Difference between revisions of "VS265: Homework assignments"

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==== Lab #3, due Thursday, October 9 at beginning of class ====
+
==== 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


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