Difference between revisions of "VS265: Homework assignments"

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'''Submission instructions''':
 
'''Submission instructions''':
email both a PDF of your solutions as well as your code (.m or .py files) as attachments to:
+
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.
    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 =
 
= Resources =

Revision as of 18:34, 7 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 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