Chris Fonnesbeck fonnesbeck

Update HISTORY.md
Commit the changes:

git add HISTORY.md
git commit -m "Changelog for upcoming release 0.1.1."

Update version number (can also be minor or major)

bumpversion patch

This is a more wordy, narrative accompaniment to my pretty bare presentation about d3 that I gave to the jQuery DC Meetup.

What is d3?

Not a chart library (though you can make charts with it)
Not a map library (though you can make maps with it)

Which is to say, d3 can be used for building things, but the 'atomic parts' are lower-level than bar graphs or projections or so on. This is a powerful fact. It also means that d3 is a good basis for simple interfaces, like Vega.js, that make its power accessible in other ways.

Not a compatibility layer (it doesn't work with bad browsers)

	import pymssql
	import pandas as pd

	## instance a python db connection object- same form as psycopg2/python-mysql drivers also
	conn = pymssql.connect(server="172.0.0.1", user="howens",password="some_fake_password", port=63642) # You can lookup the port number inside SQL server.

	## Hey Look, college data
	stmt = "SELECT * FROM AlumniMirror..someTable"
	# Excute Query here
	df = pd.read_sql(stmt,conn)

	'''Implementation and Demostration of Dynamic Time Warping
	Requires : python 2.7.x, Numpy 1.7.1, Matplotlib, 1.2.1'''

	from math import *
	import numpy as np
	import sys

	def DTW(A, B, window = sys.maxint, d = lambda x,y: abs(x-y)):
	# create the cost matrix
	A, B = np.array(A), np.array(B)

	import numba
	import numpy


	#The filter2d with the same signature as Theano
	#but not a class method.
	def filter2d_theano(node, inputs, outputs):
	image, filt = inputs
	M, N = image.shape
	Mf, Nf = filt.shape

	from __future__ import division
	from numpy import *

	class AdaBoost:

	def __init__(self, training_set):
	self.training_set = training_set
	self.N = len(self.training_set)
	self.weights = ones(self.N)/self.N
	self.RULES = []

	import collections

	def extract_top_ten(str):
	return collections.Counter(str.split()).most_common(10)

	import numpy as np
	I2 = 2*np.eye(8)
	E = -np.diag(np.ones((7)), k=-1)

	aDictionary = {'a':3, 'b':-4, 'c':3.14}
	sorted(aDictionary, key=lambda x: aDictionary[x])