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rails_env = ENV['RAILS_ENV'] || ENV['RACK_ENV'] || 'development' | |
Split.redis = YAML.load_file(Rails.root.join('config/split.yml')).fetch(rails_env) | |
Split.configure do |config| | |
adapter = Split::Persistence::RedisAdapter.with_config( | |
lookup_by: :params_user_id, | |
namespace: 'abtesting_participant_id', | |
expire_seconds: 2_592_000, # 30 days | |
) |
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curl -XPUT localhost:9200/test -d '{ | |
"settings": { | |
"index": { | |
"number_of_shards": 1, | |
"number_of_replicas": 0 | |
} | |
}, | |
"mappings": { | |
"test" : { | |
"properties" : { |
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sed -e 's/{"took":[0-9]*,"timed_out":false,"_shards":{"total":[0-9]*,"successful":[0-9]*,"failed":0},"hits":{"total":[0-9]*,"max_score":[0-9\.]*,"hits":\[\(.*\)\]}}$/\1/' -e 's/}},/}}\n/g' -e 's/{"_index"/{ "index":{"_index"/' -e 's/,"_score":[0-9\.]*, "_source" : /}}\n/g' |
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appender: | |
socketA: | |
type: org.apache.log4j.net.SocketAppender | |
port: 9500 | |
remoteHost: logstash-server | |
socketB: | |
type: org.apache.log4j.net.SocketAppender | |
port: 9501 | |
remoteHost: logstash-server |
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package org.elasticsearch.index.similarity; | |
import org.apache.lucene.search.similarities.DefaultSimilarity; | |
import org.elasticsearch.common.logging.ESLogger; | |
import org.elasticsearch.common.logging.Loggers; | |
public class NormRemovalSimilarity extends DefaultSimilarity { | |
private ESLogger logger; |
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curl -XPOST localhost:9200/boosts -d ' | |
{ | |
"settings": { | |
"number_of_shards": 1, | |
"number_of_replicas": 0 | |
}, | |
"mappings": { | |
"doc": { | |
"properties": { | |
"text": { |
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{ | |
"query": { | |
"match_all": {} | |
}, | |
"filter": { | |
"term": { | |
"model": "vw" | |
} | |
}, | |
"facets": { |
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// TEST DATA | |
curl -XPUT http://localhost:9200/twitter/user/kimchy -d '{ | |
"name" : "Shay Banon" | |
}' | |
curl -XPUT http://localhost:9200/twitter/tweet/1 -d '{ | |
"user": "kimchy", | |
"post_date": "2009-11-15T13:12:00", | |
"message": "Trying out elasticsearch, so far so good?" |
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// define inside file timetest.m | |
function blank = timetest(x, y, MAX_ITR) | |
m = length(y); | |
x = [ones(m, 1) x]; | |
theta = zeros(size(x(1,:)))'; % initialize fitting parameters | |
alpha = 0.07; | |
for num_iterations = 1:MAX_ITR | |
grad = (1/m).* x' * ((x * theta) - y); | |
theta = theta - alpha .* grad; |