# VS265: Homework assignments: Difference between revisions

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==== Lab #3, due Thursday, October | ==== Lab #3, due Thursday, October 16 at beginning of class ==== | ||

* [http://redwood.berkeley.edu/vs265/lab3.pdf lab3.pdf] | * [http://redwood.berkeley.edu/vs265/lab3.pdf lab3.pdf] | ||

Matlab code are as separate files below. | Matlab code are as separate files below. |

## Revision as of 18:18, 5 October 2014

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:

vs265 AT rctn.org

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

Student version of Matlab ($50) may be obtained here.

There is an excellent guide to Matlab by Kevin Murphy on the web: 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.

Also, a great starting point for all scientific python is using Anaconda [1]

# Assignments

#### Lab #1, due Tuesday, September 16 at beginning of class

#### Lab #2, due Tuesday, September 23 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 Thursday, October 16 at beginning of class

Matlab code are as separate files below.

Data

*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 October 9th at beginning fo class

Matlab code and data for homework

*Python code:*

- hopnet.py - python version of the above code as one file (with run, genpat, and corrupt methods)
- patterns.npz

p = np.load('patterns.npz') face,hi,X = p['face'], p['hi'], p['X']

# if you load patterns.mat, use: p = scipy.io.loadmat("patterns.mat") face,hi,X = [p[k].reshape(10,10).T.reshape(100,1) for k in 'face','hi','X'] # line above converts Fortran to C ordering