syadlowsky

/home/steve/.rvm/gems/ruby-1.9.2-p290@tirana/gems/mongo-1.6.0/lib/mongo/util/logging.rb:34:in `instrument': undefined method `level' for #<Mongoid::Logger:0x00000002c9d838> (NoMethodError)
	from /home/steve/.rvm/gems/ruby-1.9.2-p290@tirana/gems/mongo-1.6.0/lib/mongo/cursor.rb:467:in `send_initial_query'
	from /home/steve/.rvm/gems/ruby-1.9.2-p290@tirana/gems/mongo-1.6.0/lib/mongo/cursor.rb:458:in `refresh'
	from /home/steve/.rvm/gems/ruby-1.9.2-p290@tirana/gems/mongo-1.6.0/lib/mongo/cursor.rb:128:in `next'
	from /home/steve/.rvm/gems/ruby-1.9.2-p290@tirana/gems/mongo-1.6.0/lib/mongo/db.rb:509:in `command'
	from /home/steve/.rvm/gems/ruby-1.9.2-p290@tirana/gems/mongo-1.6.0/lib/mongo/connection.rb:606:in `check_is_master'
	from /home/steve/.rvm/gems/ruby-1.9.2-p290@tirana/gems/mongo-1.6.0/lib/mongo/connection.rb:392:in `connect'
	from /home/steve/.rvm/gems/ruby-1.9.2-p290@tirana/gems/mongo-1.6.0/lib/mongo/connection.rb:555:in `setup'
	from /home/steve/.rvm/gems/ruby-1.9.2-p290@tirana/gems/mongo-1.6.0/lib/mongo/

syadlowsky

Bridge::initialize :api_key => 'abcdefgh' do |bridge|
  puts 'connected!'
  bridge.get_service("adder") do |service|
    service.add(3, 5) do |ret_value|
      puts ret_value
    end
  end
end

syadlowsky

#include<stdlib.h>

// number of floats that fit in L1 cache
#define L1_SIZE 1024
#define L1_MATRIX_ALLOT 512
// number of floats that fit in L2 cache
#define L2_SIZE 8192
// number of floats that fit in L2 cache
#define L3_SIZE 8192

syadlowsky

mongo-problem.rb:42
video_group.update!({ 'videos.video_id' => 'video1', :multi => true }, '$set' => {
(rdb:1) s
/home/steve/.rvm/gems/ruby-1.9.3-p194/gems/mongomatic-0.8.2/lib/mongomatic/base.rb:265
update(opts.merge(:safe => true),update_doc)
(rdb:1) s
/home/steve/.rvm/gems/ruby-1.9.3-p194/gems/mongomatic-0.8.2/lib/mongomatic/base.rb:243
if opts[:raise] == true
(rdb:1) s
/home/steve/.rvm/gems/ruby-1.9.3-p194/gems/mongomatic-0.8.2/lib/mongomatic/base.rb:248

syadlowsky

postgres=# CREATE LANGUAGE plpythonu;
ERROR:  language "plpythonu" already exists
postgres=# \q
postgres@xxxxxx:/home/ubuntu/xxxxxxx/django_utils$ psql geodjango -f voronoi_python.sql
psql:voronoi_python.sql:826: ERROR:  language "plpythonu" does not exist

syadlowsky

The plan
========

1. [http://www.jesshamrick.com/2012/09/10/absolute-beginners-guide-to-emacs/](http://www.jesshamrick.com/2012/09/10/absolute-beginners-guide-to-emacs/)
2. [http://www.gnu.org/software/emacs/manual/](http://www.gnu.org/software/emacs/manual/) (just the main section)
3. [http://bling.github.io/blog/2013/10/27/emacs-as-my-leader-vim-survival-guide/](http://bling.github.io/blog/2013/10/27/emacs-as-my-leader-vim-survival-guide/)
4. [http://www.gnu.org/software/emacs/manual/eintr.html](http://www.gnu.org/software/emacs/manual/eintr.html)
5. [https://github.com/ronuchit/Settings/blob/master/.emacs](https://github.com/ronuchit/Settings/blob/master/.emacs)
6. [http://www.jesshamrick.com/2012/09/18/emacs-as-a-python-ide/](http://www.jesshamrick.com/2012/09/18/emacs-as-a-python-ide/)

syadlowsky

c_0 = -0.969474842881623111168210
c_2 = 4.364528972627628178315717
c_4 = -2.422793242101592081638728 

function fast_cos(t::Real)
    # Computes cos(pi*t) using a Chebyshev approximation.
    # This approximation is minimax optimal on [-1, 1].
    # By scaling by pi, we find an optimal approximation on
    # the entire range of cos(x). Because cos(x - pi) = -cos(x),
    # we can take the remainder with 2, and shift [0, 2] |-> [-1, 1],

syadlowsky

class foo:
  def __init__(self, a={}):
     self.a = a

one = foo()
two = foo()
two.a["a"] = 2
print one.a

syadlowsky

SIGMA = args.sigma
NUM_RAND_FEATURES = args.num_rand_features

data_hdf5 = h5py.File(args.filename,'r+')
d = data_hdf5['train']['x'][0].shape[0]
n = len(data_hdf5['train']['x'])

train_y = np.array(data_hdf5['train']['t'])

features = np.array(data_hdf5['train']['x'])

syadlowsky

simple.logistic = function(x, y, w, iters=30, l1_penalty=0) {
  d = ncol(x)
  n = nrow(x)
  x_c = colMeans(x)
  x = (x - matrix(rep(x_c, n), nrow=n, byrow = T))
  x_s = sqrt(colMeans(x^2))
  x = (x / matrix(rep(x_s, n), nrow=n, byrow = T))
  beta = rep(0,d)
  p = weighted.mean(y, w = w)
  beta_0 = log(p) - log(1-p)