oeegee

def dotProduct(vector: Array[Int], matrix: Array[Array[Int]]): Array[Int] = { 
    // ignore dimensionality checks for simplicity of example 
    (0 to (matrix(0).size - 1)).toArray.map( colIdx => { 
        val colVec: Array[Int] = matrix.map( rowVec => rowVec(colIdx) ) 
        val elemWiseProd: Array[Int] = (vector zip colVec).map( entryTuple => entryTuple._1 * entryTuple._2 ) 
        elemWiseProd.sum 
    } )
}
 
 

oeegee

import spark.SparkContext
import SparkContext._

/**
 * A port of [[http://blog.echen.me/2012/02/09/movie-recommendations-and-more-via-mapreduce-and-scalding/]]
 * to Spark.
 * Uses movie ratings data from MovieLens 100k dataset found at [[http://www.grouplens.org/node/73]]
 */
object MovieSimilarities {

oeegee

NAVER DEVIEW 2017 Slides

DAY 1

• Open, Share, Enjoy : 네이버의 오픈소스 활동
• 책 읽어주는 딥러닝: 배우 유인나가 해리포터를 읽어준다면 (데모, 소스코드)
• Chromium Binging 기술을 Node.js에 적용해보자
• 네이버랩스의 로보틱스 연구 소개
• Clova Platform 인공지능을 엮는 기술

oeegee

#!/bin/bash

# Minimum TODOs on a per job basis:
# 1. define name, application jar path, main class, queue and log4j-yarn.properties path
# 2. remove properties not applicable to your Spark version (Spark 1.x vs. Spark 2.x)
# 3. tweak num_executors, executor_memory (+ overhead), and backpressure settings

# the two most important settings:
num_executors=6
executor_memory=3g

oeegee

#!/bin/sh
BOOT2DOCKER_CERTS_DIR=/var/lib/boot2docker/certs
CERTS_DIR=/etc/ssl/certs
CAFILE=${CERTS_DIR}/ca-certificates.crt

for cert in $(/bin/ls -1 ${BOOT2DOCKER_CERTS_DIR}); do
    SRC_CERT_FILE=${BOOT2DOCKER_CERTS_DIR}/${cert}
    CERT_FILE=${CERTS_DIR}/${cert}
    HASH_FILE=${CERTS_DIR}/$(/usr/local/bin/openssl x509 -noout -hash -in ${SRC_CERT_FILE} 2>/dev/null)