Stream PRAM a Comp. Sci. research paper by Darrell Ulm @ PubZone

Pubzone reference for Comp. Sci. paper by Darrell Ulm "Stream PRAM," at the International Parallel and Distributed Processing Symposium.

Stream PRAM a Comp. Sci. research paper by Darrell Ulm @ PubZone

Pubzone reference for Comp. Sci. paper by Darrell Ulm "Stream PRAM," at the International Parallel and Distributed Processing Symposium.

The Github code repo. for the Knapsack approximation algorithms is here, and it includes a Scala solution. The work on a Java version is in progress at time of this writing.

Below we have the code that computes the solution that fits within the knapsack W for a set of items each with it's own weight and profit value. We look to maximize the final sum of selected items profits while not exceeding the total possible weight, W.

First we import some spark libraries into Python.

# Knapsack 0-1 function weights, values and size-capacity. from pyspark.sql import SparkSession from pyspark.sql.functions import lit from pyspark.sql.functions import col from pyspark.sql.functions import sum

Now define the function, which will take a Spark Dataframe w…

Once the IDs are added, a DataFrame join will merge all the columns into one Dataframe.

# For two Dataframes that have the same number of rows, merge all columns, row by row.

# Get the function monotonically_increasing_id so we can assign ids to each row, when the # Dataframes have the same number of rows. from pyspark.sql.functions import monotonically_increasing_id

#Create some test data with 3 and 4 columns. df1 = sqlContext.createDataFrame([("foo", "bar","too","aaa"), ("bar", "bar","aa…