EMAM2Middleware issueshttps://git.rwth-aachen.de/monticore/EmbeddedMontiArc/generators/EMAM2Middleware/-/issues2022-11-11T19:39:14+01:00https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/generators/EMAM2Middleware/-/issues/34Extend ROS topics to accept integer arrays for state representation2022-11-11T19:39:14+01:00Anis Abdollahi-SissanExtend ROS topics to accept integer arrays for state representationWhen using the EMAM2Middleware to generate a reinforcement learning agent, which is connected via ros-gym to python, defining the state as an integer array in python leads to an error, because the middleware initializes the state topic i...When using the EMAM2Middleware to generate a reinforcement learning agent, which is connected via ros-gym to python, defining the state as an integer array in python leads to an error, because the middleware initializes the state topic in ROS as Float32MultiArray, regardless of the definition in the python files.
[This](https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/applications/reinforcement_learning/topologyoptimizer/-/blob/main/additional_files/Middleware/Environment.ftl) file implements Int32MultiArray as the default topic type for the state.
To resolve this issue, it would be necessary to automatically switch between the Float- and Integer-representation for the ROS state topic.
This can be implemented in the template file for the environment of the agent.Lukas BramThilo MetzlaffLukas Bramhttps://git.rwth-aachen.de/monticore/EmbeddedMontiArc/generators/EMAM2Middleware/-/issues/33Failing EMADL Tests2019-09-04T21:13:34+02:00Alexander David HellwigFailing EMADL TestsThe tests
- de.monticore.lang.monticar.generator.middleware.CliTest#testEMADLAndRosGenerator
- de.monticore.lang.monticar.generator.middleware.CliTest#testSingleEMADLGenerator
- de.monticore.lang.monticar.generator.middleware.CliTest#tes...The tests
- de.monticore.lang.monticar.generator.middleware.CliTest#testEMADLAndRosGenerator
- de.monticore.lang.monticar.generator.middleware.CliTest#testSingleEMADLGenerator
- de.monticore.lang.monticar.generator.middleware.CliTest#testEMADLConfigFile
- de.monticore.lang.monticar.generator.middleware.GenerationTest#testEMADLMiddlewareGeneration
are all failing.https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/generators/EMAM2Middleware/-/issues/32Autotraining EMADL2CPP2019-05-15T11:33:43+02:00Nicola GattoAutotraining EMADL2CPPThe new version of EMADL2CPP supports the option to start the training of a CNN component automatically. For each CNN component, the training outputs two files: the symbol.json and the parameter file of the neural network. In order to us...The new version of EMADL2CPP supports the option to start the training of a CNN component automatically. For each CNN component, the training outputs two files: the symbol.json and the parameter file of the neural network. In order to use the autotraining function of EMADL2CPP in EMAM2Middleware generator, a cli option is needed to activate it. Furthermore, the compiled executable expects the two output files to be at the relative path "model/name.of.cnnarch.component/". This means, the generated compile.sh should ensure that these files are moved to the correct place.Evgeny KusmenkoEvgeny Kusmenkohttps://git.rwth-aachen.de/monticore/EmbeddedMontiArc/generators/EMAM2Middleware/-/issues/19Interactive overview of clustering results/chooser2019-02-03T11:00:54+01:00Alexander David HellwigInteractive overview of clustering results/chooser```
+-------------------------------+---------+
| | algo1 |
| <visualization of cluster> | >algo2 |
| | ... |
| | |
+------------...```
+-------------------------------+---------+
| | algo1 |
| <visualization of cluster> | >algo2 |
| | ... |
| | |
+-------------------------------+---------+
| |continue |
|<extra info for choosen algo> |cancel |
| | |
+-----------------------------------------+
```https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/generators/EMAM2Middleware/-/issues/16Compare Clustering of flatt Model with previous (Sub)component division2019-01-08T17:17:09+01:00Alexander David HellwigCompare Clustering of flatt Model with previous (Sub)component division