### The Task

Consider data coming in as a matrix of R samples of C dimensional data that is represented as a R x C matrix. First read in a number k that is the number of eigenvectors you want to keep. Then read in the matrix in standard form of the number of rows, the number of columns, then the data. Then translate the data into Z-score by column, that is, convert the numbers in each column to the Z-score for in column.

Write a C/C++ program called pca that uses principle component analysis to pick the top k eigenvalues and encodes the data in k dimensional space instead of C dimensions. Essentially you will be compressing the data by translating the data to the k most significant eigenvectors. Eigenvalues and eigenvectors can be computed in the matix library. Here is a help sheet on PCA.

Your output is

### Submission

Homework will be submitted as an **uncompressed** tar file to the
homework submission page linked from the class web page. You can
submit as many times as you like. **The LAST file you submit BEFORE the
deadline will be the one graded.** For all submissions you will receive
email at your uidaho.edu mail address giving you some automated feedback on the unpacking and
compiling and running of code and possibly some other things that can be autotested.

Have fun.