martindemello

class PurchaseApprover
  # Implements the chain of responsibility pattern. Does not know anything
  # about the approval process, merely whether the current handler can approve
  # the request, or must pass it to a successor.
  attr_reader :successor

  def initialize successor
    @successor = successor
  end

sonots

Fluentd ソースコード完全解説

英題：Fluentd Hacking Guide

目次

30分しかないため斜線部分は今回省く

• Fluentd の起動シーケンスとプラグインの読み込み
• Fluentd の設定ファイルのパース
• Input Plugin から Output Plugin にデータが渡る流れ

debasishg

General Background and Overview

1. 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.
2. Models and Issues in Data Stream Systems
3. 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
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

eamartin

function householder!(x)
    x[1] = x[1] + sign(x[1]) .* norm(x)
    x ./= norm(x);
end

function tridiag_qr(T)
    Q = eye(size(T)...)
    R = copy(T)

    for i in 1:(size(R, 1) - 1)

huyng

# Place the following in your home directory at ~/.numpy-site.cfg 
# in order to install numpy with openblas support


[openblas]
libraries = openblas
library_dirs = /usr/local/opt/openblas/lib
include_dirs = /usr/local/opt/openblas/include

uupaa

Chrome 上で reveal.js で作成したスライドを pdf 化する手順メモ

• URL に ?print-pdf を追加する
• CMD + P で印刷ダイアログを表示し、出力先を pdf に設定
• 出力

出力された pdf を確認し、リンクが表示されていないなど表示がおかしい場合は以下の事を試す(ここからが本題)

reveal.js/out.html の document.write している行をコメントアウトし、css/print/pdf.css を直接追加。

mblondel

# (C) Mathieu Blondel, November 2013
# License: BSD 3 clause

import numpy as np
from scipy.linalg import svd


def frequent_directions(A, ell, verbose=False):
    """
    Return the sketch of matrix A.

voluntas

Trello のススメ

更新:	2013-11-01
バージョン:	0.0.1
作者:	@voluntas
URL:	http://voluntas.github.io/

概要

jakevdp

Numba Ball Tree

This is a quick attempt at writing a ball tree for nearest neighbor searches using numba. I've included a pure python version, and a version with numba jit decorators. Because class support in numba is not yet complete, all the code is factored out to stand-alone functions in the numba version. The resulting code produced by numba is about ~10 times slower than the cython ball tree in scikit-learn. My guess is that part of this stems from lack of inlining in numba, while the rest is due to some sort of overhead

osdf

"""
Some python code for
Markov Chain Monte Carlo and Gibs sampling
by Bruce Walsh
"""


import numpy as np
import numpy.linalg as npla