markheckmann

library(shiny)

shinyServer( function(input, output, session) {
  
  output$results <- renderPrint({
    input$mydata
  })
  
  # observer if value of the data sent from the client changes
  # if yes generate a new random color and send it back to 

KasperBrandt

SELECT ?thumbnail ?abstract ?hdirank ?hdiyear 
WHERE {
  <##DBPedia_link##> <http://dbpedia.org/ontology/thumbnail> ?thumbnail .
  <##DBPedia_link##> <http://dbpedia.org/ontology/abstract> ?abstract .
  FILTER(langMatches(lang(?abstract), "en")) .
  <##DBPedia_link##> <http://dbpedia.org/property/hdiRank> ?hdirank .
  <##DBPedia_link##> <http://dbpedia.org/property/hdiYear> ?hdiyear
}

willurd

Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.

Discussion on reddit.

Python 2.x

$ python -m SimpleHTTPServer 8000

larsmans

>>> from pandas import DataFrame
>>> from sklearn.feature_extraction.text import CountVectorizer
>>> docs = ["You can catch more flies with honey than you can with vinegar.",
...         "You can lead a horse to water, but you can't make him drink."]
>>> vect = CountVectorizer(min_df=0., max_df=1.0)
>>> X = vect.fit_transform(docs)
>>> print(DataFrame(X.A, columns=vect.get_feature_names()).to_string())
   but  can  catch  drink  flies  him  honey  horse  lead  make  more  than  to  vinegar  water  with  you
0    0    2      1      0      1    0      1      0     0     0     1     1   0        1      0     2    2
1    1    2      0      1      0    1      0      1     1     1     0     0   1        0      1     0    2

bwhite

"""Information Retrieval metrics

Useful Resources:
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
http://www.nii.ac.jp/TechReports/05-014E.pdf
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
Learning to Rank for Information Retrieval (Tie-Yan Liu)
"""
import numpy as np

agramfort

"""
Implementation of pairwise ranking using scikit-learn LinearSVC

Reference: "Large Margin Rank Boundaries for Ordinal Regression", R. Herbrich,
    T. Graepel, K. Obermayer.

Authors: Fabian Pedregosa <[email protected]>
         Alexandre Gramfort <[email protected]>
"""