def main(args):
CREATE_INDIVIDUAL = False
files = []
if len(args) == 0:
print "No input arguments. Use -h or --help for more info."
exit()
for argument in args:
if argument in ["-h", "--help"]:
print "Magazine creator assistant:"
print "Two ways"
print " 1) python args.py [input files] [destination file] --> all input files are joined into one destination file."
print " 2) python args.py --nojoin [input files] --> each input file is saved as odt as {input_file}.odt"
exit()
elif argument in ["-n", "--nojoin"]:
CREATE_INDIVIDUAL = True
else:
files.append(argument)
if not CREATE_INDIVIDUAL:
glob = OpenDocumentText()
dest = files[-1]
files = files[:-1]
if files == []:
print "ERROR: NO INPUT FILES. EXITING..."
exit()
c = 0
for f in files:
defs = FileParser(f).parse()
ta = TemplateApplier(defs["TEMPLATE"], defs)
xml = ta.get()
addit = False
if not CREATE_INDIVIDUAL and not c == len(files) - 1:
addit = True
obj = FileCreator(xml, addit).get()
obj.text = obj.text
if CREATE_INDIVIDUAL:
doc = OpenDocumentText()
for style in obj.styles:
doc.styles.addElement(style)
for t in obj.text:
doc.text.addElement(t)
doc.save(f + ".odt")
else:
for style in obj.styles:
glob.styles.addElement(style)
for t in obj.text:
glob.text.addElement(t)
c += 1
if not CREATE_INDIVIDUAL:
glob.save(dest)
from lib.NaiveBayesClassifier import NaiveBayesClassifier
from lib.FileParser import FileParser
fileParser = FileParser()
# cvs format
#fileParser.parseDBData("db_car.txt")
#fileParser.printDBData()
# weka format
fileParser.buildFileDescription("car.data", "db_car.txt")
fileParser.parseFileDescription("db_car.txt")
fileParser.parseCVSPatterns("car.data")
fileParser.parseCVS2Numeric("car_numeric.data")
fileParser.convertTestFileNumeric("car-prueba.data", "car-prueba_numeric.data")
classifier = NaiveBayesClassifier(fileParser)
classifier.train(fileParser.fvector, fileParser.label)
#classifier.printTables()
classifier.testFile("car-prueba.data")
#input_data = ['vhigh', 'vhigh', 2, 2, 'med', 'med', 'unacc']
#classifier.test(input_data)
from lib.NaiveBayesClassifier import NaiveBayesClassifier
from lib.FileParser import FileParser
fileParser = FileParser()
fileParser.parseFileDescription("db_tennis.txt")
#fileParser.printDBData()
fileParser.parseCVSPatterns("tennis.data")
#fileParser.printPatterns()
classifier = NaiveBayesClassifier(fileParser)
classifier.train(fileParser.fvector, fileParser.label)
#classifier.printTables()
################## VALIDACION
input_data = ['Sunny', 'Cool', 'High', 'Strong', 'No']
classifier.test(input_data)