Link to the Elasticsearch Blog.
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##' 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") | |
} |
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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 |
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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) |
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ZIP,LAT,LNG | |
00601,18.180555, -66.749961 | |
00602,18.361945, -67.175597 | |
00603,18.455183, -67.119887 | |
00606,18.158345, -66.932911 | |
00610,18.295366, -67.125135 | |
00612,18.402253, -66.711397 | |
00616,18.420412, -66.671979 | |
00617,18.445147, -66.559696 | |
00622,17.991245, -67.153993 |
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
A personal diary of DataFrame munging over the years.
Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)
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-- 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 | |
--]] |
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--[[ | |
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()() |
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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 |
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