Alexander Petrov apetrov

## tf_embedding.py
feature_columns = []
for feature_name in CATEGORICAL_COLUMNS:
    vocabulary = df[feature_name].unique()
    column = tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary)
    embedding_column = tf.feature_column.embedding_column(column, dimension=len(vocabulary))
    feature_columns.append(embedding_column)

for feature_name in NUMERIC_COLUMNS:
    feature_columns.append(tf.feature_column.numeric_column(feature_name, dtype=tf.float32))


## Rakefile
# Adding source locations to rake trace
class Rake::Task
  def execute_with_actions_logs(args=nil)
    @actions.each do |t|
      if application.options.trace
        puts "#{name} #{t.inspect}"
      end
    end
    execute_without_actions_logs(args)
  end

## database_dsl.rb
select [user_id, sum(payout),sum(fee).as(fee)], from: :transactions do
  join :users do
    users :id
    transactions :user_id
  end
  groupby :user_id
  where { user_id [1,2,3] }
  where { fee > 40 }
  order "sum(payout)", :desc
end

## validation_test.rb
# Let's say user wants to charge a scooter.
class Charge
  belongs_to :user
  belongs_to :scooter
end

# we want to ensure that scooter is discharged
class Charge
  belongs_to :user
  belongs_to :scooter

## -
2017-07-27T12:03:27.452Z 12559 TID-orl6iw5e8 ElasticRunner JID-b787bc0bcf984d41eebb12b5 INFO: fail: 13.223 sec                                               2017-07-27T12:03:27.452Z 12559 TID-orl6iw5e8 WARN: {"class"=>"ElasticRunner", "args"=>["c35e07c2-8b27-4e9a-9204-807c75a222e0", "inventory-forecast-es01.virool", "traffic/impressions", {"elastic"=>"forecasting_traffic/1501038000-c35e07c2-8b27-4e9a-9204-807c75a222e0.json.gz", "from"=>"2017-07-26T03:00:00.000Z", "to"=>"2017-07-26T04:00:00.000Z"}], "retry"=>20, "queue"=>"load_common", "jid"=>"b787bc0bcf984d41eebb12b5", "created_at"=>1501156944.0693657, "enqueued_at"=>1501156994.228116, "error_message"=>"Failure \e[0;37m2017-07-27 12:03:27.223\e[0m [\e[32minfo\e[0m] Connection reset by peer\n/opt/rubies/2.1.3/lib/ruby/2.1.0/net/protocol.rb:153:in `read_nonblock'\n/opt/rubies/2.1.3/lib/ruby/2.1.0/net/protocol.rb:153:in `rbuf_fill'\n/opt/rubies/2.1.3/lib/ruby/2.1.0/net/protocol.rb:134:in `readuntil'\n/opt/rubies/2.1.3/lib/ruby/2.1.0/net/protocol.rb:144:in `readl

## -
10news.com
abbreviations.com
abc.es
abc15.com
abc2news.com
adn.com
ambitiouskitchen.com
andhrajyothy.com
anoregoncottage.com
apieceofrainbow.com

## keras-iris.py
import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from keras.utils import np_utils
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder
from sklearn.pipeline import Pipeline

## app.rb

      
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## -
sap.ui.controller("controller.BehaviorAllPages", {

  onInit: function() {
    this.loadReportData();
  },

  onBeforeRendering: function() {
  },

  onAfterRendering: function() {

## -
sap.ui.controller("controller.BehaviorAllPages", {

	onInit: function() {
		this.loadReportData();
	},

	onBeforeRendering: function() {
	},

	onAfterRendering: function() {
	feature_columns = []
	for feature_name in CATEGORICAL_COLUMNS:
	vocabulary = df[feature_name].unique()
	column = tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary)
	embedding_column = tf.feature_column.embedding_column(column, dimension=len(vocabulary))
	feature_columns.append(embedding_column)

	for feature_name in NUMERIC_COLUMNS:
	feature_columns.append(tf.feature_column.numeric_column(feature_name, dtype=tf.float32))
	# Adding source locations to rake trace
	class Rake::Task
	def execute_with_actions_logs(args=nil)
	@actions.each do \|t\|
	if application.options.trace
	puts "#{name} #{t.inspect}"
	end
	end
	execute_without_actions_logs(args)
	end
	select [user_id, sum(payout),sum(fee).as(fee)], from: :transactions do
	join :users do
	users :id
	transactions :user_id
	end
	groupby :user_id
	where { user_id [1,2,3] }
	where { fee > 40 }
	order "sum(payout)", :desc
	end
	# Let's say user wants to charge a scooter.
	class Charge
	belongs_to :user
	belongs_to :scooter
	end

	# we want to ensure that scooter is discharged
	class Charge
	belongs_to :user
	belongs_to :scooter
	10news.com
	abbreviations.com
	abc.es
	abc15.com
	abc2news.com
	adn.com
	ambitiouskitchen.com
	andhrajyothy.com
	anoregoncottage.com
	apieceofrainbow.com
	import numpy
	import pandas
	from keras.models import Sequential
	from keras.layers import Dense
	from keras.wrappers.scikit_learn import KerasClassifier
	from keras.utils import np_utils
	from sklearn.model_selection import cross_val_score
	from sklearn.model_selection import KFold
	from sklearn.preprocessing import LabelEncoder
	from sklearn.pipeline import Pipeline
	sap.ui.controller("controller.BehaviorAllPages", {

	onInit: function() {
	this.loadReportData();
	},

	onBeforeRendering: function() {
	},

	onAfterRendering: function() {