# VS265: Homework assignments: Difference between revisions

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==== Lab #3, due Thursday, October | ==== Lab #3, due Thursday, October 21 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 19:22, 14 October 2014

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

**Submission instructions**:
Only paper copies of the homework will be accepted. Solutions are due at the start of the class. Please place them on the speaker's desk at the front of the class.

# 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 21 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