Heiko Strathmann karlnapf

## new_tester.py
import glob
from optparse import OptionParser
import os
import sys

from modshogun import SerializableAsciiFile, WrappedObjectArray


if __name__ == "__main__":
    op = OptionParser()

## BSD shogun
/*
 * -*- coding: utf-8 -*-
 * vim: set fileencoding=utf-8
 *
 * Copyright (c) 2016, Shogun-Toolbox e.V. <shogun-team@shogun-toolbox.org>
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *

## TIMIT_kernel_exp_family.ipynb

      
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                karlnapf
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## gamma_median_heuristic.py
def gamma_median_heuristic(Z, num_subsample=1000):
    """
    Computes the median pairwise distance in a random sub-sample of Z.
    Returns a \gamma for k(x,y)=\exp(-\gamma ||x-y||^2), according to the median heuristc,
    i.e. it corresponds to \sigma in k(x,y)=\exp(-0.5*||x-y||^2 / \sigma^2) where
    \sigma is the median distance. \gamma = 0.5/(\sigma^2)
    """
    inds = np.random.permutation(len(Z))[:np.max([num_subsample, len(Z)])]
    dists = squareform(pdist(Z[inds], 'sqeuclidean'))
    median_dist = np.median(dists[dists > 0])

## MNST_kernel_exp_family.ipynb

      
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                karlnapf
                / MNST_kernel_exp_family.ipynb
            
            
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              March 4, 2016 10:30
            
              
                MNIST and kernel exponential family
              
          
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## index.rst

      
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                karlnapf
                / index.rst
            
            
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              August 29, 2015 14:17
            
          
    Welcome to Shogun-manual's documentation!

Contents:

  
## gist:b24fa3d69aafaa282806
<!DOCTYPE html>
<html>
<head>
<title>Shogun 4.0</title>
</head>

<body>

<p>
The Shogun team just released <a href="http://www.shogun-toolbox.org/new/41" target="_blank">version 4.0</a> of their community driven Machine Learning toolbox.

## doxygen_page
# A single Shogun program
This documents classififier_libsvm_probabilities.cpp.

Cool thing is we can use standard markdown to write the documentation while the source code can be executed. Another advantage is that not all source code is pasted at once. Rather, we can write little paragraphs in between, even including math.

## Include and initialisation
First, let's include the needed header files and initialise Shogun.
However, if this is not documented in this doxypage (one might not want to do that as it is standard), the code still is in the source file and thus can be executed and tested.

### Header includes

## gist:8ed7de163eed39e733da
{
 "metadata": {
  "name": "",
  "signature": "sha256:5480489225e5331c5fb14f21f5cb3e8312909e64210905685b98b1b36a07e6f3"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [

## gist:dbd4f2aa2a9f9ca38e4c
{
 "metadata": {
  "name": "Gastby ML assignment 6"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
	import glob
	from optparse import OptionParser
	import os
	import sys

	from modshogun import SerializableAsciiFile, WrappedObjectArray


	if __name__ == "__main__":
	op = OptionParser()
	/*
	* -- coding: utf-8 --
	* vim: set fileencoding=utf-8
	*
	* Copyright (c) 2016, Shogun-Toolbox e.V. <shogun-team@shogun-toolbox.org>
	* All rights reserved.
	*
	* Redistribution and use in source and binary forms, with or without
	* modification, are permitted provided that the following conditions are met:
	*
	def gamma_median_heuristic(Z, num_subsample=1000):
	"""
	Computes the median pairwise distance in a random sub-sample of Z.
	Returns a \gamma for k(x,y)=\exp(-\gamma \|\|x-y\|\|^2), according to the median heuristc,
	i.e. it corresponds to \sigma in k(x,y)=\exp(-0.5*\|\|x-y\|\|^2 / \sigma^2) where
	\sigma is the median distance. \gamma = 0.5/(\sigma^2)
	"""
	inds = np.random.permutation(len(Z))[:np.max([num_subsample, len(Z)])]
	dists = squareform(pdist(Z[inds], 'sqeuclidean'))
	median_dist = np.median(dists[dists > 0])
	<!DOCTYPE html>
	<html>
	<head>
	<title>Shogun 4.0</title>
	</head>

	<body>

	<p>
	The Shogun team just released <a href="http://www.shogun-toolbox.org/new/41" target="_blank">version 4.0</a> of their community driven Machine Learning toolbox.
	# A single Shogun program
	This documents classififier_libsvm_probabilities.cpp.

	Cool thing is we can use standard markdown to write the documentation while the source code can be executed. Another advantage is that not all source code is pasted at once. Rather, we can write little paragraphs in between, even including math.

	## Include and initialisation
	First, let's include the needed header files and initialise Shogun.
	However, if this is not documented in this doxypage (one might not want to do that as it is standard), the code still is in the source file and thus can be executed and tested.

	### Header includes
	{
	"metadata": {
	"name": "",
	"signature": "sha256:5480489225e5331c5fb14f21f5cb3e8312909e64210905685b98b1b36a07e6f3"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	"metadata": {
	"name": "Gastby ML assignment 6"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{