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  // generate [0..n-1]
  auto seq = [](size_t n) -> std::vector<size_t> {
      std::vector<size_t> v(n);
      for (size_t i=0; i<n; ++i) v[i] = i;
      return v;
  };
  auto index = seq(n);

  // n * n distance matrix
  std::vector<D> dists(n * n);

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import io
import avro.schema
import avro.io
import lipsum
import random

from kafka.client import KafkaClient
from kafka.producer import SimpleProducer, KeyedProducer

g = lipsum.Generator()

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worker_processes 2;

error_log /var/log/nginx/error.log;
pid /var/run/nginx.pid;

events {
    worker_connections 1024;
    use epoll;
}

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[unix_http_server]
file=/tmp/supervisor.sock                       ; path to your socket file

[supervisord]
logfile=/var/log/supervisord/supervisord.log    ; supervisord log file
logfile_maxbytes=50MB                           ; maximum size of logfile before rotation
logfile_backups=10                              ; number of backed up logfiles
loglevel=error                                  ; info, debug, warn, trace
pidfile=/var/run/supervisord.pid                ; pidfile location
nodaemon=false                                  ; run supervisord as a daemon

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"""
(C) Mathieu Blondel - 2010
License: BSD 3 clause

Implementation of the collapsed Gibbs sampler for
Latent Dirichlet Allocation, as described in

Finding scientifc topics (Griffiths and Steyvers)
"""

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Introduction to Kafka

Kafka acts as a kind of write-ahead log (WAL) that records messages to a persistent store (disk) and allows subscribers to read and apply these changes to their own stores in a system appropriate time-frame.

Terminology:

• Producers send messages to brokers
• Consumers read messages from brokers
• Messages are sent to a topic

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########################### GTEST
# Enable ExternalProject CMake module
INCLUDE(ExternalProject)

# Set default ExternalProject root directory
SET_DIRECTORY_PROPERTIES(PROPERTIES EP_PREFIX ${CMAKE_BINARY_DIR}/third_party)

# Add gtest
# http://stackoverflow.com/questions/9689183/cmake-googletest
ExternalProject_Add(

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import numpy as np
from matplotlib import pylab as plt
#from mpltools import style # uncomment for prettier plots
#style.use(['ggplot'])

'''
function definitions
'''
# generate all bernoulli rewards ahead of time
def generate_bernoulli_bandit_data(num_samples,K):

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import numpy as np
from math import exp, log

"""
SGD for logistic loss + factorization machines
The code follows this paper:
[1] http://www.ics.uci.edu/~smyth/courses/cs277/papers/factorization_machines_with_libFM.pdf
"""

def sigmoid(x):

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SLIDES := $(patsubst %.md,%.slides.pdf,$(wildcard *.md))
HANDOUTS := $(patsubst %.md,%.handout.pdf,$(wildcard *.md))

all : $(SLIDES)

%.slides.pdf : %.md
	pandoc --latex-engine=xelatex --toc $^ -t beamer --slide-level 2 -o $@

%.handout.pdf : %.md
	pandoc $^ -t beamer --slide-level 2 -V handout -o $@