def main():
userInput = parser.getInput()
fileList = parser.getFiles(userInput['train'])
pdata = parser.parseFiles(fileList)
allsent = ''
for f in pdata:
allsent += f[3]
all_words = FreqDist(w.lower()
for w in word_tokenize(allsent)
if w not in stopwords.words('english') )
global top_words
top_words = all_words.keys()[:500]
# pdata = getParseData()
featdata = featureAggregator(pdata)
print featdata[:10]
def main():
userInput = parser.getInput()
fileList = parser.getFiles(userInput['train'])
parsedata = parser.parseFiles(fileList)
allsent = ''
for f in parsedata:
allsent += f[3]
all_words = FreqDist(w.lower()
for w in word_tokenize(allsent)
if w not in stopwords.words('english') )
global top_words
top_words = all_words.keys()[:500]
featdata = extractor.featureAggregator(parsedata)
# print featdata[20]
print "Sample Data Item:\n\n"
print "%20s %4s %4s %20s" % ("FILENAME", "LINENUM", "VOTE", "SENTENCE" )
print "-" * 79
print "%10s %4s %4s %20s" % (featdata[20][0], featdata[20][1], featdata[20][2], featdata[20][3])
print "\n\nFeatures of this Data Item"
print "-" * 79
for key,val in featdata[20][4].items():
print "%50s : %10s" % (key, val )
# print "A sample feature: %s" % (featdata[20][4])
allacc = splitfeatdata(featdata)
print "\n\n"
print "-" * 60
print "Accuracy Values: %s" % (allacc)
print "==" * 60
print "Overall Classifier Accuracy %4.4f " % (sum(allacc)/len(allacc))
def main():
database = raw_input("Enter name of .db file >")
conn = sqlite3.connect(database)
cursor = conn.cursor()
cursor.execute('''
SELECT COUNT(*) FROM SQLITE_MASTER;''')
nTables = cursor.fetchone()
nTables = (nTables[0]) / 2 # account for input table + FD table
initScreen = raw_input("Please select an option:\n"
"1. Compute attribute closure\n"
"2. Compare FD tables\n"
"3. Normalize a table\n")
if initScreen == '1':
print getAttributeClosure(cursor)
if initScreen == '2':
print compareFDs(cursor)
elif initScreen == '3':
i = 0
while i < nTables: # loop for multiple tables within db, subject to change condition
inTable = raw_input("Enter name of input table or q to quit >")
if inTable == 'q': # break from loop if user wishes to quit
break
inFDtable = raw_input("Enter name of FD table >")
inRows, inFDs, fdDict = getInput(inTable, inFDtable, cursor)
os.system("clear")
choice = raw_input(
"Please choose one of the following "
"(entering anything else will terminate the program):\n"
"1. Convert to BCNF\n2. Convert to 3NF\n")
if choice == '1':
# convert to BCNF
convertbcnf(inRows, inFDs, cursor, conn, fdDict, inTable[5:])
elif choice == '2':
# convert to 3nf
convert3nf(inRows, inFDs, cursor, conn, fdDict, inTable[5:])
else:
# quit
return
i += 1
def main():
userInput = parser.getInput()
fileList = parser.getFiles(userInput['train'])
pdata = parser.parseFiles(fileList)
allsent = ''
for f in pdata:
allsent += f[3]
all_words = FreqDist(w.lower() for w in word_tokenize(allsent)
if w not in stopwords.words('english'))
global top_words
top_words = all_words.keys()[:500]
# pdata = getParseData()
featdata = featureAggregator(pdata)
print featdata[:10]
