What is #Magento?

Magento is an e-commerce platform built atop open source technology, enabling online sellers to have ultimate control over the look, functionality, and content of their online shops. With Magento, there is the opportunity to deploy powerful marketing strategies, enjoy advanced catalog management, and manage search engine optimisation to maximise your brand’s exposure to your target market.

Today, Magento is one of the best ecommerce platforms available that outstrips other platforms such as WordPress and Shopify when it comes to the control you have over it.

This considerable power comes at a price, though. Magento needs serious hosting setup to shine fully and requires some degree of technical understanding. Granted, even complete novices can design pretty sites with its interface but having knowledge about basic hosting concepts and some scripting cannot hurt.

In addition, there is a large and helpful community of developers and other merchants behind Magento, who are always more than happy to help out newcomers. It is a robust system at its most basic level, and once it is integrated with other systems, its true power is realised.

Magento is super fast too. Any experienced developer will be able to tell you that overall speed is of high importance when it comes to e-commerce. Your customer is not going to wait around for five to 10 minutes for a page to load if she wants to make a purchase. If your site is slow, she will go elsewhere. With Magento, this never has to be an issue as the whole platform is highly optimised and designed to run at lightning fast speed.

If you want to learn more about Magento, check out our infographic below:

Distributed Evolutionary Algorithms in Python

DEAP

DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data structures transparent. It works in perfect harmony with parallelization mechanism such as multiprocessing and SCOOP.

DEAP includes the following features:

  • Genetic algorithm using any imaginable representation
    • List, Array, Set, Dictionary, Tree, Numpy Array, etc.
  • Genetic programing using prefix trees
    • Loosely typed, Strongly typed
    • Automatically defined functions
  • Evolution strategies (including CMA-ES)
  • Multi-objective optimisation (NSGA-II, SPEA2, MO-CMA-ES)
  • Co-evolution (cooperative and competitive) of multiple populations
  • Parallelization of the evaluations (and more)
  • Hall of Fame of the best individuals that lived in the population
  • Checkpoints that take snapshots of a system regularly
  • Benchmarks module containing most common test functions
  • Genealogy of an evolution (that is compatible with NetworkX)
  • Examples of alternative algorithms : Particle Swarm Optimization, Differential Evolution, Estimation of Distribution Algorithm

See the DEAP User’s Guide for DEAP documentation.

Installation

We encourage you to use easy_install or pip to install DEAP on your system. Other installation procedure like apt-get, yum, etc. usually provide an outdated version.

pip install deap

The latest version can be installed with

pip install git+https://github.com/DEAP/deap@master

If you wish to build from sources, download or clone the repository and type

python setup.py install

 

Source

#Tree #Traversals

Below is the Python code for traversing trees in various recursive modes like In order, Preorder, Post Order and their reverse orders…

The code is provided in python, but can be easily translated to Java/JS/PHP etc

#HackerRank: Computing the Correlation

Problem

You are given the scores of N students in three different subjects – MathematicsPhysics and Chemistry; all of which have been graded on a scale of 0 to 100. Your task is to compute the Pearson product-moment correlation coefficient between the scores of different pairs of subjects (Mathematics and Physics, Physics and Chemistry, Mathematics and Chemistry) based on this data. This data is based on the records of the CBSE K-12 Examination – a national school leaving examination in India, for the year 2013.

Pearson product-moment correlation coefficient

This is a measure of linear correlation described well on this Wikipedia page. The formula, in brief, is given by:

where x and y denote the two vectors between which the correlation is to be measured.

Input Format

The first row contains an integer N.
This is followed by N rows containing three tab-space (‘\t’) separated integers, M P C corresponding to a candidate’s scores in Mathematics, Physics and Chemistry respectively.
Each row corresponds to the scores attained by a unique candidate in these three subjects.

Input Constraints

1 <= N <= 5 x 105
0 <= M, P, C <= 100

Output Format

The output should contain three lines, with correlation coefficients computed
and rounded off correct to exactly 2 decimal places.
The first line should contain the correlation coefficient between Mathematics and Physics scores.
The second line should contain the correlation coefficient between Physics and Chemistry scores.
The third line should contain the correlation coefficient between Chemistry and Mathematics scores.

So, your output should look like this (these values are only for explanatory purposes):

0.12
0.13
0.95

Test Cases

There is one sample test case with scores obtained in Mathematics, Physics and Chemistry by 20 students. The hidden test case contains the scores obtained by all the candidates who appeared for the examination and took all three tests (Mathematics, Physics and Chemistry).
Think: How can you efficiently compute the correlation coefficients within the given time constraints, while handling the scores of nearly 400k students?

Sample Input

20
73  72  76
48  67  76
95  92  95
95  95  96
33  59  79
47  58  74
98  95  97
91  94  97
95  84  90
93  83  90
70  70  78
85  79  91
33  67  76
47  73  90
95  87  95
84  86  95
43  63  75
95  92  100
54  80  87
72  76  90

Sample Output

0.89  
0.92  
0.81

There is no special library support available for this challenge.

Solution(Source)

 

#HackerEarth: #BattleOfBots 9: Taunt

Problem

Taunt is a two player board game which is played on a 10X4 grid of cells and is played on opposite sides of the game-board. Each player has an allocated color, Orange ( First Player ) or Green ( Second Player ) being conventional. Each player has nine piece in total. The players move their pieces towards to his / her opponent’s area by moving their pieces strategically. Each piece has a different moving feature and is one of the 3 types of pieces.

Piece 1: It can move to horizontally or vertically adjacent cell, if the cell doesn’t contain a piece of same color.

enter image description here

Piece 2: It can move to horizontally adjacent cell or can move two steps forward, if the cell doesn’t contain a piece of same color (except the piece itself).

enter image description here

This type of piece can move to its own position if its in the second last row of the grid and going downward or if its in the second row of the grid and going upward.

enter image description here

Piece 3: It can move two step diagonally in the forward direction, if the cell doesn’t contain a piece of same color (except the piece itself).

enter image description here enter image description here

This type of piece can move to its own position if its in the second last row of the grid and going downward or if its in the second row of the grid and going upward.

enter image description here

Players take turns involving moves of pieces as mentioned above and can captures opponent’s piece by jumping on or over opponent’s pieces.

Note: Forward direction for first player is downward and for second player is upward.

If a piece (except piece 1) is moving downward and touches the last row, its direction will change i.e. now it will move upward. Similarly, once if a piece (except piece 1) is moving upward and touches the first row, its direction will change i.e. now it will move downward.

Rules:

  • Player can only move according to the moves mentioned above.
  • A player may not move an opponent’s piece.
  • A player can captures opponent’s piece by jumping on or over opponent pieces.

The game will end after 100 moves ( 50 moves for each player ) or when any of the players don’t have any move left. At the end of the game the player with majority of pieces will win.

We will play it on an 10X4 grid. The top left of the grid is [0,0] and the bottom right is [9,3].

Input:
The input will be a 10X4 matrix consisting only of 0,1or2. Next line will contain an integer denoting the total number of moves till the current state of the board. Next line contains an integer – 1 or 2 which is your player id.

In the given matrix, top-left is [0,0] and bottom-right is [9,3]. The y-coordinate increases from left to right, and x-coordinate increases from top to bottom.

A cell is represented by 3 integers.

First integer denotes the player id (1 or 2).
Second integer denotes the type of piece (1, 2 or 3).
Third integer denotes the direction of the piece (0 (upward) or 1 (downward)). When the piece is of first type, direction doesn’t matter as the piece is free to move to horizontally or vertically adjacent cell, if the cell doesn’t contain a piece of same color.

Empty cell is represented by 000.

Output:
In the first line print the coordinates of the cell separated by space, the piece you want to move.
In second line print the coordinates of the cell in which the piece will jump.
You must take care that you don’t print invalid coordinates. For example, [1,1] might be a valid coordinate in the game play if the piece is able to jump to [1,1], but [9,10] will never be. Also if you play an invalid move or your code exceeds the time/memory limit while determining the move, you lose the game.

Starting state
The starting state of the game is the state of the board before the game starts.

131 131 131 121
121 121 111 111
111 000 000 000
000 000 000 000
000 000 000 000
000 000 000 000
000 000 000 000
000 000 000 210
210 210 220 220
220 230 230 230

First Input
This is the input give to the first player at the start of the game.

131 131 131 121
121 121 111 111
111 000 000 000
000 000 000 000
000 000 000 000
000 000 000 000
000 000 000 000
000 000 000 210
210 210 220 220
220 230 230 230
0
1

 

SAMPLE INPUT
000 000 000 000
000 000 000 111
000 000 111 130
000 000 000 000
000 000 000 000
000 220 000 000
131 000 000 000
121 000 210 000
000 210 131 000
000 210 000 000
58
1
SAMPLE OUTPUT
8 2
8 0

Explanation

This is player 1’s turn, and the player will move the piece at [8,2] and will take two steps diagonally in downward direction and will be at [8,0]
After his/her move the state of game becomes:

000 000 000 000
000 000 000 111
000 000 111 130
000 000 000 000
000 000 000 000
000 220 000 000
131 000 000 000
121 000 210 000
130 210 000 000
000 000 000 000
59
2

Note: Direction of the piece is also changed from 1 to 0 as the piece was moving downward and touches the last row. This state will be fed as input to program of player 2.

Here is the code of the default bot.

Time Limit:1.0 sec(s) for each input file.
Memory Limit:256 MB
Source Limit:1024 KB

Sample Game

LDAP Connector

Below is a sample code to perform LDAP Queries. Just modify the configuration information and then provide any valid query to get the search results.

You can also modify the code to get custom business logic as required.

 

Sort a list of tuples by Nth item in Python

Suppose you have a list of tuples that looks something like this:

[('abc', 121),('abc', 231),('abc', 148), ('abc',221)]

And you want to sort this list in ascending order by the integer value inside the tuples.

We can achieve this using the key keyword with sorted().

sorted([('abc', 121),('abc', 231),('abc', 148), ('abc',221)], key=lambda x: x[1])

key should be a function that identifies how to retrieve the comparable element from your data structure. For example, the second element of the tuple, so we access [1].

Source: StackOverflow.com