lila arreglos

var int[15] grades, age;
text[15] students;

main
{
    grades[0] = 90;
    grades[1] = 71;
    grades[2] = 95;
    grades[3] = 76;
    grades[4] = 9;
    grades[5] = 100;
    grades[6] = 90;
    grades[7] = 85;
    grades[8] = 99;
    grades[9] = 100;
    grades[10] = 100;
    grades[11] = 90;
    grades[12] = 97;
    grades[13] = 96;
    grades[14] = 89;

    age[0] = 18;
    age[1] = 18;
    age[2] = 19;
    age[3] = 19;
    age[4] = 17;
    age[5] = 18;
    age[6] = 19;
    age[7] = 16;
    age[8] = 18;
    age[9] = 19;
    age[10] = 19;
    age[11] = 20;
    age[12] = 20;
    age[13] = 20;
    age[14] = 21;

    students[0] = "LAURA";
    students[1] = "LUISA";
    students[2] = "JUAN";
    students[3] = "ANTONIO";
    students[4] = "LUIS";
    students[5] = "ITZEL";
    students[6] = "ERNESTO";
    students[7] = "ALEXANDER";
    students[8] = "FRANCISCO";
    students[9] = "JULIAN";
    students[10] = "BERTHA";
    students[11] = "MONICA";
    students[12] = "JULIAN";
    students[13] = "MANUEL";
    students[14] = "JESUS";

    // Where to start?
    tellMeWhatToUse(grades);

    // lets see the data visualy
    quickShow(grades,students);
    quickShow(grades,age);

    // Ok lets see the mean
    mean(grades);

    // Now lets see who are the atypical grades
    getOutliers(grades);

    // Lets see if there is a correlation between ages and grades
    pearsoneCorrelation(grades,age);

    // Lets bee cool and round the 99 to 100
    fillValue(grades,99,100);

    // Hmm... now lets think what would happend if those atypical students were not present in the data
    removeOutliers(grades);

    // lets see now
    printMeasures(grades);

}

python main.py <yourfile>.li

doskey antlr4=java org.antlr.v4.Tool $*
doskey grun =java org.antlr.v4.gui.TestRig $*