University of Illinois, Urbana Champaign

CS 441

Numpy
February 1, 2022
1 * Prerequisites
You should familiarize yourself with the numpy.ndarray class of python’s numpy library.
You should be able to answer the following questions before starting this assignment. Let’s assume
a is a numpy array. * What is an array’s shape (e.g., what is the meaning of a.shape)?
* What is numpy’s reshaping operation? How much computational over-head would it induce?
* What is numpy’s transpose operation, and how it is different from reshaping? Does it
cause computation overhead? * What is the meaning of the commands a.reshape(-1, 1) and
a.reshape(-1)? * Would happens to the variable a after we call b = a.reshape(-1)? Does any
of a’s attributes change? * How do assignments in python and numpy work in general? * Does the
b=a statement use copying by value? Or is it copying by reference? * Is the answer to the previous
question change depending on whether a is a numpy array or a scalar value?
You can answer all of these questions by
1. Reading numpy's documentation from https://numpy.org/doc/stable/.
2. Making trials using dummy variables.
2 *Learning Objectives
This assignment’s main objective is for you to learn how to utilize matrix operations, and avoid
manual calculations as much as possible.
This objective will have the following benefits: 1. Effciency and Scalability: Utilizing matrix
operations will make your code way more effcient for two reasons:
1.1. **Single Processor Speed-Ups**: Using matrix operations can help your processor plan much
1.2. **Multi-Processor Speed-Ups**: Matrix operations are extremely parallelizable. This has a
Furthermore, you could easily use GPU acceleration for the same matrix operations if necessa
1.3. **Spending Less Time Executing High-Level Language Commands and Out-sourcing the Heavy-lif
2. Portabiliy: This will make your code smaller, more understandable, and therefore less prone
to have bugs.