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48 lines
1.7 KiB
Markdown
48 lines
1.7 KiB
Markdown
Yannakakis
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==========
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An algorithm to construct an adaptive distinguishing sequence for a mealy
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machine. If it does not exist, a partial sequence will be generated, which is
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still useful for generating a seperating set (in the sense of Lee and
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Yannakakis). The partial leaves will be augmented via ordinary seperating
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sequences. In effect, the resulting test method is an instantiation of the HSI-
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method, which tends towards the DS-method.
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Most of the algorithms are found in the directory `lib/` and their usage is best
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illustrated in `src/main.cpp` or `src/methods.cpp`.
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Currently states and inputs are encoded internally as integer values (because
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this enables fast indexing). Only for I/O, maps are used to translate between
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integers and strings. To reduce the memory footprint `uint16_t`s are used, as
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the range is big enough for our use cases (`uint8_t` is clearly too small for
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the number of states, but could be used for alphabets).
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## Building
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The only dependency is boost. In order to build the project, one can use cmake.
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```
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mkdir build
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cd build
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cmake -DCMAKE_BUILD_TYPE=RelWithDebInfo ..
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make
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```
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Then every .cpp file in the src directory will be built and generate an
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executable in the build directory. Note that you'll need c++14, but clang in Mac
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OSX will understand that (and if not, you'll have to update Xcode). The main
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sourcefile (`src/main.cpp`) can also be built with c++11 (this is tested on some
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commits on both Windows and linux).
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## Java
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For now the java code, which acts as a bridge between LearnLib and this c++
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tool, is included here. But it should earn its own repo at some point. Also, my
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javanese is a bit rusty...
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## License
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See `LICENSE`
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