Francis Tseng frnsys

I hereby claim:

To claim this, I am signing this object:

	# Adapted from
	# http://pytables.github.io/usersguide/libref/homogenous_storage.html#the-carray-class

	import os
	import numpy
	import tables
	from itertools import groupby
	from operator import itemgetter
	from time import time

	import operator
	from itertools import combinations
	from functools import reduce

	import numpy as np

	def hac(vecs, sim_func, threshold):
	"""
	Hierarchical Agglomerative Clustering.
	"""

	import random
	import networkx as nx

	class Person():
	def __init__(self, name, stance=None):
	self.name = name

	if stance is None:
	self.stance = random.randrange(0, 2)
	else:

	import sys
	import logging
	import numpy
	import gensim


	logging.basicConfig(level=logging.INFO)

	train_sentences = gensim.models.doc2vec.LabeledLineSentence(sys.argv[1])
	model = gensim.models.Doc2Vec(train_sentences, size=400, window=8, min_count=2,

	# Copyright (c) 2012, Ryan Gomba
	# All rights reserved.
	#
	# Redistribution and use in source and binary forms, with or without
	# modification, are permitted provided that the following conditions are met:
	#
	# 1. Redistributions of source code must retain the above copyright notice, this
	# list of conditions and the following disclaimer.
	# 2. Redistributions in binary form must reproduce the above copyright notice,
	# this list of conditions and the following disclaimer in the documentation

	import numpy as np

	# the 2d array of our samples,
	# each component is a category label
	a = np.array([[1,2,3],[4,5,6]])

	# the 3d array that will be the one-hot representation
	# a.max() + 1 is the number of labels we have
	b = np.zeros((a.shape[0], a.shape[1], a.max() + 1))

	<!doctype html>
	<html lang="en">
	<head>
	<meta charset="utf-8"/>
	<meta name="viewport" content="width=device-width,initial-scale=1">
	<title>hello</title>
	</head>
	<body>
	<div id="root">Loading...</div>
	<script src="bundle.js"></script>

	import numpy as np
	from pyqtree import Index
	from shapely import geometry
	import matplotlib.pyplot as plt
	from matplotlib.patches import Polygon
	plt.style.use('ggplot')

	# distance we expect the bus stop to be from the road
	# will pad all bounding boxes accordingly
	radius = 0.1