First I download the sequence.index
file from the 1000genomes EBI FRP site:
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[ | |
{ | |
"self": "http://toolkit.vph-noe.eu/home/tools/data-conversion-tools.html", | |
"name": "Data Conversion Tools" | |
}, | |
{ | |
"self": "http://toolkit.vph-noe.eu/home/tools/data-fusion.html", | |
"name": "Data Fusion" | |
}, | |
{ |
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training = read.csv("train.csv") | |
testing = read.csv("test.csv") | |
training$ACTION = as.factor(training$ACTION) | |
libs = c("caret", "randomForest", "rpart", "kernlab", "glmnet", "gbm", | |
"doMC") | |
for (l in libs) { |
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for j in `seq 2014 2050`; do | |
i=8 | |
ncal $i $j | tail -3 | (echo -n "$j," ; awk 'BEGIN{ORS=","} {print NF-1}';echo) | |
done | egrep "5,5,5,$" |cut -f1 -d, |
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var fs = require('fs') | |
var DEBUG = true; | |
var log = console.log; | |
if (!DEBUG) | |
log = function() {}; | |
function parse_scp_time(fval) { | |
var hour = fval.readInt8(0); |
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# EXAMPLE USAGE | |
# example of colsidecolors rowsidecolors (single column, single row) | |
mat <- matrix(1:100, byrow=T, nrow=10) | |
column_annotation <- sample(c("red", "blue", "green"), 10, replace=T) | |
column_annotation <- as.matrix(column_annotation) | |
colnames(column_annotation) <- c("Variable X") | |
row_annotation <- sample(c("red", "blue", "green"), 10, replace=T) | |
row_annotation <- as.matrix(t(row_annotation)) |
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git config --global alias.hub '!open $(git config --get remote.origin.url | sed -e "s|git@github\.com:|https://github.com/|" -e "s|git@bitbucket\.org:|https://bitbucket.org/|" -e "s|\.git$||")' |
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<out.json jq -r '.Root.ResultSet.Entity[] | [.color, .width, .average, .reference, .Time]|@csv' > out.csv |
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exiv2 -Pv -g Exif.Image.DateTime *.JPG | awk '{gsub(/:/,"/", $2); print $1,$2}' |
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Q: what are "single tree-based" (as opposed to forest-based) supervised learning methods? | |
A: some of my favorites: | |
- ADT | |
+ wiki: http://en.wikipedia.org/wiki/Alternating_decision_tree | |
+ ref: http://perun.pmf.uns.ac.rs/radovanovic/dmsem/cd/install/Weka/doc/classifiers-papers/trees/ADTree/atrees.pdf | |
- rpart in R | |
+ http://cran.r-project.org/web/packages/rpart/vignettes/longintro.pdf |
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