VS265: Homework assignments Fall2010
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.
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/"
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.
Lab #1, due Thursday, September 9th at beginning of class
for Python: either ...
In : import scipy.io In : d = scipy.io.loadmat("data.mat") In : X,O = d['X'],d['O']
or use data.npz
In : import numpy as np In : d = np.load('data.npz') In : X,O = d['X'],d['O']
Lab #2, due Tuesday, Sep 21 at beginning of class
For Python you can use apples-oranges.npz
In : import numpy as np In : d = np.load('apples-oranges.npz') In : d.keys() Out: ['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
Lab #4, due Tuesday, Oct 5 at beginning of class
- 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
Lab #6 (Due Tuesday, Oct 26 at beginning of class)
- hopnet.py - python version of the above code as one file (with run, genpat, and corrupt methods)
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