VS265: Homework assignments Fall2010: Difference between revisions
(No difference)

Revision as of 02:45, 28 August 2012
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 25pm in Hearst 310, Fall 2010, CCN 06180) which you might want to take. A Python Boot Camp kickedoff 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 applesoranges.npz
In [1]: import numpy as np In [2]: d = np.load('applesoranges.npz') In [3]: d.keys() Out[3]: ['oranges2', 'apples2', 'apples', 'oranges']
 Solutions: pdf (ignore self grading instructions) zip'd Matlab code
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
 Solutions: pdf zip'd Matlab code.
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
 Solutions: pdf
Lab #5 due Tuesday, Oct 12 at beginning of class
Python code:
 kohonen.py
 showrfs.py
 Solutions: pdf zip'd Matlab code
Lab #6 (Due Tuesday, Oct 26 at beginning of class)
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
 Solutions: pdf
Lab #7 (Due Tuesday, Nov. 2)
Lab #8 (Due Thursday, Nov. 11)
 lab8.pdf
 scribble.mat
 scribble_v6.mat (version 6)
 extract_patches.m
 prob.m
 show_patches.m
 boltz.m
 sample.m
 draw.m
 sigmoid.m
Python code:
 boltz.py  python version of the above code.