fabianp

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

Reference: 

    "Large Margin Rank Boundaries for Ordinal Regression", R. Herbrich,
    T. Graepel, K. Obermayer 1999

    "Learning to rank from medical imaging data." Pedregosa, Fabian, et al., 
    Machine Learning in Medical Imaging 2012.

ibeex

A warning occurred (42 apples)
An error occurred

observerss

// EDIT: 2013/10/20
// google has updated its kwt UI, this script doesn't work any more!
// may be I will update this script when I have time to investigate their new Interface.


// requires
var utils = require('utils');
var casper = require('casper').create()
var casper = require('casper').create({
    verbose: true,

caniszczyk

curl -s https://api.github.com/orgs/twitter/repos?per_page=200 | ruby -rubygems -e 'require "json"; JSON.load(STDIN.read).each { |repo| %x[git clone #{repo["ssh_url"]} ]}'

coreylynch

import itertools

import numpy as np

from sklearn.linear_model import SGDClassifier, SGDRanking
from sklearn import metrics
from minirank.compat import RankSVM as MinirankSVM
from scipy import stats

yoavram

import requests

#http://docs.python-requests.org/en/latest/user/quickstart/#post-a-multipart-encoded-file

url = "http://localhost:5000/"
fin = open('simple_table.pdf', 'rb')
files = {'file': fin}
try:
  r = requests.post(url, files=files)
	print r.text

rxaviers

People

:bowtie:	😄 :smile:	😆 :laughing:
😊 :blush:	😃 :smiley:	☺️ :relaxed:
😏 :smirk:	😍 :heart_eyes:	😘 :kissing_heart:
😚 :kissing_closed_eyes:	😳 :flushed:	😌 :relieved:
😆 :satisfied:	😁 :grin:	😉 :wink:
😜 :stuck_out_tongue_winking_eye:	😝 :stuck_out_tongue_closed_eyes:	😀 :grinning:
😗 :kissing:	😙 :kissing_smiling_eyes:	😛 :stuck_out_tongue:

cstrelioff

process corpus for lda

In a blog post I wrote about the python package lda, see here, I used the pre-processed data (included with the lda package) for the example. I have since received many questions regarding the document-term matrix, the titles, and the vocabulary-- where do they come from? This gist will use the textmining package to (hopefully) help answer these types of questions.

paulirish

Option 1: Command-line download extension as zip and extract

extension_id=jifpbeccnghkjeaalbbjmodiffmgedin   # change this ID
curl -L -o "$extension_id.zip" "https://clients2.google.com/service/update2/crx?response=redirect&os=mac&arch=x86-64&nacl_arch=x86-64&prod=chromecrx&prodchannel=stable&prodversion=44.0.2403.130&x=id%3D$extension_id%26uc" 
unzip -d "$extension_id-source" "$extension_id.zip"

Thx to crxviewer for the magic download URL.

anj1

import types
import tensorflow as tf 
import numpy as np

# Expressions are represented as lists of lists,
# in lisp style -- the symbol name is the head (first element)
# of the list, and the arguments follow.

# add an expression to an expression list, recursively if necessary.
def add_expr_to_list(exprlist, expr):