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Uniregistry, Corp. 9 ['COUNTRY', 'CHRISTMAS', 'PICS', 'PHOTO', 'GIFT', 'LINK', 'GUITARS', 'SEXY', 'TATTOO'] | |
Afilias Limited 8 ['BLACK', 'MEET', '\xe7\xa7\xbb\xe5\x8a\xa8 (xn--6frz82g) \xe2\x80\x93 Chinese for "mobile"', 'BLUE', 'KIM', 'PINK', 'RED', 'SHIKSHA'] | |
United TLD Holdco Ltd. 7 ['DEMOCRAT', 'SOCIAL', 'MODA', 'DANCE', 'IMMOBILIEN', 'KAUFEN', 'NINJA'] | |
United TLD Holdco, Ltd. 7 ['ROCKS', 'CONSULTING', 'HAUS', 'PUB', 'ACTOR', 'REVIEWS', 'FUTBOL'] | |
Top Level Domain Holdings Limited 6 ['VODKA', 'COOKING', 'RODEO', 'HORSE', 'FISHING', 'MIAMI'] |
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import math | |
import numpy as np | |
# Another example at http://stackoverflow.com/questions/10658866/calculating-pdf-of-dirichlet-distribution-in-python | |
def gamma(x): | |
return math.gamma(x) |
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import numpy as np | |
def projection_on_to_simplex(sphere_point): | |
# References | |
# Same implmentation as the one used here at https://gist.github.com/daien/1272551 | |
# Efficient Projections onto the .1-Ball for Learning in High Dimensions - http://machinelearning.org/archive/icml2008/papers/361.pdf | |
mu = np.sort(sphere_point)[::-1] | |
mu_sums = np.cumsum(mu) | |
rho = np.nonzero(mu * np.arange(1, sphere_point.shape[0] + 1) > (mu_sums - 1))[0][-1] |
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from iris import load_cube | |
import iris.quickplot as qplt | |
import matplotlib.pyplot as plt | |
from matplotlib import animation | |
import sys | |
# air_temp_location is the directory with your air.sig995 netCDF files | |
air_temp_location = "" | |
date = range(1948,2015) |
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start: | |
0x00401000: pushl %ebp | |
0x00401001: movl %esp, %ebp | |
0x00401003: subl $0x8, %esp | |
0x00401006: movl 0x14(%ebp), %eax | |
0x00401009: movl %eax, -4(%ebp) | |
0x0040100c: movl 0x10(%ebp), %ecx | |
0x0040100f: movsbl (%ecx), %edx | |
0x00401012: movl %edx, -8(%ebp) | |
0x00401015: movl -8(%ebp), %eax |
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from __future__ import absolute_import | |
""" | |
This will configure and run the nosetests for graph against remove ec2 servers. | |
Running this makes a few assumptions. | |
1) You must have access to graph on github. This will checkout the repo to run the tests | |
2) You must have an account on EC2 and be able to create instances. | |
In order to configure, you need to create a file in your home called ~/.boto that looks like: |
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import pycurl | |
import cStringIO | |
import json | |
import urllib | |
from collections import defaultdict | |
from time import sleep | |
import glob as g | |
base_api_url = "http://localhost:8090/" |
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let x = [1;3;4;5;1;2;10;7];; | |
let y = List.sort compare x;; | |
let convert_pos list n = | |
let rec aux i r = function | |
| [] -> r | |
| e::t -> aux (i+1) (r @ [e*n+i]) t | |
in aux 0 [] list;; |
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<!DOCTYPE html> | |
<html> | |
<head> | |
<meta charset="utf-8" /> | |
<title>My First Canvas Example</title> | |
</head> | |
<body style="margin:0px"> | |
<Canvas id="my_canvas"> </Canvas> | |
<script type="application/dart" src="dots.dart"></script> |
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import 'dart:html'; | |
import 'dart:math'; | |
void main() { | |
init_palau_flag(); | |
} | |
void init_palau_flag() { | |
palau_flag(); | |
} |
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