create different ssh key according the article Mac Set-Up Git
$ ssh-keygen -t rsa -C "[email protected]"
/** | |
* This stylesheet will work pretty well with a regular Org Mode HTML export. | |
* However, you do have to turn off all of the defaults: | |
* | |
* (setq org-export-html-style-include-scripts nil | |
* org-export-html-style-include-default nil) | |
* | |
* and insert a call to the stylesheet: | |
* | |
* (setq org-export-html-style |
# Mathieu Blondel, October 2010 | |
# License: BSD 3 clause | |
import numpy as np | |
from numpy import linalg | |
def linear_kernel(x1, x2): | |
return np.dot(x1, x2) | |
def polynomial_kernel(x, y, p=3): |
# Requirements | |
#sudo apt-get install libcurl4-gnutls-dev # for RCurl on linux | |
#install.packages('RCurl') | |
#install.packages('RJSONIO') | |
library('RCurl') | |
library('RJSONIO') | |
query <- function(querystring) { | |
h = basicTextGatherer() |
import time | |
import socket | |
def collect_metric(name, value, timestamp): | |
sock = socket.socket() | |
sock.connect( ("localhost", 2003) ) | |
sock.send("%s %d %d\n" % (name, value, timestamp)) | |
sock.close() | |
def now(): | |
return int(time.time()) |
""" | |
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. |
""" | |
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 <[email protected]> | |
Alexandre Gramfort <[email protected]> | |
""" |
create different ssh key according the article Mac Set-Up Git
$ ssh-keygen -t rsa -C "[email protected]"
""" Theano CRBM implementation. | |
For details, see: | |
Taylor GW, Hinton GE, Roweis ST. Modeling Human Motion Using Binary Latent Variables. | |
In: Advances in Neural Information Processing Systems 19. MIT Press; 2007. pp. 1345–1352. | |
Sample data: | |
https://uoguelphca-my.sharepoint.com/:u:/g/personal/gwtaylor_uoguelph_ca/EfJARkZuiX1JmwMKQxQqKJMBaMBUNOcF83FW_n9gk7OIbg?e=fnCjet | |
@author Graham Taylor""" | |
import numpy |
#!/usr/bin/python | |
# | |
# (originally entered at https://gist.github.com/1035399) | |
# | |
# License: GPLv3 | |
# | |
# To download the AFINN word list do: | |
# wget http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/6010/zip/imm6010.zip | |
# unzip imm6010.zip | |
# |
import com.twitter.scalding._ | |
import cern.colt.matrix.{DoubleFactory2D, DoubleFactory1D } | |
import cern.colt.matrix.linalg.Algebra | |
import java.util.StringTokenizer | |
class Portfolios(args : Args) extends Job(args) { | |
val cash = 1000.0 // money at hand | |
val error = 1 // its ok if we cannot invest the last dollar |