mamonu

from pyspark import SparkContext
from pyspark.sql import SQLContext
from pyspark.sql.types import *
from IPython.display import display

sc = SparkContext(appName="CarCSV")
sqlContext = SQLContext(sc)

schema = StructType([StructField("year", IntegerType(), False),
                     StructField("make", StringType(), False),

mamonu

from bitarray import bitarray
import mmh3

class BloomFilter:
    
    def __init__(self, size, hash_count):
        self.size = size
        self.hash_count = hash_count
        self.bit_array = bitarray(size)
        self.bit_array.setall(0)

mamonu

This gist explains how a graph database can help for HR analytics. There are two files included:
- load the data.cql: this file contains the cypher statements that load the data into neo4j
- query the data.cql: this file has some sample queries that serve to demonstrate some of the concepts.
Hope this is useful.

Rik

mamonu

Using Git

Global Settings

Related Setup: https://gist.github.com/hofmannsven/6814278

Related Pro Tips: https://ochronus.com/git-tips-from-the-trenches/

mamonu

/*
This example uses Scala.  Please see the MLlib documentation for a Java example.

Try running this code in the Spark shell.  It may produce different topics each time (since LDA includes some randomization), but it should give topics similar to those listed above.

This example is paired with a blog post on LDA in Spark: http://databricks.com/blog
Spark: http://spark.apache.org/

also use.....
https://github.com/databricks/spark-csv

mamonu

Project Management

In my optimization class last semester we briefly talked about project management, where there is a set of activities with given durations and some activities need to be completed before other activities can begin. We were taught to explore the management of the project’s timeline in Excel, which was tedious and prone to errors due to its manual process.

---

mamonu

import akka.actor.{ Actor, Props }

object $NAME$ {
  def props: Props = Props(new $NAME$)
}

class $NAME$ extends Actor {
  override def receive = ???
}

mamonu

Taken from https://web.archive.org/web/20130517134339/http://bulba.sdsu.edu/jeanette/thesis/PennTags.html (since the source document now appears to be offline)

Penn Treebank II Tags

Note: This information comes from "Bracketing Guidelines for Treebank II Style Penn Treebank Project" - part of the documentation that comes with the Penn Treebank.

Contents:

Bracket Labels
Clause Level

mamonu

Advanced Functional Programming with Scala - Notes

Copyright © 2017 Fantasyland Institute of Learning. All rights reserved.

1. Mastering Functions

A function is a mapping from one set, called a domain, to another set, called the codomain. A function associates every element in the domain with exactly one element in the codomain. In Scala, both domain and codomain are types.

val square : Int => Int = x => x * x

mamonu

import sys

import numpy
from nltk.cluster import KMeansClusterer, GAAClusterer, euclidean_distance
import nltk.corpus
from nltk import decorators
import nltk.stem

stemmer_func = nltk.stem.EnglishStemmer().stem
stopwords = set(nltk.corpus.stopwords.words('english'))