johnmyleswhite

> v <- c(1, 2, 3, 4)
> v[1]
[1] 1
> v[4]
[1] 4
> v[0]
numeric(0)
> v[5]
[1] NA
> v[-1]

sergeyf

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

# generate all bernoulli rewards ahead of time
def generate_bernoulli_bandit_data(num_samples,K):
    CTRs_that_generated_data = np.tile(np.random.rand(K),(num_samples,1))
    true_rewards = np.random.rand(num_samples,K) < CTRs_that_generated_data
    return true_rewards,CTRs_that_generated_data

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

jhemann

agramfort

"""
Non-Negative Garotte implementation with the scikit-learn
"""

# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#         Jaques Grobler (__main__ script) <jaques.grobler@inria.fr>
#
# License: BSD Style.

import numpy as np