- 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.
- Models and Issues in Data Stream Systems
- 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
- Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
- [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t
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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) |
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# 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 |
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。
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# (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. |
- 更新
2013-11-01
- バージョン
0.0.1
- 作者
@voluntas
- URL
概要
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
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""" | |
Some python code for | |
Markov Chain Monte Carlo and Gibs sampling | |
by Bruce Walsh | |
""" | |
import numpy as np | |
import numpy.linalg as npla |
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"""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 |