# VS265: Homework assignments Fall2010: Difference between revisions

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==== Lab #6 (Due Tuesday, Oct 26 at beginning of class) ==== | ==== Lab #6 (Due Tuesday, Oct 26 at beginning of class) ==== | ||

* [http://redwood.berkeley.edu/vs265/lab6.pdf] | * [http://redwood.berkeley.edu/vs265/lab6.pdf lab6.pdf] | ||

* [http://redwood.berkeley.edu/vs265/lab6.zip zip'd Matlab files] | * [http://redwood.berkeley.edu/vs265/lab6.zip zip'd Matlab files] | ||

* [http://redwood.berkeley.edu/vs265/lab6/genpat.m genpat.m] | * [http://redwood.berkeley.edu/vs265/lab6/genpat.m genpat.m] |

## Revision as of 07:05, 14 October 2010

Students are encouraged to work in groups, but turn in assignments *individually*, listing the group members they worked with.

**Submission instructions**:
email both a PDF of your solutions as well as your code (.m or .py files) as attachments to:

rctn.org vs265 (vs265 should be out front)

You can hand in a paper copy of your solutions before class, but you still have to email your code to the address above before the assignment is due.

# Resources

## Matlab

Amir, the past GSI for the course says "There is a guide to Matlab on the web by Kevin Murphy which is really excellent. I think it would be great for the VS265 students: http://code.google.com/p/yagtom/"

## Python

Fernando Perez at the Brain Imaging Center has an *excellent* set of resources on Python for scientific computing. You will likely find the "Starter Kit" particularly useful.

Additionally, Josh Bloom (Astronomy) is teaching a Science Research Computing with Python course this semester (on Mondays 2-5pm in Hearst 310, Fall 2010, CCN 06180) which you might want to take. A Python Boot Camp kicked-off that class, and has a lot of accessible introductory material.

# Assignments

#### Lab #1, due Thursday, September 9th at beginning of class

*for Python: either ...*

In [1]: import scipy.io In [2]: d = scipy.io.loadmat("data.mat") In [3]: X,O = d['X'],d['O']

*or use data.npz*

In [1]: import numpy as np In [2]: d = np.load('data.npz') In [3]: X,O = d['X'],d['O']

- Solutions: pdf lab1.py lab1.m (from '08)

#### Lab #2, due Tuesday, Sep 21 at beginning of class

*For Python you can use apples-oranges.npz*

In [1]: import numpy as np In [2]: d = np.load('apples-oranges.npz') In [3]: d.keys() Out[3]: ['oranges2', 'apples2', 'apples', 'oranges']

#### Lab #3, due Tuesday, September 28 at beginning of class

Matlab code are as separate files below.

*For Python you can use *

- data2d.npz (see previous assignments above for how to read this in)
- faces2.npz
- hebb.py
- eigmovie.py

#### Lab #4, due Tuesday, Oct 5 at beginning of class

- lab4
- foldiak scripts (zip)
- sparsenet scripts (zip)
- You will also need the following set of whitened natural movie images: IMAGES.mat

#### Lab #5 due Tuesday, Oct 12 at beginning of class

*Python code:*