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//------------------------------------------------------------------------------
// Download Langauges
//
// VITLanguage: An individual language, name ("English", "Spanish") and code
//   ("en", "es")
//
// VITLanguageManager: Download an array of VITLanguages from API
//
//------------------------------------------------------------------------------

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x

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### THE SCRIPT

#!/usr/bin/env ruby
dump = `ps -ef | grep zeus`
processes = dump.split("\n")

processes.each do |process|
  process_id = process.scan(/[0-9]+/)[1]
  `sudo kill -9 #{process_id}`
end

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require 'selenium-webdriver'
require 'selenium/client'
#system 'java -jar ~/Downloads/selenium-2.37.0/selenium-server-standalone-2.37.0.jar &'

browser = Selenium::Client::Driver.new \
     :host => "localhost", 
     :port => 4444, 
     :browser => "*firefox", 
     :url => "http://www.pictage.com",
     :timeout_in_second => 60

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from sklearn import linear_model

Y = np.array(Y) # array of training answers, 1 or 0

X = np.array([]) # array of image brightness
for filename in filenames:
    # load the images
    image = misc.imread(constants.IMAGE_64_PATH + '/' + filename, mode='L')
    avg_brightness = np.matrix(image).mean()
    X = np.append(X, avg_brightness)

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import tflearn
from tflearn.data_preprocessing import ImagePreprocessing
from tflearn.data_augmentation import ImageAugmentation
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_1d, max_pool_1d
from tflearn.layers.estimator import regression

img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()

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import tflearn
from tflearn.data_preprocessing import ImagePreprocessing
from tflearn.data_augmentation import ImageAugmentation

from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_1d, max_pool_1d
from tflearn.layers.estimator import regression

img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()

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"a4s_employee_full_name"=>
  "\x04\bo:\eRepresentableDataValue\b:\v@fieldo:!EmployeeFullNameBuiltInField\x00:\f@recordo:\rEmployee\x10:\x10@attributeso:\x1FActiveRecord::AttributeSet\x06;\no:$ActiveRecord::LazyAttributeHash\n:\v@types}\x10I\"\aid\x06:\x06ETo:?ActiveRecord::ConnectionAdapters::PostgreSQL::OID::Integer\t:\x0F@precision0:\v@scale0:\v@limit0:\v@rangeo:\nRange\b:\texclT:\nbeginl-\a\x00\x00\x00\x80:\bendl+\a\x00\x00\x00\x80I\"\x0Fcreated_at\x06;\x0ETU:JActiveRecord::AttributeMethods::TimeZoneConversion::TimeZoneConverter[\t:\v__v2__[\x00[\x00o:@ActiveRecord::ConnectionAdapters::PostgreSQL::OID::DateTime\b;\x100;\x110;\x120I\"\x0Fupdated_at\x06;\x0ETU;\x18[\t;\x19[\x00[\x00@\x15I\"\fuser_id\x06;\x0ET@\fI\"\x1Aineligible_for_rehire\x06;\x0ETo: ActiveRecord::Type::Boolean\b;\x100;\x110;\x120I\"\euntracked_prior_tenure\x06;\x0ET@\fI\"\ttags\x06;\x0ETo:=ActiveRecord::ConnectionAdapters::PostgreSQL::OID::Array\a:\r@subtypeo:\x1DActiveRecord::Type::Text\b;\x100;\x110;\x120:\x0F@delimiterI\"\x06,\x06;\x0ET

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# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(
    data_words, data_plant_types, test_size=0.33, random_state=42)

# Build a neural network
network = input_data(shape=[None, len(data_words[0])])
network = fully_connected(network, 2048, activation='relu')
network = fully_connected(
    network, len(data_plant_types[0]), activation='softmax')

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# Git
# https://gist.github.com/n0n0x/7be453ff18f58020bafe
source ~/.git_bash_completion

# Go
export GOPATH=$HOME/development/go
export GOSRC=$GOPATH/src/github.com/LastZactionHero
export GOBIN=$GOPATH/bin
PATH=$PATH:$GOBIN
PATH=$PATH:/usr/local/go/bin