install rpy2 on Mac+R v 3.4.1 + virtenv

It's just pain :/ So small cheat sheet.

1) add to ~/.bash_profile
export R_HOME=/usr/local/Cellar/r/3.4.1/lib/R

2) Copy over (oh yes, facepalm) headers

cp -R /usr/local/Cellar/r/3.4.1/lib/R/include/R* /usr/local/include/

don't forget to delete them after 

k-means and pca against mnist

Recently I've implemented SVD low-rank approximation for image compression, but then I thought that actually this low-rank approximation shows direction with highest variance... and that means we could estimate how many classes we should pick to train K-Means...

Octave online

Guys, have you seen Octave online? quite impressive thing, you can store!!! your scripts, it can plot and does symbolic evaluations! Basic Machine Learning, Linear Algebra, Convex optimization, etc courses can be done right without any investment in an expensive software (hello Matlab basic bundle for 5k). w00h00

http://octave-online.net/

SVD low rank approximation for pictures (SVD image compression on Python3)

Well, tons of posts were written about it. It wont be "yet another one". But I implemented it anyway, just for fun. I wont explain theoretical part, but will suggest some links about it.

So good docs are:
http://timbaumann.info/svd-image-compression-demo/

Some good docs (thx Berkeley). This book is abandoned now, but it's good to read and store link for future reference.
http://inst.eecs.berkeley.edu/~ee127a/book/login/l_svd_low_rank.html
http://inst.eecs.berkeley.edu/~ee127a/book/login/l_svd_apps_image.html

Just nice presentation about PCA and SVD
http://math.arizona.edu/~brio/VIGRE/ThursdayTalk.pdf

and the source svd-img-compression.py

magic happens in np.dot() we pickup just take approx_rank singular values and discard others, so get some compression.

Result is below, on left side depicted original greyscale picture with rank equals 440 on a right side depicted low rank approximation with rank equals 50

Tensorflow in Gentoo virtualenv

Just tried to install tensorflow in Gentoo virtual env with python 3.4 and failed

k-means and MNIST dataset

Just thought few days ago how should look avarage 0 or 1 or 9 or any number. So downloaded MNIST dataset and implemented k-means algorithm (I could calculate average vector in every cluster, but that wont be interesting).

Back to the Linux or how I debugged silent crash

Just got tired from Mac and decided to move to my old Gentoo Linux. After recompiling world (oh yeah, all from sources) I stuck with SIGSEGV with plot command in Octave (Matlab like math package).

When I tried to plot anything it crashed with SIGSEGV without any obvious reason. So I got to go deeper to fix it. Gentoo supports nice debug options all that one needs is just to enable them
https://wiki.gentoo.org/wiki/Debugging

After that simple
# sudo ulimit -c 99999999
# octave
octave:1> plot(1)

then crash with core in the current folder. Now one need to unwind the stack
# gdb /usr/lib/debug/usr/bin/octave-cli-3.8.2.debug core
(gdb) bt
#0  fl_create_gl_context (g=0x0) at Fl_Gl_Choice.H:102
#1  Fl_Gl_Window::make_current (this=0xc12430) at Fl_Gl_Window.cxx:168
#2  0x00007fc1a5706447 in plot_window::show_canvas (this=0xc11bb0,
    this=0xc11bb0) at dldfcn/__init_fltk__.cc:935
....
#88 0x00007fc1b715a620 in __libc_start_main () from /lib64/libc.so.6
#89 0x0000000000400979 in _start ()

So, I didnt recompile nvidia drivers or my opengl config is bad. I had problems with opengl config, repoint it with eselect opengl and run X11 again.

Problem has solved