# VS265: Homework assignments Fall2010: Difference between revisions

No edit summary |
No edit summary |
||

(6 intermediate revisions by 2 users not shown) | |||

Line 30: | Line 30: | ||

In [3]: X,O = d['X'],d['O'] | In [3]: X,O = d['X'],d['O'] | ||

* Solutions: [http://redwood.berkeley.edu/vs265/soln1-2010.pdf pdf] [http://redwood.berkeley.edu/vs265/lab1.txt lab1.py] [http://redwood.berkeley.edu/vs265/lab1-08.m lab1.m (from '08)] | * Solutions: <!-- [http://redwood.berkeley.edu/vs265/soln1-2010.pdf pdf] [http://redwood.berkeley.edu/vs265/lab1.txt lab1.py] [http://redwood.berkeley.edu/vs265/lab1-08.m lab1.m (from '08)] --> | ||

==== Lab #2, due Tuesday, Sep 21 at beginning of class ==== | ==== Lab #2, due Tuesday, Sep 21 at beginning of class ==== | ||

Line 47: | Line 47: | ||

Out[3]: ['oranges2', 'apples2', 'apples', 'oranges'] | Out[3]: ['oranges2', 'apples2', 'apples', 'oranges'] | ||

<!-- | * Solutions: <!-- [http://redwood.berkeley.edu/vs265/sols/soln2-08.pdf pdf] (ignore self grading instructions) [http://redwood.berkeley.edu/vs265/sols/soln2.zip zip'd Matlab code] --> | ||

==== Lab #3, due Tuesday, September 28 at beginning of class ==== | ==== Lab #3, due Tuesday, September 28 at beginning of class ==== | ||

Line 63: | Line 63: | ||

* [http://redwood.berkeley.edu/vs265/lab3/eigmovie.txt eigmovie.py] | * [http://redwood.berkeley.edu/vs265/lab3/eigmovie.txt eigmovie.py] | ||

<!-- | * Solutions: <!-- [http://redwood.berkeley.edu/vs265/sols/hw3-08.pdf pdf] [http://redwood.berkeley.edu/vs265/sols/hw3-08.zip zip'd Matlab code]. For self-grading, each question is worth 3 points. --> | ||

==== Lab #4, due Tuesday, Oct 5 at beginning of class ==== | ==== Lab #4, due Tuesday, Oct 5 at beginning of class ==== | ||

Line 70: | Line 70: | ||

* [http://redwood.berkeley.edu/vs265/sparsenet_class_scripts.zip sparsenet scripts (zip)] | * [http://redwood.berkeley.edu/vs265/sparsenet_class_scripts.zip sparsenet scripts (zip)] | ||

* You will also need the following set of whitened natural movie images: [https://redwood.berkeley.edu/bruno/sparsenet/IMAGES.mat IMAGES.mat] | * You will also need the following set of whitened natural movie images: [https://redwood.berkeley.edu/bruno/sparsenet/IMAGES.mat IMAGES.mat] | ||

<!-- | * Solutions: <!-- [http://redwood.berkeley.edu/vs265/sols/sol4-08.pdf pdf] --> | ||

==== Lab #5 due Tuesday, Oct 12 at beginning of class ==== | ==== Lab #5 due Tuesday, Oct 12 at beginning of class ==== | ||

Line 80: | Line 80: | ||

* [http://redwood.berkeley.edu/vs265/lab5/kohonen.txt kohonen.py] | * [http://redwood.berkeley.edu/vs265/lab5/kohonen.txt kohonen.py] | ||

* [http://redwood.berkeley.edu/vs265/lab5/showrfs.txt showrfs.py] | * [http://redwood.berkeley.edu/vs265/lab5/showrfs.txt showrfs.py] | ||

<!-- | * Solutions: <!-- [http://redwood.berkeley.edu/vs265/sols/soln5-08.pdf pdf] [http://redwood.berkeley.edu/vs265/sols/lab5.zip zip'd Matlab code] --> | ||

==== Lab #6 (Due Tuesday, Oct 26 at beginning of class) ==== | ==== Lab #6 (Due Tuesday, Oct 26 at beginning of class) ==== | ||

Line 100: | Line 100: | ||

face,hi,X = [p[k].reshape(10,10).T.reshape(100,1) for k in 'face','hi','X'] | 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 | # line above converts Fortran to C ordering | ||

<!-- | * Solutions: <!-- [http://redwood.berkeley.edu/vs265/sols/sol6-08.pdf pdf] --> | ||

==== Lab #7 (Due Tuesday, Nov. 2) ==== | ==== Lab #7 (Due Tuesday, Nov. 2) ==== | ||

Line 122: | Line 122: | ||

* [http://redwood.berkeley.edu/amir/vs298/boltz/draw.m draw.m] | * [http://redwood.berkeley.edu/amir/vs298/boltz/draw.m draw.m] | ||

* [http://redwood.berkeley.edu/amir/vs298/boltz/sigmoid.m sigmoid.m] | * [http://redwood.berkeley.edu/amir/vs298/boltz/sigmoid.m sigmoid.m] | ||

''Python code:'' | |||

* [http://redwood.berkeley.edu/vs265/lab8/boltz.txt boltz.py] - python version of the above code. | |||

==== Lab #9 (Due Tuesday, Nov. 23) ==== | |||

* [http://redwood.berkeley.edu/vs265/lab9.pdf lab9.pdf] | |||

* [http://redwood.berkeley.edu/vs265/ica/ica.m ica.m] | |||

* [http://redwood.berkeley.edu/vs265/ica/test_data.mat test_data.mat] | |||

* [http://redwood.berkeley.edu/vs265/ica/ica2.m ica2.m] | |||

* [http://redwood.berkeley.edu/vs265/ica/extract_patches.m extract_patches.m] | |||

* [http://redwood.berkeley.edu/vs265/ica/showbfs.m showbfs.m] | |||

* [http://redwood.berkeley.edu/vs265/ica/IMAGES.mat IMAGES.mat] | |||

<!-- * Solutions: Thanks to Chung-Hay from our 2006 class [http://redwood.berkeley.edu/amir/vs298/soln8.pdf pdf]--> |

## Latest revision as of 23:41, 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 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.

# 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:

#### Lab #2, due Tuesday, Sep 21 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']

- Solutions:

#### 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:

#### 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:

#### Lab #5 due Tuesday, Oct 12 at beginning of class

*Python code:*

- kohonen.py
- showrfs.py
- Solutions:

#### 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:

#### 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.