tonysimpson

from matplotlib import pylab # .exe install is on sourceforge
import numpy# easy_install numpy
from mpl_toolkits.basemap import Basemap # easy_install basemap

# functions to create some random data points and convert them to meters via the map projection
def create_points_in_lon_lat(N=10000):
    return zip(numpy.random.standard_normal(N)*360, numpy.random.standard_normal(N) * 45)

def convert_lon_lat_points_to_meters_using_transform(points, tran):
    # maybe there is a better way to get long/lat into meters but this works ok

joemiller

#!/bin/bash
#
# this script will attempt to detect any ephemeral drives on an EC2 node and create a RAID-0 stripe
# mounted at /mnt. It should be run early on the first boot of the system.
#
# Beware, This script is NOT fully idempotent.
#

METADATA_URL_BASE="http://169.254.169.254/2012-01-12"

freeman-lab

/**
 * bisecting <master> <input> <nNodes> <subIterations>
 *
 * divisive hierarchical clustering using bisecting k-means
 * assumes input is a text file, each row is a data point
 * given as numbers separated by spaces
 *
 */

import org.apache.spark.SparkContext

yxtay

# References
# - https://www.tensorflow.org/versions/r0.10/tutorials/word2vec/index.html
# - https://github.com/tensorflow/tensorflow/blob/r0.10/tensorflow/examples/tutorials/word2vec/word2vec_basic.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import math