BLUF: Can I export a .als file corresponding to a model I have created with the Alloy API?
Example: I have a module that I read in using edu.mit.csail.sdg.alloy4compiler.parser.CompUtil. I then add signatures and facts to create a modified model in memory. Can I "de-parse" that and basically invert the lexer (edu.mit.csail.sdg.alloy4compiler.parser.CompLexer) to get a .als file somehow?
It seems like there ought to be a way to decompile the model in memory and save that as code to be later altered, but I'm having trouble identifying a path to that in the Alloy API Javadocs. I'm building a translator from select behavioral aspects of UML/SysML as part of some research, so I'm trying to figure out if there is something extant I can take advantage of or if I need to create it.
It seems a similar question has been asked before: Generating .als files corresponding to model instances with Alloy API
From the attached post https://stackoverflow.com/users/2270610/lo%c3%afc-gammaitoni stated he has written a solution for this in his Lightning application. He said that he may include the source code for completing this task. I'm unsure if he has uploaded the solution yet.
Related
I am new to this field, this question might look dumb to some of you but please bear with it.
I have created a keras model which works well. I can save it in .hdf5 format using model.save("model_name.hdf5) which is good.
Main question is if there is any other format i can save my model in so that it can be used in c++/android/JavaScript. Is this even possible to do so?? If you are thinking why I am asking for all 3 languages, its because I have 3 projects each of them use respective language.
Thanks for any help in advance.
The answer depends on what you are going to save and use in another language.
If you just need the architecture of the model to be saved, you may save it as JSON, which can later be used in any other platform and language you are going to use.
model_json = model.to_json()
If you also need the weights and biases, I do not know any specific tool, but you can simply read the stored data in python, create a multidimensional array, and then save it in a file appropriate for any of the languages you need. For example, the weights of the second layer can be found in model.layers[2].get_weights().
Finally, if you want to run the model in another language, you need to implement the for-loops that make the processing. You might find some conversion tools for your target language. For example, for C, this tool can help.
Assume that I have a Spock specification that given a city and state tests for the correct zip code. Assume I have a text file of cities and states that is used to drive the tests in the where clause.
Now assume that I want to split the tests so that I can run for "Virginia" or "Maryland". The approach that I have taken is to create a new VirginiaSpec and a new MarylandSpec and in that spec, I modify the where clause.
This works, but seems inefficient because every other part of the VirginiaSpec and MarylandSpec is exactly the same. In addition, if the logic changes, then I would need to change it in every spec that I have.
So what I am looking for is an approach that allows me to have one StateSpec in which the where clause can be parameterized.
I realize I have not included a code example, however if my question is not clear, then I can provide one. Thanks for your help.
-Dan
You have a couple of options. You could put the basic setup and structure and even the test itself in a base test class, then extend that class w/ your VirginiaSpec and MarylandSpec. Your spec classes would be very small probably just defining a constant that is the state for the spec.
But that seems needless. If both the cities and states are in this file, you could just read in the file in the where section of your test.
https://snekse.github.io/test-often-and-prosper-slides/#/42
If you cannot get the WHERE section working, you could always read in your file during the setupSpec and store the data in some kind of data structure then loop through it.
Spock: Reading Test Data from CSV File
But in general, using the Where label is going to be the right answer.
I recently walked through the Advanced Orchard tutorial on Pluralsight and it really showed me a lot of things I can do to extend Orchard. That said, I was wondering if there is a way for one module to return a view from another module?
The scenario for this is I'm building custom modules for my clients that have features that would be proprietary so I'd want to protect them with an API key, similar to how oForms works. The only difference from mine to theirs is they allow functionality regardless of activation whereas mine wouldn't work at all so I'd like to have a base module that all of my custom modules derive from and each one could do something like:
if (this.IsActivated())
return View("ViewFromThisModule")
else
return View("NotActivatedViewFromBaseModule")
The real purpose behind this would be so I don't have to copy the view(s) utilized in the base module to each custom one for things such as whether the module is activated or not.
Per Betrand's suggestion, instead of going the multiple module route I'll instead do a single module that breaks out features instead. Then I don't need to share anything because the whole thing is self-contained.
Don't know where to start on this one so hopefully you guys can clear up my question. I have project where email will be searched for specific words/patterns and stored in a structured manner. Something that is done with Trip it.
The article states that they developed a DataMapper
The DataMapper is responsible for taking inbound email messages
addressed to plans [at] tripit.com and transforming them from the
semi-structured format you see in your mail reader into a highly
structured XML document.
There is a comment that also states
If you're looking to build this yourself, reading a little bit about
Wrappers and Wrapper Induction might be helpful
I Googled and read about wrapper induction but it was just too broad of a definition and didn't help me understand how one would go about solving such problem.
Is there some open source project out there that does similar things?
There are a couple of different ways and things you can do to accomplish this.
The first part, which involves getting access to the email content I'll not answer here. Basically, I'll assume that you have access to the text of emails, and if you don't there are some libraries that allow you to connect java to an email box like camel (http://camel.apache.org/mail.html).
So now you've got the email so then what?
A handy thing that could help is that lingpipe (http://alias-i.com/lingpipe/) has an entity recognizer that you can populate with your own terms. Specifically, look at some of their extraction tutorials and their dictionary extractor (http://alias-i.com/lingpipe/demos/tutorial/ne/read-me.html) So inside of the lingpipe dictionary extractor (http://alias-i.com/lingpipe/docs/api/com/aliasi/dict/ExactDictionaryChunker.html) you'd simply import the terms you're interested in and use that to associate labels with an email.
You might also find the following question helpful: Dictionary-Based Named Entity Recognition with zero edit distance: LingPipe, Lucene or what?
Really a very broad question, but I can try to give you some general ideas, which might be enough to get started. Basically, it sounds like you're talking about an elaborate parsing problem - scanning through the text and looking to apply meaning to specific chunks. Depending on what exactly you're looking for, you might get some good mileage out of a few regular expressions to start - things like phone numbers, email addresses, and dates have fairly standard structures that should be matchable. Other data points might benefit from some indicator words - the phrase "departing from" might indicate that what follows is an address. The natural language processing community also has a large tool set available for text processing - check out things like parts of speech taggers and semantic analyzers if they're appropriate to what you're trying to do.
Armed with those techniques, you can follow a basic iterative development process: For each data point in your expected output structure, define some simple rules for how to capture it. Then, run the application over a batch of test data and see which samples didn't capture that datum. Look at the samples and revise your rules to catch those samples. Repeat until the extractor reaches an acceptable level of accuracy.
Depending on the specifics of your problem, there may be machine learning techniques that can automate much of that process for you.
We've been working with the NLTK library in a recent project where we're
mainly interested in the named entities part.
In general we're getting good results using the NEChunkParser class.
However, we're trying to find a way to provide our own terms to the
parser, without success.
For example, we have a test document where my name (Shay) appears in
several places. The library finds me as GPE while I'd like it to find
me as PERSON...
Is there a way to provide some kind of a custom file/
code so the parser will be able to interpret the named entity as I
want it to?
Thanks!
The easy solution is to compile a list of entities that you know are misclassified, then filter the NEChunkParser output in a postprocessing module and replace these entities' tags with the tags you want them to have.
The proper solution is to retrain the NE tagger. If you look at the source code for NLTK, you'll see that the NEChunkParser is based on a MaxEnt classifier, i.e. a machine learning algorithm. You'll have to compile and annotate a corpus (dataset) that is representative for the kind of data you want to work with, then retrain the NE tagger on this corpus. (This is hard, time-consuming and potentially expensive.)