resources.codingthematrix.comCoding the Matrix resources

Coding the Matrix Resources This page links to resources for Coding the Matrix . The links are organized by chapter and section; as a consequence, there might be multiple links to the same resource. Please do not make these materials publicly available elsewhere, and do not make your solutions public. Don't deprive others of the pleasure and challenge of solving the problems, and don't place temptation in the path of others. The Function (and other mathematical and computational preliminaries) Lab: Python—modules and control structures—and inverse index dictutil.py stories_small.txt stories_big.txt The Field Playing with ℂ plotting.py image.py img01.png png.py Module used by image.py Playing with GF (2) GF2.py The Vector Dictionary-based representations of vectors vecutil.py Our implementation of Vec vecutil.py Solving a triangular system of linear equations triangular.py Lab: Comparing voting records using dot-product voting_record_dump109.txt US_Senate_voting_data_109.txt This is the same as voting_record_dump109.txt, just with a more informative name UN_voting_data.txt A different dataset that can be used for the same purpose Problems vec.py The tests formerly located in test_mat.py have been added to this file as doctests. test_vec.py This file is not needed since its doctests have been added to vec.py. The Vector Space The Matrix What is a matrix? matutil.py Matrix-vector and vector-matrix multiplication in terms of linear combinations solver.py Lab: Error-correcting codes bitutil.py Lab: Transformations in 2D geometry image_mat_util.py which uses... png.py Problems mat.py The tests formerly located in test_mat.py have been added to this file as doctest. mat_sparsity.py This 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. UN_voting_data.txt The Basis Perspective rendering plotting.py vecutil.py Lab: Perspective rectification board.png image_mat_util.py which uses... png.py cit.png Problems matutil.py solver.py Dimension Dimension and rank independence.py solver.py The annihilator solver.py Problems solver.py triangular.py Gaussian elimination Gaussian elimination over GF (2) gaussian_examples.py Using Gaussian elimination for other problems cracking_rand.py Some code for cracking Python's rand function echelon.py Lab: Threshold Secret-Sharing bitutil.py Lab: Factoring integers factoring_support.py The Inner Product Orthogonality orthogonalization.py Lab: machine learning The following .data files were derived from data obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg. train.data File with data on which to train your classifier validate.data File with data on which to test your classifier cancer_data.py The 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. Orthogonalization Projection orthogonal to multiple vectors orthogonalization.py Problems orthonormalization.py QR.py read_data.py age-height.txt Special Bases Discrete Fourier transform fft.py Lab: Using wavelets for compression image.py png.py used by image.py flag.png Dali.png The Singular Value Decomposition Closest dimension- k vector space svd.py game_results.csv Lab: Digits mnist_loader.py mnist-images.dat mnist-labels.dat Lab: Eigenfaces faces.zip unclassified.zip image.py png.py used by image.py eigenfaces.py The Eigenvector Lab: Pagerank pagerank.py pagerank_test.py indexindex.txt titles.txt links.bin inverseindex The Linear Program The simplex algorithm simplex.py Lab: Learning through linear programming train.data File with data on which to train your classifier validate.data File with data on which to test your classifier cancer_data.py The 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....