def testit():
    a = ExtendedNaiveBayes('testing1')
    print('fitting data')
    file_to_test = 'reverse.csv'
    a.fit(file_to_test)
    print('done')
    with open(file_to_test, 'r') as f:
        for (i, line) in enumerate(f):
            line = line.strip()
            if (i == 0):
                headers = line.split(',')
                continue
            headers_submit = []
            csv_data_submit = []
            loc = ''
            for (j, signal) in enumerate(line.split(',')):
                if (j == 0):
                    loc = signal
                    continue
                if (signal.strip() == ''):
                    continue
                headers_submit.append(headers[j])
                csv_data_submit.append(int(round(float(signal.strip()))))
            print(loc)
            a.predict_proba(headers_submit, csv_data_submit)
def testit():
    a = ExtendedNaiveBayes('testing1')
    print('fitting data')
    file_to_test = 'reverse.csv'
    a.fit(file_to_test)
    print('done')
    with open(file_to_test, 'r') as f:
        for (i, line) in enumerate(f):
            line = line.strip()
            if (i == 0):
                headers = line.split(',')
                continue
            headers_submit = []
            csv_data_submit = []
            loc = 
            for (j, signal) in enumerate(line.split(',')):
                if (j == 0):
                    loc = signal
                    continue
                if (signal.strip() == ):
                    continue
                headers_submit.append(headers[j])
                csv_data_submit.append(int(round(float(signal.strip()))))
            print(loc)
            a.predict_proba(headers_submit, csv_data_submit)