vst

##' Declares the generic as.json method.
##'
##' @param x the object to be marshalled to JSON
##' @param ... extra arguments to the actual function
##' @return a JSON representation of the consumed object.
##' @export
as.json = function (x, ...) {
  UseMethod("as.json")
}

JerrySievert

jerry@pi:/tmp/redis-2.4.15$ make -j 2
cd src && make all
make[1]: Entering directory `/tmp/redis-2.4.15/src'
MAKE hiredis
MAKE jemalloc
make[2]: Entering directory `/tmp/redis-2.4.15/deps/hiredis'
cc -std=c99 -pedantic -c -O3 -fPIC  -Wall -W -Wstrict-prototypes -Wwrite-strings -g -ggdb  net.c
checking for xsltproc... no
checking for gcc... gcc
checking whether the C compiler works... yes

ScottEvil

Installing GeoMesa on GeoServer
 - Downloaded geoserver 2.5.2 war wget http://downloads.sourceforge.net/project/geoserver/GeoServer/2.5.2/geoserver-2.5.2-war.zip
 - and the corresponding WPS plugin wget http://downloads.sourceforge.net/project/geoserver/GeoServer/2.5.2/extensions/geoserver-2.5.2-wps-plugin.zip
 - Deployed war file to Tomcat_HOME/webapps folder and then copied WPS plugin jars over to WEB-INF/lib and checked to ensure permissions were good
 - Download Accumulo 1.5.3 :: wget http://apache.go-parts.com/accumulo/1.5.3/accumulo-1.5.3-bin.tar.gz
 - Download Scala 2.10 :: wget http://downloads.typesafe.com/scala/2.10.5/scala-2.10.5.tgz
 - Download Kafka 2.10-0.8.2.1 (Kafka 0.8 install that corresponds to scala 2.10) http://ftp.wayne.edu/apache/kafka/0.8.2.1/kafka_2.10-0.8.2.1.tgz
 - Download Hadoop wget http://apache.mirrors.tds.net/hadoop/common/hadoop-2.6.0/hadoop-2.6.0.tar.gz
 
 Unzip each of the downloads

Kaixhin

--[[
--   Element-Research Torch RNN Tutorial for recurrent neural nets : let's predict time series with a laptop GPU
--   https://christopher5106.github.io/deep/learning/2016/07/14/element-research-torch-rnn-tutorial.html
--]]

--[[
--   Part 1
--]]

require 'rnn'

tmyymmt

object HexBytesUtil {

  def hex2bytes(hex: String): Array[Byte] = {
    hex.replaceAll("[^0-9A-Fa-f]", "").sliding(2, 2).toArray.map(Integer.parseInt(_, 16).toByte)
  }

  def bytes2hex(bytes: Array[Byte], sep: Option[String] = None): String = {
    sep match {
      case None => bytes.map("%02x".format(_)).mkString
      case _ => bytes.map("%02x".format(_)).mkString(sep.get)

georgeblck

###
### This is a batched LSTM forward and backward pass. Written by Andrej Karpathy (@karpathy)
### BSD License
### Re-written in R by @georgeblck
###

rm(list=ls(all=TRUE))

LSTM.init <- function(input_size, hidden_size, fancy_forget_bias_init = 3){
  # Initialize parameters of the LSTM (both weights and biases in one matrix) 

aappddeevv

You may need from time to time to grab a day-level granularity date dimension. You can find some wizards in some database packages or some PL/SQL commands to generate it, but if you just need something simple, try the below:

package datedim

import collection.JavaConverters._
import au.com.bytecode.opencsv.CSVReader
import java.io.FileReader
import scala.collection.mutable.ListBuffer
import au.com.bytecode.opencsv.CSVWriter

nagadomi

-- sudo luarocks install csvigo
-- でインストールしておく
require 'csvigo'

--[[ test.csv
a,b,c,d   
1,2,3,4
5,5,3,3
--]]

philipperemy

from __future__ import print_function

import numpy as np
from keras.callbacks import Callback
from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential
from numpy.random import choice

from utils import prepare_sequences

karpathy

--[[ 
Efficient LSTM in Torch using nngraph library. This code was optimized 
by Justin Johnson (@jcjohnson) based on the trick of batching up the 
LSTM GEMMs, as also seen in my efficient Python LSTM gist.
--]]

function LSTM.fast_lstm(input_size, rnn_size)
  local x = nn.Identity()()
  local prev_c = nn.Identity()()
  local prev_h = nn.Identity()()