César de Pablo zdepablo

## gist:8172796

      
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                debasishg
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                A collection of links for streaming algorithms and data structures
              
          
    General Background and Overview


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.
Models and Issues in Data Stream Systems
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
Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
[Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&amp;rep=rep1&amp;t


## split_strat_scale.r
library(caret)

## select training indices preserving class distribution
in.train <- createDataPartition(yclass, p=0.8, list=FALSE)
summary(factor(yclass))
ytra <- yclass[in.train]; summary(factor(ytra))
ytst <- yclass[-in.train]; summary(factor(ytst))

## standardize features: training parameters of scaling for test-part
Xtra <- scale(X[in.train,])

## git-branches-by-commit-date.sh
# Credit http://stackoverflow.com/a/2514279
for branch in `git branch -r | grep -v HEAD`;do echo -e `git show --format="%ci %cr" $branch | head -n 1` \\t$branch; done | sort -r
	library(caret)

	## select training indices preserving class distribution
	in.train <- createDataPartition(yclass, p=0.8, list=FALSE)
	summary(factor(yclass))
	ytra <- yclass[in.train]; summary(factor(ytra))
	ytst <- yclass[-in.train]; summary(factor(ytst))

	## standardize features: training parameters of scaling for test-part
	Xtra <- scale(X[in.train,])
	# Credit http://stackoverflow.com/a/2514279
	for branch in `git branch -r \| grep -v HEAD`;do echo -e `git show --format="%ci %cr" $branch \| head -n 1` \\t$branch; done \| sort -r