Slater Stich sl8r000

## insight_example.ipynb

      
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                sl8r000
                / insight_example.ipynb
            
            
              Created
              April 20, 2015 01:36
            
          
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## insight_example.html
<!DOCTYPE html>
<html>
<head>

<meta charset="utf-8" />
<title>Insight Example</title>

<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>

## live_run.ipynb

      
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                sl8r000
                / live_run.ipynb
            
            
              Created
              May 13, 2015 16:42
            
              
                Insight Example
              
          
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## gist:02423475fa7df6568954
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {

## blah.ipynb

      
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                sl8r000
                / blah.ipynb
            
            
              Created
              September 20, 2015 02:35
            
          
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## seaborn.ipynb

      
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                sl8r000
                / seaborn.ipynb
            
            
              Created
              September 24, 2015 21:22
            
          
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## markov.py
import sys
import random

if __name__ == "__main__":
    state, num_steps = sys.argv[1], int(sys.argv[2])
    count = dict()
    count['a'], count['b'] = 0, 0

    for i in range(num_steps):
        count[state] += 1

## gist:4138528
$ python markov.py a 100
a 0.72
b 0.28
$ python markov.py a 200
a 0.74
b 0.26
$ python markov.py a 1000
a 0.748
b 0.252
$ python markov.py a 10000

## Simple_Markov_Matrix.txt
$ python
>>> import numpy
>>> P = array([[1.0/3, 2.0/3], [1.0/4, 3.0/4]])
>>> P
array([[ 0.33333333,  0.66666667],
       [ 0.25      ,  0.75      ]])
>>> numpy.linalg.matrix_power(P, 2)
array([[ 0.27777778,  0.72222222],
       [ 0.27083333,  0.72916667]])
>>> numpy.linalg.matrix_power(P, 3)

## Theorem2_1.py
import sys
import random
from numpy import *
import time

class Markov(object):
	def __init__(self, transition_matrix, start_state):
		self.P = transition_matrix
		self.state = start_state
	<!DOCTYPE html>
	<html>
	<head>

	<meta charset="utf-8" />
	<title>Insight Example</title>

	<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
	<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 154,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	import sys
	import random

	if __name__ == "__main__":
	state, num_steps = sys.argv[1], int(sys.argv[2])
	count = dict()
	count['a'], count['b'] = 0, 0

	for i in range(num_steps):
	count[state] += 1
	$ python markov.py a 100
	a 0.72
	b 0.28
	$ python markov.py a 200
	a 0.74
	b 0.26
	$ python markov.py a 1000
	a 0.748
	b 0.252
	$ python markov.py a 10000
	$ python
	>>> import numpy
	>>> P = array([[1.0/3, 2.0/3], [1.0/4, 3.0/4]])
	>>> P
	array([[ 0.33333333, 0.66666667],
	[ 0.25 , 0.75 ]])
	>>> numpy.linalg.matrix_power(P, 2)
	array([[ 0.27777778, 0.72222222],
	[ 0.27083333, 0.72916667]])
	>>> numpy.linalg.matrix_power(P, 3)
	import sys
	import random
	from numpy import *
	import time

	class Markov(object):
	def __init__(self, transition_matrix, start_state):
	self.P = transition_matrix
	self.state = start_state