Ojitha Hewa Kumanayaka

I was inspired to write this simple blog after reading the Microsoft Migrate a SQL Server database from Windows to Linux using backup and restore .This article about how to restore the AdventureWork on Linux, but this blog about MSSQL server 2017 on Mac OS Sierra. MSSQL available as a docker image to install on Mac. If you have Docker installed in your Mac, simple to install with two commands as shown in the http://aka.ms/sqldev site: docker pull microsoft/mssql -server -linux docker run -e 'ACCEPT_EULA=Y' -e 'SA_PASSWORD=yourStrong(!)Password' -p 1433 : 1433 -d microsoft/mssql -server -linux After the docker installation, login to the doker: docker exec -i -t < CONTAINER ID > /bin/bash Create a new folder “backup” in the “/var/opt/mssql”. If you are not admin run the command sudo su to login as super user. cd /var/opt/mssql/ mkdir backup Your work as admin in the docker is finished. Download the AdventureWork from https://msftdbpro...

Please read the wikipedia Collatz Conjecture first. Here the python function for the injecture: def collatz (n) : return n / 2 if n % 2 == 0 else 3 * n + 1 When you sequence , you get the result: n = 10 sequence = [n] while n != 1 : n = collatz(n) sequence.append(n) Generator functions are lazy. Using yield statement you can create generator function as follows: def collatz_iter (n) : while n != 1 : n = collatz(n) yield n Above function is lazy. It doesn’t compute, until iterate over that: s =collatz_iter( 10 ) for i in s: print(i) Reference: The Five Kinds of Python Functions by Steven F. Lott Publisher: O’Reilly Media, Inc.

Introduction This is Panda exercises: Data Frames In this section, I explore how to create data frames from different ways: list dictionary Json csv file In addition to that basic Data Frame (DF) manipulations: import pandas as pd cols = { 'name' : [ 'Ted' , 'Mak' , 'Nina' , 'Leo' ] , 'age' : [ 50 , 20 , 33 , 25 ] } l_students= [{ 'name' : 'Tailor' , 'grade' : '10' , 'math' : 60 } ,{ 'name' : 'Lora' , 'grade' : '09' , 'math' : 80 } ,{ 'name' : 'Joe' , 'grade' : '11' , 'math' : 56.90 } ,{ 'name' : 'Tailor' , 'grade' : '11' , 'math' : 68.98 } ] studemtDF = pd.DataFrame(l_students) # read from the json import json json_students = json.dumps(l_students) # [ # { # "grade": "10", # ...

Recently I started to reach to integrate Atom editor with Spark pyspark. In addition to the Atom, I found how to integrate pyspark with IntelliJ Idea which I suppose to discuss later. The nice thin about Atom is hydrogen plugin which you can use for inline evaluation with python. Here the steps Install Spark Install Atom Install hydrogen plugin to atom most important to set the PYTHONPATH as follows export PYTHONPATH = /<SPARK_HOME>/python :/<SPARK_HOME>/python/lib/py4j- 0 . 9 -src.zip Now run the following code to verify. Here the testing code. from pyspark import SparkContext from pyspark import SQLContext sc = SparkContext() sqlContext = SQLContext(sc) df = sqlContext.createDataFrame([( "Ojitha" , "Kumanayaka" ),( "Mark" , "Anthony" )],( "first_name" , "last_name" )) df.show()    

Algorithm Analysis measure the speed of the task algorithm suppose to accomplished. Here the necessary maths back ground to analyse the algorithmic complexity. CONTENTS 12 -fold way Order of the Growth Algorithm Analysis of Sorting Algorithm Analysis of Searching Appendix There are two basic rules in the Algorithm analysis: Rule of sum: if an action is performed making A choices or B choices, then it can be performed \(A+B\) ways. Rule of product: If an action can be performed by making A choices followed by B choices, then it can be performed \(A\times B\) ways. Permutation: an ordered list where every object appears exactly once \[ _{n}P_{r} = \frac {n!} {(n - r)!} \text{ where } !0 = 1 \] combinations: When order is not important and the repetition is not allowed, the number of ways to choose k from the distinct n is as for \(n \ge k \ge 0\): \[ {n \choose k }= \frac {n!}{ k!(n-k)! } \] in general, choosing k out of n is same as not cho...

These are the notes I've achieved certificate from the well known Recommender System course .  

The expectation of this blog is to enable developer to start LDAP integration quickly and easily with Spring Security. I have used ApacheDS which is embedded in the Apache Studio. Here the steps: The DN dc=example,dc=com is a example domain controller you can easily follow the documentation. Here the ldif document #[1] create domain (distinguished name) dn: dc=example,dc=com objectclass: top objectclass: domain dc: example #[ 2 ] create people organizational unit dn: ou=people,dc=example,dc=com objectClass: organizationalUnit objectClass: top ou: people #[ 3 ] create a user dn: uid=ojitha,ou=people,dc=example,dc=com objectClass: organizationalPerson objectClass: person objectClass: inetOrgPerson objectClass: top cn: Ojitha Hewa sn: Hewa uid: ojitha #[ 4 ] create group dn: ou=groups,dc=example,dc=com ...