-v      (--vocabulary) generates the vocabulary from the input file into
            the output file, excluding words in reserved words file.

            Example: 
                $ Sblocker -v inputFile outputFile rwFile

            Definition:
                inputFile   File path with the full data that wants to be processed.
                outputFile  File path were the program will store the solution.
                rwFile      File path were the reserved words are stored.


    -co     (--corpus) generates one different corpus per each data type 
            received as argument after the input file and the reserved words 
            file.  

            Example:
                $ Sblocker -co inputFile rwFile Corpus1 Corpus2 ... CorpusX

            Definition:
                inputFile   File path with the full data that wants to be processed.
                rwFile      File path were the reserved words are stored.
                CorpusX     Name of the data type that wants to be searched and generated
                            corpus at. The input file must have them on the first column
                            and separating the rest of the line with a ",", as if it were
                            at the first column of a csv.


    -h      (--help) prints the user manual for the program.

            Example:
                $ testClassifier -h


    -l      (--learner) calculates the proabilities for each token for each corpus and
            stores it into files named as learned_CORPUS.txt on the output folder.

            Example: 
                $ Sblocker -l vocFile Corpus1 Corpus2 ... CorpusX

            Definition:
                vocFile     Vocabulary file generated with --vocabulary option
                CorpusX     Name of the data type that wants to be searched and generated
                            corpus at. The input file must have them on the first column
                            and separating the rest of the line with a ",", as if it were
                            at the first column of a csv.

    -cl     (--classify) classifies the given input into the created classes readed
            from the learned data.
                       
            Example:
                $ Sblocker -cl corpusTest rwFile Learn1 Learn2 ... LearnN

            Definition:
                corpusTest  File path which has one sentence thata is going to be classified
                            on each line.
                rwFile      File path were the reserved words are stored.
                LearnN      File path with the learned output data from using -l option.


    -e      (--error) calulates the error and success percentage of the classification.

            Example: 
                $ Sblocker -e resumeData expectResume

            Definition:
                resumeData  File path to the resume generated by classifier.
                expectData  File path to the expected data, it must be with the same syntax
                            as resumeData (one line per sentence class and in same order as
                            origin corpusTest).


    -u      (--updateDatabase) Updates the emails database by adding
            all the emails in the folder inputs/GenerateMails to database.


    -t      (--generateTest) Generate the test files for the program,
                takes 20% random data from mail-train.csv for geting this
                tests.

    Returns success unless an error occurs  

    Copyright (c) 2021 Adrián Epifanio, Óscar Hernández and Fabio Ovidio

    Permission is hereby granted, free of charge, to any person obtaining a copy  
    of this software and associated documentation files (the \Software\), to deal  
    in the Software without restriction, including without limitation the rights  
    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell  
    copies of the Software, and to permit persons to whom the Software is  
    furnished to do so, subject to the following conditions:  

    The above copyright notice and this permission notice shall be included in all  
    copies or substantial portions of the Software.  

    THE SOFTWARE IS PROVIDED \AS IS\, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR  
    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,  
    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE  
    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER  
    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,  
    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE  
    SOFTWARE.