نام
-	lark
-	graphviz
-	ltlf2dfa

زبان مورد نیاز

مقدار	نام
>=3.7	Python

نحوه نصب

نصب پکیج whl Scarlet-ltl-0.0.1:

pip install Scarlet-ltl-0.0.1.whl

نصب پکیج tar.gz Scarlet-ltl-0.0.1:

pip install Scarlet-ltl-0.0.1.tar.gz