KartikTalwar

The fastest remote directory rsync over ssh archival I can muster (40MB/s over 1gb NICs)

This creates an archive that does the following:

rsync (Everyone seems to like -z, but it is much slower for me)

• a: archive mode - rescursive, preserves owner, preserves permissions, preserves modification times, preserves group, copies symlinks as symlinks, preserves device files.
• H: preserves hard-links
• A: preserves ACLs

rbresearch

# Ranking Algorithm for ETF Replay

# Rank based on 3 factors
# 1. ReturnA    (ret1)
# 2. ReturnB    (ret2)
# 3. Volatility (vol)

# Returns are ranked from high to low
# Volatility is ranked from low to high

rbresearch

# Backtest of ETF Replay Ranking Algorithm

# clear the workspace
rm(list=ls())

# step-by-step example of ETF Replay Ranking Algorithm

##### etfReplayRank Function #####
# we can use the same step-by-step approach outlined above to write a function
# to rank instruments based on ETF Replay

theanalyst

;;; pushbullet.el --- An emacs client for the pushbullet android app
;;; Using the pushbullet api at https://pushbullet.com/api 
;;; Uses grapnel for http requests https://github.com/leathekd/grapnel
;;; 
;;; Commentary: 
;;; 
;;; Code:

(require 'grapnel)
(require 'json)

mblondel

"""Kernel K-means"""

# Author: Mathieu Blondel <[email protected]>
# License: BSD 3 clause

import numpy as np

from sklearn.base import BaseEstimator, ClusterMixin
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils import check_random_state

vgoklani

"""Kernel K-means"""

# Author: Mathieu Blondel <[email protected]>
# License: BSD 3 clause

import numpy as np

from sklearn.base import BaseEstimator, ClusterMixin
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils import check_random_state

alexchinco

################################################################################
################################################################################
## Prep workspace
################################################################################
################################################################################
options(width=200, digits=6, digits.secs=6)

rm(list=ls())

library(foreign)

rmcgibbo

Compiling an optimized ATLAS on Ubuntu 12.04

1. Download the latest ATLAS source from http://sourceforge.net/projects/math-atlas/files/. I'm using version 3.10.1
2. Download the latest Netlib LAPACK from http://www.netlib.org/lapack/. I'm using version 3.4.2
3. Turn off frequency scalings on your chip so that you can get reliable timings. This is essential to get a good ATLAS build

  sudo apt-get install cpufreq-info cpuspeed cpufrequtils sysfsutils
  # set each core to the "performance" governor, so that the clock frequency doesn't go down when idle
  # I have 8 cores, which is why I need to do this 8 times
  sudo cpufreq-selector -c 0 -g performance

erogol

function[binary_codes,G_vecs] = WTA_kernel_binary(X,G_vecs,G, K)
% G is number of permutation vectors
% K is the insterest span

if size(G_vecs,1) == 0
        G_vec = zeros(G,size(X,2));
        for i = 1:G 
                G_vecs(i,:) = randperm(size(X,2),size(X,2));
        end
end

mblondel

"""Variable-length curve averaging"""

# Author: Mathieu Blondel <[email protected]>
# License: BSD 3 clause

import numpy as np
from scipy.interpolate import interp1d


def curves_mean_std(X, Y, kind="linear"):