Ada + 机器学习（Python 框架）

Question

I'm trying to write a simple machine learning application in Ada, and also trying to find a good framework to use. My knowledge of one thing is extremely minimal, and of the other is somewhat minimal.

There are several nifty machine learning frameworks out there, and I'd like to leverage one for use with an Ada program, but I guess I'm just...at a loss. Can I use an existing framework written in Python, for instance and wrap (or I guess, bind?) the API calls in Ada? Should I just pass off the scripting capabilities? I'm trying to figure it out.

Case in point: Scikit (sklearn) https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html#

This does some neat stuff, and I'd like to be able to leverage this, but with an Ada program. Does anyone have advice from a similar experience?

I am just researching, so I have tried finding information.

http://www.inspirel.com/articles/Ada_Python_Binding.html https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html#

Answer 1

The inspirel solution is based on python2.7. If you're using anything from python3.5 onwards a few mods need to be made

python_module.py

#print 'Hello from Python module'
print('Hello from Python module')

#print 'Python adding:', a, '+', b
print('Python adding:', a, '+', b)

ada_main.adb

-- Python.Execute_String("print 'Hello from Python!'");
Python.Execute_String("print('Hello from Python!')");

Some routines have been deprecated so the linkage has to change

python.adb

--pragma Import(C, PyInt_AsLong, "PyInt_AsLong");
pragma Import(C, PyInt_AsLong, "PyLong_AsLong");

--pragma Import(C, PyString_FromString, PyString_FromString");
pragma Import(C, PyString_FromString, "PyUnicode_FromString");

Now the fun bit. On Linux, changing to say python 3.9, you'd just change

--for Default_Switches ("Ada") use ("-lpython2.7");
for Default_Switches ("Ada") use ("-lpython3.7");

but on windows, the libraries aren't dumped in a community lib. All the packages are kept separately. The -L has to be added to tell the linker where to find the library. In my case, I did a non-admin install of python, so it looks something like

for Default_Switches ("Ada") use ("-L\Users\StdUser\AppData\Local\Programs\Python\Python37-32\libs", "-lpython37");

Use gprbuild instead of gnatmake -p, which has been deprecated. If you do all your mods correctly

gprbuild ada_main.gpr

should give you an executable in obj\ada_main.exe. Running the executable should give

C:\Users\StdUser\My Documents\ada-python>gprbuild ada_main.gpr
Compile
   [Ada]          ada_main.adb
Bind
   [gprbind]      ada_main.bexch
   [Ada]          ada_main.ali
Link
   [link]         ada_main.adb

C:\Users\StdUser\My Documents\ada-python>obj\ada_main.exe
executing Python directly from Ada:
Hello from Python!

loading external Python module and calling functions from that module:
Hello from Python module!

asking Python to add two integers:
Python adding: 10 + 2
Ada got result from Python: 12

we can try other operations, too:
subtract: 8
multiply: 20
divide  : 5

Remember to put the pythonxx.dll somewhere on your path otherwise it won't be able to find the library wen it starts executing.

Answer 2

It is possible to use an existing machine learning framework written in Python, such as scikit-learn, with an Ada program. One way to do this is to use Ada's foreign function interface (FFI) to bind to the Python API calls, so that you can call the Python functions from within your Ada program. This can be done using a library such as GPRBind or SWIG, which can generate the necessary bindings.

Another option is to use the Python script to perform the machine learning tasks and pass the data to and from the Ada program using the command line or a file. This way you can leverage the capabilities of the Python framework without having to integrate it directly into your Ada program.

It's worth noting that, as Ada is a systems programming language, it tends to be used more often in embedded systems, real-time systems and safety-critical systems. It's less popular for ML. Therefore, it might be more challenging to find Ada libraries or Ada-specific tutorials for ML tasks.

It's recommended that you try out different ways of integrating with Python and see which one works best for your specific use case.