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• с помоща на tf-idf се определят важните думи, така че ако се научат да може поне 80% от текста да бъде разбран

• да се използва API-то на Google Translate да се преведат тези думи и да се запазват в текстов файл

• да може да се прави quizz с тези думи, като може да се използва levenshtein distance за оценяване на това колко близо си до превода (0-100%)

• quizz-а да може да работи и в двете посоки на превод. Например:

  • да имаме quizz за EN -> BG
  • да имеме quizz за BG -> EN

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Linked list

Problems:

• Find the middle element of a linked list. Can you do the work with one pass over the list? See here.

• Reverse a linked list. Can you reverse the list with O(1) space complexity? See here.

• Given a singly linked list, determine if it is a palindrome. Could you do it in O(n) time and O(1) space? See here.

• Given a linked list, determine if it has a cycle in it. Can you solve it using O(1) memory? See here.

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"age";"job";"marital";"education";"default";"balance";"housing";"loan";"contact";"day";"month";"duration";"campaign";"pdays";"previous";"poutcome";"y"
30;"unemployed";"married";"primary";"no";1787;"no";"no";"cellular";19;"oct";79;1;-1;0;"unknown";"no"
33;"services";"married";"secondary";"no";4789;"yes";"yes";"cellular";11;"may";220;1;339;4;"failure";"no"
35;"management";"single";"tertiary";"no";1350;"yes";"no";"cellular";16;"apr";185;1;330;1;"failure";"no"
30;"management";"married";"tertiary";"no";1476;"yes";"yes";"unknown";3;"jun";199;4;-1;0;"unknown";"no"
59;"blue-collar";"married";"secondary";"no";0;"yes";"no";"unknown";5;"may";226;1;-1;0;"unknown";"no"
35;"management";"single";"tertiary";"no";747;"no";"no";"cellular";23;"feb";141;2;176;3;"failure";"no"
36;"self-employed";"married";"tertiary";"no";307;"yes";"no";"cellular";14;"may";341;1;330;2;"other";"no"
39;"technician";"married";"secondary";"no";147;"yes";"no";"cellular";6;"may";151;2;-1;0;"unknown";"no"
41;"entrepreneur";"married";"tertiary";"no";221;"

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import numpy as np
import pandas as pd

from nose.tools import *

from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import PolynomialFeatures

bank_data = pd.read_csv('./data/bank.csv', sep=";")
bank_features = bank_data.drop('y', axis=1)

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     _   _                             _
    / \ | |_ _ __ ___   ___  ___ _ __ | |__   ___ _ __ ___
   / _ \| __| '_ ` _ \ / _ \/ __| '_ \| '_ \ / _ \ '__/ _ \
  / ___ \ |_| | | | | | (_) \__ \ |_) | | | |  __/ | |  __/
 /_/   \_\__|_| |_| |_|\___/|___/ .__/|_| |_|\___|_|  \___|
                                |_|

Update intruction

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Features of ATMOSPHERE you may find useful

• You can take a full advantage of the ATMOSPHERE framework by using its distributed architecture.

  • You may execute the test remotely by changing the IP on the @Server annotation or create/modify the config.properties file. To connect remote Agent to a Server use runAgent command with CLI arguments.
  • You may use the Server and the Agent on separate machines.
  • You can connect multiple agents to the Server. Each Agent can handle up to 14 devices.

• Use the atmosphere-ui-viewer to dump the UI elements. Also you may find its remote control options useful.