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void NetworkGenetic::allocateHostAndGPUObjects( size_t deviceRam, size_t hostRam){
    long int hostGenSize = hostRam;
    long int deviceGenSize = deviceRam;
    std::cerr<<"total free device ram : "<<deviceRam<<std::endl;
    std::cerr<<"total free host ram : "<<hostRam<<std::endl;

    //this block below makes sure that the number of objects in stream is exactly half of the total amount of allocatable space on the GPU
    _streamSize = deviceGenSize/(sizeof(double)*2);

    while(_streamSize%_hostParams.array[2] || (_streamSize/_hostParams.array[2])&(_streamSize/_hostParams.array[2]-1)) // get the largest number divisible by the individual size, the threads in a block, and the size of a double

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project(Earthquake_Forecaster)
cmake_minimum_required(VERSION 3.2.1)
find_package(CUDA REQUIRED)
INCLUDE_DIRECTORIES(${CMAKE_SOURCE_DIR}/headers)
INCLUDE_DIRECTORIES(${CMAKE_SOURCE_DIR}/src)
INCLUDE_DIRECTORIES(${CMAKE_SOURCE_DIR}/kernels)
INCLUDE_DIRECTORIES(${CMAKE_SOURCE_DIR}/libs/rapidjson/include)
LINK_DIRECTORIES(/usr/local/cuda/lib64)

set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib)

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#include <neuroFunc.h>
#include <utilFunc.h>

__host__ __device__ void neuroSum(double &store, double &input){
    store += input;
}

__host__ __device__ void neuroMulti(double &store, double &input){
    store *= input;
}

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/**
  * Created by james on 27/01/16.
  */

import algorithmia.VideoSplits._
import org.scalatest._

//TODO: figure out how to return a data:// string instead of a https://api.algorithmia.com/v1 string
class splitSpec extends FunSuite with Matchers{

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/**
  * Created by james on 27/01/16.
  */


import algorithmia.VideoSplits._
import com.algorithmia.Algorithmia
import org.scalatest._

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case class SplitOnce(inputVideoUrl: String,
                     outputFolder: String,
                     outputFilename: String,
                     startTime: String,
                     stopTime: String) extends Inputs{
  require(inputVideoUrl != null, "input video must not be null.")
  require(outputFolder != null, "output folder must not be null.")
  require(outputFilename != null, "output Filename must not be null.")
  require(startTime != null, "start time must not be null.")

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from sklearn.externals import joblib


self.svmStorage = "/tmp/svm/"
    def onConnect(self, request):
        print("Client connecting: {0}".format(request.peer))
        self.training = True
        self.svm = joblib.load(self.svmStorage + 'my_model.pkl')
        
            def saveSVM(self):

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  var itrTrain: Int = 0
  trainDirectoryLabels.foreach(directory => {
    val label = itrTrain
    itrTrain += 1
    val images = directory.listFiles.toList
    images.foreach(image => {
      val vec = new ImageVectorizer(image, numOfTrainLabels, label)
      val imageSet: DataSet = vec.vectorize()
      
      model.fit(imageSet)

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  val imageRecordReader = new ImageRecordReader(250, 250, 3, labels)
  imageRecordReader.initialize(new FileSplit(rawDir))

  val iterator: DataSetIterator = new RecordReaderDataSetIterator(imageRecordReader, batchSize, 0, labels.length)

  while(iterator.hasNext){
    val nextData: DataSet = iterator.next
    nextData.scale()

    val trainTest = nextData.splitTestAndTrain(0.25d)

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case class SplitOnce(inputVideoUrl: String,
                     outputFolder: String,
                     outputPrefix: String,
                     outputExtension: String,
                     startTime: String,
                     stopTime: String) extends Inputs
                     
                     
//this had a runtime exeception, SplitOnce.getClass returned the SplitOnce Companion Obj