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#  This gist solves the hard-margin SVM problem in three ways: using quadprog, using kernlab's ipop, and by 
#  the e1071 wrapper around libsvm.
#
#  author: R. Walker  (r_walker@zoho.com)
#  LICENSE: MIT
library("quadprog")
library("kernlab")
library("e1071")

# Use Fisher iris data and binarize one of the species

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ipoptr_qp <- function(Dmat, dvec, Amat, bvec, ub=100){
  # Solve the quadratic program
  #
  # min -d^T x + 1/2 x^T D x
  #   s.t. A%*%x>= b
  #
  # with ipoptr.
  
  n <- length(bvec)
  # Jacobian structural components

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import json
import csv
from collections import defaultdict

# load the mapping (supplied by the dataset) from id into book_id
with open("goodreads/book_id_map.csv") as f:
    reader = csv.reader(f)
    _ = next(reader)
    book_id_map = {int(_id): int(book_id) for _id, book_id in reader}

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import json
from scipy.sparse import coo_matrix, save_npz
import numpy as np


print("reading data...")
with open("goodreads/interactions.json") as f:
    data = json.load(f)
    books = data["books"]
    interactions = data["interactions"]

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{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "s3:ListBucket"
            ],
            "Resource": [
                "arn:aws:s3:::fancy-bucket"

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import argparse
import json
import sys
import warnings
import numpy as np

from scipy.sparse import load_npz, coo_matrix

class CooccurenceRecommender:
    

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# image segmentation demo
# author: rwalker (ryan@ryanwalker.us)
# license: MIT

library("jpeg")
library("png")
library("graphics")
library("ggplot2")
library("gridExtra")
#######################################################################################

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import org.json4s.{DefaultFormats, Formats}
import scala.language.postfixOps
import scalate.ScalateSupport
import org.scalatra._
import java.io.PrintWriter

class Chunks extends ScalatraServlet with ScalateSupport {
  /*
    Simple example of chunked transfer encoding in Scalatra (scala web framework)
  */

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#!/usr/bin/python3
#   This script is a simple introduction to the python elasticsearch API. 
#
#   This script will populate an elasticsearch index from a file and then give a simple command line query interface.
#   Each line of the input file will be mapped into a JSON document of the form { "text": "my file line..." } and added
#   to the index. 
#
#   You can use Docker to spin up a local elasticsearch instance to play around with, e.g.
#   docker run --name elasticsearch -d -p 9200:9200 elasticsearch:latest
#

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