# 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)
#input_data = ['low', 'high', 4, 4, 'big', 'med', 'acc']
#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)
