Linear discriminant analysis using alglib

Question

I've been asked to perform a linear discriminant analysis on a set of data for one of my projects. I'm using ALGLIB (C++ version) which has a fisherlda function but I need some help understanding how to use it.

The user answers a set of 6 questions (answers are a number from 1-7) which gives me an example data set of eg {1,2,3,4,5,6}. I then have 5 classes of 6 values each eg {0.765, 0.895, 1.345, 2.456, 0.789, 5.678}. The fisher lda function takes a 2 dimensional array of values and returns another 1d array of values (that I have no idea what they mean).

As I understand it I need to see to which class the users answers best fit?

Any help understanding LDA and/or how I can use this function would be greatly appreciated.

EDIT:

Here's the definition of the function I'm trying to use:

/*************************************************************************
Multiclass Fisher LDA

Subroutine finds coefficients of linear combination which optimally separates
training set on classes.

INPUT PARAMETERS:
    XY          -   training set, array[0..NPoints-1,0..NVars].
                    First NVars columns store values of independent
                    variables, next column stores number of class (from 0
                    to NClasses-1) which dataset element belongs to. Fractional
                    values are rounded to nearest integer.
    NPoints     -   training set size, NPoints>=0
    NVars       -   number of independent variables, NVars>=1
    NClasses    -   number of classes, NClasses>=2


OUTPUT PARAMETERS:
    Info        -   return code:
                    * -4, if internal EVD subroutine hasn't converged
                    * -2, if there is a point with class number
                          outside of [0..NClasses-1].
                    * -1, if incorrect parameters was passed (NPoints<0,
                          NVars<1, NClasses<2)
                    *  1, if task has been solved
                    *  2, if there was a multicollinearity in training set,
                          but task has been solved.
    W           -   linear combination coefficients, array[0..NVars-1]

  -- ALGLIB --
     Copyright 31.05.2008 by Bochkanov Sergey
*************************************************************************/
void fisherlda(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nvars, const ae_int_t nclasses, ae_int_t &info, real_1d_array &w);

Answer 1

You are using fisherlda function which is an implementation of LDA algorithm.

LDA(Linear Discriminant Analysis) is aimed to find a linear combination of features that best characterizes or separates two or more classes of objects or events.

Assume the line y=wx(w,x both stand for a matrix here),so the given result of fisherlad is a 1d array of coefficients which is w.Then you can use this line to determine which class the answers belong.