mamrehn

## logistic_regression_with_checkpointing.py
'''
A logistic regression example using the meta-graph checkpointing
features of Tensorflow.

Author: João Felipe Santos, based on code by Aymeric Damien
(https://github.com/aymericdamien/TensorFlow-Examples/)
'''

from __future__ import print_function

## hoverable.py
"""Hoverable Behaviour (changing when the mouse is on the widget by O. Poyen.
License: LGPL
"""
__author__ = 'Olivier POYEN'


from kivy.properties import BooleanProperty, ObjectProperty
from kivy.core.window import Window

class HoverBehavior(object):

## useful_pandas_snippets.py
#List unique values in a DataFrame column
pd.unique(df.column_name.ravel())

#Convert Series datatype to numeric, getting rid of any non-numeric values
df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)

#Grab DataFrame rows where column has certain values
valuelist = ['value1', 'value2', 'value3']
df = df[df.column.isin(value_list)]

## compare_adagrad_adadelta.py
"""
Comparing adagrad, adadelta and constant learning in gradient descent(the seddle point function y^2 - x^2)

Reference:
1. comparison on several learning rate update scheme: http://ml.memect.com/archive/2014-12-12/short.html#3786866375172817
2. Saddle point, http://en.wikipedia.org/wiki/Saddle_point

"""
import numpy as np
import theano

## loader.html
<!doctype html>
<html>
<head>
<meta charset="utf-8" />
<title>STL File Viewer</title>
<script src="https://cdnjs.cloudflare.com/ajax/libs/three.js/r68/three.min.js"
></script>
<script src="https://rawgit.com/mrdoob/three.js/master/examples/js/controls/TrackballControls.js"
></script>
<script src="loader.js"></script>

## remove_unused_kernels.sh
#!/bin/sh
dpkg -l 'linux-*' | sed '/^ii/!d;/'"$(uname -r | sed "s/\(.*\)-\([^0-9]\+\)/\1/")"'/d;s/^[^ ]* [^ ]* \([^ ]*\).*/\1/;/[0-9]/!d' | xargs sudo apt-get -y purge
	'''
	A logistic regression example using the meta-graph checkpointing
	features of Tensorflow.

	Author: João Felipe Santos, based on code by Aymeric Damien
	(https://github.com/aymericdamien/TensorFlow-Examples/)
	'''

	from __future__ import print_function
	"""Hoverable Behaviour (changing when the mouse is on the widget by O. Poyen.
	License: LGPL
	"""
	__author__ = 'Olivier POYEN'


	from kivy.properties import BooleanProperty, ObjectProperty
	from kivy.core.window import Window

	class HoverBehavior(object):
	#List unique values in a DataFrame column
	pd.unique(df.column_name.ravel())

	#Convert Series datatype to numeric, getting rid of any non-numeric values
	df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)

	#Grab DataFrame rows where column has certain values
	valuelist = ['value1', 'value2', 'value3']
	df = df[df.column.isin(value_list)]
	"""
	Comparing adagrad, adadelta and constant learning in gradient descent(the seddle point function y^2 - x^2)

	Reference:
	1. comparison on several learning rate update scheme: http://ml.memect.com/archive/2014-12-12/short.html#3786866375172817
	2. Saddle point, http://en.wikipedia.org/wiki/Saddle_point

	"""
	import numpy as np
	import theano
	<!doctype html>
	<html>
	<head>
	<meta charset="utf-8" />
	<title>STL File Viewer</title>
	<script src="https://cdnjs.cloudflare.com/ajax/libs/three.js/r68/three.min.js"
	></script>
	<script src="https://rawgit.com/mrdoob/three.js/master/examples/js/controls/TrackballControls.js"
	></script>
	<script src="loader.js"></script>
	#!/bin/sh
	dpkg -l 'linux-' \| sed '/^ii/!d;/'"$(uname -r \| sed "s/\(.\)-\([^0-9]\+\)/\1/")"'/d;s/^[^ ]* [^ ]* \([^ ]\)./\1/;/[0-9]/!d' \| xargs sudo apt-get -y purge