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Merge pull request #21 from vivz/develop
Good work guys!
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| .DS_Store | ||
| *pycache* | ||
| *.pyc | ||
| data/ | ||
| *.DS_Store | ||
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| from django.core.management.base import BaseCommand, CommandError | ||
| from LL1_Academy.models import Grammar | ||
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| class Command(BaseCommand): | ||
| help = 'This will delete all grammars in the database be careful' | ||
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| def handle(self, *args, **options): | ||
| Grammar.objects.all().delete() |
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| from django.core.management.base import BaseCommand, CommandError | ||
| import time | ||
| from LL1_Academy.tools import MassGrammarGenerator | ||
| import os | ||
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| class Command(BaseCommand): | ||
| help = 'This command will populate the database with a small set of grammars' | ||
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| def handle(self, *args, **options): | ||
| #Number of randomly generated grammars | ||
| num = 100 | ||
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| #Number variables this run will include. | ||
| #For example [2,3] will run the script to generate | ||
| #grammars with 2 variables and 3 variables | ||
| nVariables = [2, 3] | ||
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| nonTerminals = ['A','B','C','D'] | ||
| terminals = ['x','y','z','w'] | ||
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| for n in nVariables: | ||
| start_time = time.time() | ||
| mg = MassGrammarGenerator.MassGrammarGenerator(n) | ||
| mg.run(num,nonTerminals[:n],terminals) | ||
| print("{}Variables: {} seconds---".format(n,(time.time() - start_time))) |
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| @@ -1,4 +1,9 @@ | ||
| {% extends "LL1_Academy/_base.html" %} | ||
| {% block content %} | ||
| <a href="learn"><button>Start Learning</button></a> | ||
| <div id="landingPage"> | ||
| <h1 id="siteTitle">LL(1) Academy</h1> | ||
| <div id="startButton"> | ||
| <a href="learn"><button class="button" id="startLearning">Start Learning</button></a> | ||
| </div> | ||
| </div> | ||
| {% endblock %} |
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| Original file line number | Diff line number | Diff line change |
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| @@ -1,3 +1,41 @@ | ||
| from django.test import TestCase | ||
| from django.test import TestCase, Client | ||
| from django.urls import reverse | ||
| from LL1_Academy.models import Grammar, Question | ||
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| class UrlTest(TestCase): | ||
| def setup(self): | ||
| g = Grammar(prods="{'A': ['xA', 'Bz'],'B': ['yB']}", nonTerminals="AB", terminals="xyz", startsymbol="A") | ||
| g.save() | ||
| q = Question(gid=g, qnum=0, category="FI", symbol="A", answer="xy") | ||
| q.save() | ||
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| def test_index1(self): | ||
| response = self.client.get('/') | ||
| self.assertEqual(response.status_code, 200) | ||
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| def test_index2(self): | ||
| response = self.client.get('/index') | ||
| self.assertEqual(response.status_code, 200) | ||
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| def test_learn(self): | ||
| response = self.client.get('/learn') | ||
| self.assertEqual(response.status_code, 200) | ||
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| def test_get_question(self): | ||
| response = self.client.get('/get_question') | ||
| self.assertEqual(response.status_code, 200) | ||
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| def test_check_answer(self): | ||
| response = self.client.get('/check_answer') | ||
| self.assertEqual(response.status_code, 404) | ||
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| class RenderingTest(TestCase): | ||
| def test_index1(self): | ||
| response = self.client.get('/') | ||
| self.assertTemplateUsed(response, 'LL1_Academy/index.html') | ||
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| def test_index1(self): | ||
| response = self.client.get('/learn') | ||
| self.assertTemplateUsed(response, 'LL1_Academy/learn.html') | ||
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| # Create your tests here. |
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| from LL1_Academy.tools import GrammarChecker,SingleGrammarGenerator | ||
| from LL1_Academy.tools import GrammarChecker,SingleGrammarGenerator, SvmLearn | ||
| from LL1_Academy.models import Grammar, Question | ||
| import os | ||
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| # | ||
| #Description: The MassGrammarGenerator class outputs a specified number of | ||
| # random grammars based on the given list of terminals and non-terminals | ||
| # | ||
| #Input: num: the number of grammars to be generated | ||
| # nonTerminals: a list of characters representing the nonterminals of length 2-4 | ||
| # Terminals: a list of characters representing the terminals of length 2-4 | ||
| # | ||
| #Output: a specified number of grammars written to some plaintext files under ./txt. | ||
| # You may need to create this directory first before running this script in order | ||
| # for it to work. | ||
| # | ||
| #Usage: | ||
| # mg = MassGrammarGenerator() | ||
| # mg.run(1000，['S','F'],['a','(','+',')']) | ||
| # | ||
| #Description: The MassGrammarGenerator class randomly generates grammar and filter | ||
| #out the interesting ones to store in the database. | ||
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| script_dir = os.path.dirname(__file__) #<-- absolute dir the script is in | ||
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| class MassGrammarGenerator: | ||
| def __init__(self): | ||
| self.statusSummary = {-1:0, | ||
| 0:0, | ||
| 1:0, | ||
| 2:0} | ||
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| def __init__(self, n): | ||
| self.statusSummary = {"LL1":0, "non-LL1":0} | ||
| self.gc = GrammarChecker.GrammarChecker() | ||
| self.g = SingleGrammarGenerator.SingleGrammarGenerator() | ||
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| #builds the ML model using our trainingData | ||
| fI = os.path.join(script_dir, 'trainingData/'+str(n)+'var-interesting') | ||
| fN = os.path.join(script_dir, 'trainingData/'+str(n)+'var-not-interesting') | ||
| self.learnModel = SvmLearn.SvmLearn(n,fI,fN) | ||
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| def run(self,num, nonTerminals, terminals, writeToTxt = False): | ||
| gc = GrammarChecker.GrammarChecker() | ||
| g = SingleGrammarGenerator.SingleGrammarGenerator() | ||
| n = len(nonTerminals) | ||
| if writeToTxt: | ||
| f = open(os.path.join(script_dir, 'data/'+str(n)+'Variables'), 'w') | ||
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| #generate num grammars and check them, discard the left hand recursion ones | ||
| def run(self,num, nonTerminals, terminals): | ||
| self.nonTerminals = nonTerminals | ||
| self.terminals =terminals | ||
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| #generate num grammars, filter them and write the good ones to DB | ||
| for i in range(0,num): | ||
| grammar = g.generate(nonTerminals, terminals) | ||
| firstSets, followSets, parsingTable, status, reachable = gc.solve(grammar,'A',False) | ||
| self.statusSummary[status] += 1 | ||
| if (not status == -1) and reachable: | ||
| self.statusSummary[2] += 1 | ||
| if writeToTxt: | ||
| f.write(str(grammar)+'\n \tFirst Set: '+str(firstSets)+'\n \tFollow Set: '+str(followSets)+'\n \tReachable: '+ str(reachable) +'\n\n') | ||
| else: | ||
| #write to DB if not to TXT | ||
| newG = Grammar(prods=str(grammar), nonTerminals=''.join(nonTerminals), terminals=''.join(terminals), startSymbol='A') | ||
| newG.save() | ||
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| #LL1 Question | ||
| ans = 'True' if status==0 else 'False' | ||
| qLL = Question(gid=newG,qnum=0,category='LL',answer=ans) | ||
| qLL.save() | ||
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| #ParseTable Question | ||
| qPT = Question(gid=newG,qnum=1,category='PT',answer=str(parsingTable)) | ||
| qPT.save() | ||
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| #First and Follow Set | ||
| qnum = 2 | ||
| for v in nonTerminals: | ||
| qFirst = Question(gid=newG,qnum=qnum,category='FI',symbol=v,answer=''.join(firstSets[v])) | ||
| qFirst.save() | ||
| qnum +=1 | ||
| qFollow = Question(gid=newG,qnum=qnum,category='FO',symbol=v,answer=''.join(followSets[v])) | ||
| qFollow.save() | ||
| qnum +=1 | ||
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| #print a small summary | ||
| print(str(n)+"Variables:\nleft recursion: "+str(self.statusSummary[-1]) | ||
| +"; LL(1): " + str(self.statusSummary[0]) | ||
| +"; not LL(1): " + str(self.statusSummary[1]) | ||
| + "; Reachable: " + str(self.statusSummary[2])) | ||
| self.createOneGrammar() | ||
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| #prints a small report | ||
| print(str(len(self.nonTerminals)) + " Variables: " | ||
| + str(self.statusSummary["LL1"] + self.statusSummary["non-LL1"]) | ||
| + " interesting grammars picked out of "+str(num)+"\n\tLL1: " + str(self.statusSummary["LL1"]) | ||
| + "\n\tnot LL(1): " + str(self.statusSummary["non-LL1"])) | ||
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| def createOneGrammar(self): | ||
| #This function randomly generates a single grammar, and saves it to the DB if | ||
| #it is not left-recursion and interesting | ||
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| #generate a single grammar randomly | ||
| grammar = self.g.generate(self.nonTerminals, self.terminals) | ||
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| #get answers using the checker | ||
| #result = firstSets, followSets, parsingTable, status, reachable | ||
| result = self.gc.solve(grammar,'A',False) | ||
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| #Abandon left recursive grammars and grammars with non-reachable variables | ||
| if (result[3] == -1) or (not result[4]): | ||
| return | ||
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| #If the ML model decidese it's interesting, save to DB | ||
| prediction = self.learnModel.predictGrammar(grammar,result[0],result[1]) | ||
| if prediction[0]==1: | ||
| self.saveToDB(grammar,result) | ||
| print("YES: "+str(grammar)+"\n\tFirst: "+str(result[0])+"\n\tFollow: "+str(result[1])) | ||
| else: | ||
| print("NO: "+str(grammar)+"\n\tFirst: "+str(result[0])+"\n\tFollow: "+str(result[1])) | ||
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| def saveToDB(self,grammar,result): | ||
| #This function takes the grammar and the result returned by gc.solve. It | ||
| #populates the Grammar and Question table in DB with the correct fields | ||
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| firstSets, followSets, parsingTable, status, reachable = result | ||
| newG = Grammar(prods=str(grammar), nonTerminals=''.join(self.nonTerminals), | ||
| terminals=''.join(self.terminals), startSymbol='A') | ||
| newG.save() | ||
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| #First and Follow Set | ||
| qnum = 0 | ||
| for v in self.nonTerminals: | ||
| qFirst = Question(gid=newG,qnum=qnum,category='FI', | ||
| symbol=v,answer=''.join(firstSets[v])) | ||
| qFirst.save() | ||
| qnum +=1 | ||
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| for v in self.nonTerminals: | ||
| qFollow = Question(gid=newG,qnum=qnum,category='FO', | ||
| symbol=v,answer=''.join(followSets[v])) | ||
| qFollow.save() | ||
| qnum +=1 | ||
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| #LL1 Question | ||
| if status == 0: | ||
| ans = 'True' | ||
| self.statusSummary["LL1"]+=1 | ||
| else: | ||
| ans = 'False' | ||
| self.statusSummary["non-LL1"]+=1 | ||
| qLL = Question(gid=newG,qnum=qnum,category='LL',answer=ans) | ||
| qLL.save() | ||
| qnum+=1 | ||
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| #Parse Table Question | ||
| qPT = Question(gid=newG,qnum=qnum,category='PT',answer=str(parsingTable)) | ||
| qPT.save() | ||
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