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/**
  * Started to rough this naive S3-native filesystem RDD out because I need to use IAM
  * profiles for S3 access and also https://issues.apache.org/jira/browse/HADOOP-3733.
  * 
  * Use at your own risk, bear in mind this is maybe 30 - 45min of work and testing and 
  * expect it to behave as such.
  * 
  * Feedback/criticism/discussion welcome via Github/Twitter
  * 
  * In addition to Spark 1.0.x, this depends on Amazon's S3 SDK, dependency is as follows:

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package controllers

import play.api.mvc._

import org.squeryl._
import org.squeryl.PrimitiveTypeMode._

/**
 * bind a session to each action with this nifty Squeryl trait
 */

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This is a fork of Bootstrap Typeahead that adds minimal but powerful extensions and eliminates matching to prevent over-filtering of AJAX responses.

Forked from https://gist.github.com/1891669.

For example, process typeahead list asynchronously and return objects

  # This example does an AJAX lookup and is in CoffeeScript
  $('.typeahead').typeahead(
    # source can be a function

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/**
 * actor wrapping access for browser socket
 */
class BrowserSocket(
  val s: WebSocketConnection,
  val userId: Long,
  val teamId: Long

) extends Actor {

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package org.deeplearning4j.VGGwebDemo;

import org.nd4j.linalg.factory.Nd4j;
import org.deeplearning4j.nn.modelimport.keras.trainedmodels.Utils.ImageNetLabels;
import org.datavec.image.loader.NativeImageLoader;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.modelimport.keras.trainedmodels.TrainedModels;
import org.deeplearning4j.util.ModelSerializer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization;

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#!/bin/bash

# helper function that ensures cmd returns 0 exit code
function checkexit {
    "$@"
    local status=$?
    if [ $status -ne 0 ]; then
        echo "error with $1" >&2
        exit 1
    fi

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                        Name                                       ALEXNET
                 Description                        CIFAR-10 128x3x224x224
            Operating System                  GNU/Linux Ubuntu 16.04.2 LTS
                     Devices              TITAN X (Pascal) 6 1 12779978752
                   CPU Cores                                            12
                     Backend                                          CUDA
                 BLAS Vendor                                        CUBLAS
                CUDA Version                                          8000
               CUDNN Version                                          6020
                Total Params                                      20344650

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import lombok.extern.slf4j.Slf4j;
import org.datavec.api.berkeley.Pair;
import org.datavec.api.io.filters.RandomPathFilter;
import org.datavec.api.io.labels.ParentPathLabelGenerator;
import org.datavec.api.split.FileSplit;
import org.datavec.api.split.InputSplit;
import org.datavec.image.loader.NativeImageLoader;
import org.datavec.image.recordreader.ImageRecordReader;
import org.datavec.image.transform.FlipImageTransform;
import org.datavec.image.transform.ImageTransform;

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for i in $(find ./ -maxdepth 2 -iname '*.png' -type f -print); do if $(identify "$i" 2>&1 |grep -q improper); then echo "$i incorrect"; fi; done

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// our reduction op class that we will need shortly
class GeoAveragingReduction(columnOutputName: String="AveragedLatLon") extends AggregableColumnReduction {
        
    override def reduceColumn(columnData: java.util.List[Writable]): Writable = {
        val coordinates = columnData.map( d => d.toString.split(",") ).map{ case Array(lat,lon) => (lat.toDouble,lon.toDouble) }
        val (lat,lon) = coordinates.reduceLeft(avgCoordinates)
        new Text(s"""$lat,$lon""")
    }

    private def avgCoordinates(latlon1: (Double, Double), latlon2: (Double, Double)): (Double, Double) = {