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# Coding the Matrix

Lab: Transformations in 2D geometry

cancer_data.pyThe procedure returns a pair (A, b) consisting of a P-by-D matrix A and a P-vector b where P is a set of patient IDs. This differs slightly from the spec given in this chapter of Edition 0.

Lab: Comparing voting records using dot-product

test_vec.pyThis file is not needed since its doctests have been added to vec.py.

cracking_rand.pySome code for cracking Pythons rand function

Matrix-vector and vector-matrix multiplication in terms of linear combinations

Dictionary-based representations of vectors

cancer_data.pyThe procedure returns a pair (A, b) consisting of a P-by-D matrix A and a P-vector b where P is a set of patient IDs. This differs slightly from the spec given in this chapter of Editions 0 and 1.

validate.dataFile with data on which to test your classifier

This page links to resources forCoding the Matrix. The links are organized by chapter and section; as a consequence, there might be multiple links to the same resource.

Lab: Pythonmodules and control structuresand inverse index

Projection orthogonal to multiple vectors

train.dataFile with data on which to train your classifier

Using Gaussian elimination for other problems

Please do not make these materials publicly available elsewhere, and do not make your solutions public. Dont deprive others of the pleasure and challenge of solving the problems, and dont place temptation in the path of others.

Lab: Learning through linear programming

validate.dataFile with data on which to test your classifier

mat_sparsity.pyThis file contains some simple doctests that should take very little time if your implementations of transpose, matrix-vector multiplication, vector-matrix multiplication, and matrix-matrix multiplication exploit sparsity.

mat.pyThe tests formerly located in test_mat.py have been added to this file as doctest.

The Function (and other mathematical and computational preliminaries)

Lab: Using wavelets for compression

vec.pyThe tests formerly located in test_mat.py have been added to this file as doctests.

UN_voting_data.txtA different dataset that can be used for the same purpose

The following .data files were derived from data obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg.

train.dataFile with data on which to train your classifier

US_Senate_voting_data_109.txtThis is the same as voting_record_dump109.txt, just with a more informative name

Solving a triangular system of linear equations