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How to implement a Berlekamp–Massey algorithm? Explained with easy example

The Berlekamp–Massey algorithm is an algorithm that will find the shortest linear feedback shift register for a given binary output sequence. The algorithm will also find the minimal polynomial of a linearly recurrent sequence in an arbitrary field. I have used sage for computing this program. How to implement a Berlekamp–Massey algorithm? Explained with easy example # Berlekamp-Massey Algorithm #from __future__ import print_function s = [GF(2)(0), 0, 1, 0, 0, 0, 0, 0, 1, 1, 0] #input sequence n = len(s) C = [GF(2)(1)] B = [GF(2)(1)] temp = [] T = [] L = 0 N = 0 m = -1 print '----- n',n print '-----------------------------------------------------------------------' while N

Processing Large JSON dataset with Spark SQL with better performance and optimization

Processing Large JSON dataset with Spark SQL.md Processing Large JSON dataset with Spark SQL Spark SQL offers built-in support, like JSON, parquet, XML, CSV, text files, and other numbers of data formats. Each new release of the Spark version contains various improvements in the API that make use of JSON data more convenient and efficient. JavaScript Object Notation (JSON) format is a very basic file readable for humans, and convenient to use. But its simplicity, since it’s schema-less, can lead to problems. Such services can return insane JSON responses containing integer numbers as strings, or encode nulls in different ways, such as null , "" , or even "null" , particularly when you have to deal with unreliable third-party data sources. Assuming you have an access to spark tools like spark-shell to execute the below commands. Loading data You can read and JSON directly from files like HDFS or S3 bucket path to DataFrame: Example for S3 val df = sp...

Approach to generate case Classes from the Complex Nested JSON Schema in Spark

Approach to generate case Classes from the Complex Nested JSON Schema in Spark.md Approach to generate case Classes from the Complex Nested JSON Schema in Spark I worked in the Big Data Projects which does the ETL (Extract-Transform-Load) jobs from Oracle RDBMS to big data ecosystem (Spark, HDFS and Hive). Doing an ETL for lots of clients isn’t a easy task. You have to do lots for Oracle ETL query conversion to Spark query. You need to convert the functions that are in-built and custom, apparently you have to write UDF functions to handle it. So doing all these type of work we need a framework to maintain code, run unit testcases and scripts to validation. In the framework I mentioned, case class plays an important role during the extraction and validation especially handling the parquet files. case class is the meta-data for the tables or data files that we are dealing. Usally it contains the field (column) name and its datatype case class makes the spark to understand the t...

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