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 <fabian@fseoane.net>
         Alexandre Gramfort <alexandre.gramfort@inria.fr>
"""

hemanth

require 'oauth'
require 'launchy'
require 'nokogiri'


class User
  def authenticate(consumer_key, consumer_secret, site)
    consumer = OAuth::Consumer.new(consumer_key, consumer_secret, site)
    request_token = consumer.get_request_token
    puts "Authorize Follower and then paste in the PIN:"

domenic

This article has been given a more permanent home on my blog. Also, since it was first written, the development of the Promises/A+ specification has made the original emphasis on Promises/A seem somewhat outdated.

You're Missing the Point of Promises

Promises are a software abstraction that makes working with asynchronous operations much more pleasant. In the most basic definition, your code will move from continuation-passing style:

getTweetsFor("domenic", function (err, results) {
 // the rest of your code goes here.

skryv

      echo "-----> Installing via pip, but setting environment vars first."
      export BLAS=$(pwd)/vendor/lib/atlas-base/atlas/libblas.a
      export LAPACK=$(pwd)/vendor/lib/atlas-base/atlas/liblapack.a
      export ATLAS=$(pwd)/vendor/lib/atlas-base/libatlas.a
      export LIBRARY_PATH=$(pwd)/vendor/lib:$(pwd)/lib/atlas-base:$(pwd)/lib/atlas-base/atlas
      export LD_LIBRARY_PATH=$(pwd)/vendor/lib:$(pwd)/vendor/lib/atlas-base:$(pwd)/vendor/lib/atlas-base/atlas

      pip install --use-mirrors scikit-learn

skryv

heroku config:add BUILDPACK_URL=https://github.com/ToonTimbermont/heroku-buildpack-python

elijahmanor

describe("getTweets - Server", function () {
    var server, fakeData = [ /* ... */ ];

    before(function () {
        // Doesn’t work :( It’s JSONP!
        server = sinon.fakeServer.create();
        server.respondWith(
            "GET", 
            "https://api.twitter.com/.../elijahmanor.json?count=5",
            [200, { "Content-Type": "application/json" }, JSON.stringify(fakeData)]

sbos

module HMM

using Distributions

import Distributions.rand
import Distributions.fit

immutable HiddenMarkovModel{TP, K}
    theta::Vector{TP}
    A::Matrix{Float64}

jperla

# Incomplete row major matrix implementation.
type RowMajorMatrix{T} <: AbstractMatrix{T}
    col_major::Matrix{T}
end

row_major{T}(c::Matrix{T}) = RowMajorMatrix{T}(c)
Base.size{T}(m::RowMajorMatrix{T}, n::Int) = if n == 1 size(m.col_major, 2) elseif n == 2 size(m.col_major, 1) else size(m.col_major, n) end

Base.getindex{T}(m::RowMajorMatrix{T}, i, j) = m.col_major[j, i]
function setindex!{T}(m::RowMajorMatrix{T}, value, i, j)

syllog1sm

"""A simple implementation of a greedy transition-based parser. Released under BSD license."""
from os import path
import os
import sys
from collections import defaultdict
import random
import time
import pickle

SHIFT = 0; RIGHT = 1; LEFT = 2;

larsmans

import numpy as np
#from scipy.special import chdtrc
from scipy.sparse import spdiags

from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import LabelBinarizer


def _chisquare(f_obs, f_exp, reduce):
    """Replacement for scipy.stats.chisquare with custom reduction.