Flavio xboard

## ssh-tunneling.txt
SSH TUNNELING


ssh -f -L 3000:talk.google.com:5222 home -N

I was able to send my Google Talk traffic encrypted through the firewall back to my server at home and then out to Google. All I had to do was reconfigure my Jabber client to use localhost as the server and the port 3000 that I had configured.

## bayes_update.R
## Corey Chivers, 2012 ##
sim_bayes<-function(p=0.5,N=100,y_lim=20,a_a=2,a_b=10,b_a=8,b_b=3)
{
   ## Simulate outcomes in advance
   outcomes<-sample(1:0,N,prob=c(p,1-p),replace=TRUE)
   success<-cumsum(outcomes)

   for(frame in 1:N)
   {
      png(paste("plots/",1000+frame,".png",sep=""))

## nnlr

import scipy as sp
from scipy import optimize as opt
def nnlr(X, y, C):
    """
    Non-negative Logistic Regression with L2 regularizer
    """
    def lr_cost(X, y, theta, C):
        m = len(y)
        return (1./m) * (sp.dot(-y, sp.log(sigmoid(sp.dot(X, theta)))) \

## MEHMM.py
import numpy as np

class MEHMM(object):
    def __init__(self, initialProb, transProb):
        self.N = initialProb.shape[0]
        self.initialProb = initialProb
        self.transProb = transProb
        assert self.initialProb.shape == (self.N, )
        assert self.transProb.shape == (self.N, self.N)


## HMM.py
import numpy as np

class HMM(object):
    def __init__(self, initialProb, transProb, obsProb):
        self.N = initialProb.shape[0]
        self.initialProb = initialProb
        self.transProb = transProb
        self.obsProb = obsProb
        assert self.initialProb.shape == (self.N, 1)
        assert self.transProb.shape == (self.N, self.N)

## McCannUSAMap
<html>
<head>
<!-- Load the d3 library. -->
<script src="http://d3js.org/d3.v3.min.js" charset="utf-8"></script>
<link href='http://fonts.googleapis.com/css?family=Open+Sans' rel='stylesheet' type='text/css'>
<style>
body { font-family: "Open Sans"; }
text.stateID { dominant-baseline: middle; text-anchor: middle; }
</style>
</head>

## kernel_kmeans.py
"""Kernel K-means"""

# Author: Mathieu Blondel <mathieu@mblondel.org>
# 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

## nmf_cd.py
"""
NMF by coordinate descent, designed for sparse data (without missing values)
"""

# Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause

import numpy as np
import scipy.sparse as sp
import numba

## scanner.py
"""
Scanner: match text to generate tokens.
Adam Blinkinsop <blinks@acm.org>

First, construct a scanner with the tokens you'd like to match described as
keyword arguments, using Python-syntax regular expressions.
WARNING: Group syntax in these expressions has an undefined effect.

>>> simple = Scan(ID=r'\w+')


## onename.gist
Verifying that +xboard is my blockchain ID. https://onename.com/xboard
	SSH TUNNELING


	ssh -f -L 3000:talk.google.com:5222 home -N

	I was able to send my Google Talk traffic encrypted through the firewall back to my server at home and then out to Google. All I had to do was reconfigure my Jabber client to use localhost as the server and the port 3000 that I had configured.
	## Corey Chivers, 2012 ##
	sim_bayes<-function(p=0.5,N=100,y_lim=20,a_a=2,a_b=10,b_a=8,b_b=3)
	{
	## Simulate outcomes in advance
	outcomes<-sample(1:0,N,prob=c(p,1-p),replace=TRUE)
	success<-cumsum(outcomes)

	for(frame in 1:N)
	{
	png(paste("plots/",1000+frame,".png",sep=""))

	import scipy as sp
	from scipy import optimize as opt
	def nnlr(X, y, C):
	"""
	Non-negative Logistic Regression with L2 regularizer
	"""
	def lr_cost(X, y, theta, C):
	m = len(y)
	return (1./m) * (sp.dot(-y, sp.log(sigmoid(sp.dot(X, theta)))) \
	import numpy as np

	class MEHMM(object):
	def __init__(self, initialProb, transProb):
	self.N = initialProb.shape[0]
	self.initialProb = initialProb
	self.transProb = transProb
	assert self.initialProb.shape == (self.N, )
	assert self.transProb.shape == (self.N, self.N)
	import numpy as np

	class HMM(object):
	def __init__(self, initialProb, transProb, obsProb):
	self.N = initialProb.shape[0]
	self.initialProb = initialProb
	self.transProb = transProb
	self.obsProb = obsProb
	assert self.initialProb.shape == (self.N, 1)
	assert self.transProb.shape == (self.N, self.N)
	<html>
	<head>
	<!-- Load the d3 library. -->
	<script src="http://d3js.org/d3.v3.min.js" charset="utf-8"></script>
	<link href='http://fonts.googleapis.com/css?family=Open+Sans' rel='stylesheet' type='text/css'>
	<style>
	body { font-family: "Open Sans"; }
	text.stateID { dominant-baseline: middle; text-anchor: middle; }
	</style>
	</head>
	"""Kernel K-means"""

	# Author: Mathieu Blondel <mathieu@mblondel.org>
	# 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
	"""
	NMF by coordinate descent, designed for sparse data (without missing values)
	"""

	# Author: Mathieu Blondel <mathieu@mblondel.org>
	# License: BSD 3 clause

	import numpy as np
	import scipy.sparse as sp
	import numba
	"""
	Scanner: match text to generate tokens.
	Adam Blinkinsop <blinks@acm.org>

	First, construct a scanner with the tokens you'd like to match described as
	keyword arguments, using Python-syntax regular expressions.
	WARNING: Group syntax in these expressions has an undefined effect.

	>>> simple = Scan(ID=r'\w+')