{"push_id":884992750,"size":1,"distinct_size":1,"ref":"refs/heads/master","head":"a8f1976d6448947922be6765a2eca4ecef74d629","before":"cd595dbd3442787afa326b7d1663544a5efb1be1","commits":[{"sha":"a8f1976d6448947922be6765a2eca4ecef74d629","author":{"email":"96786206f5d6036759c22f50e857eb7ab446c090@clemson.edu","name":"b-g-goodell"},"message":"Created main.py\n\nRunning \"python main.py\" will ping the e-booksdirectory.com database for all math textbooks, pull the (first layer of their) download links, search for all PDF urls, pull all PDF files to a local copy, then run optical character recognition on each textbook to generate a text file. This text file can be viewed as a vector in high dimensional Euclidean space, wherein each standard basis vector corresponds to a unique human word, and wherein each vector coordinate is the relative frequency of the associated word.\r\n\r\nTexts may then be regarded as admitting a distance between one another (in, say, the p-norm on sequence spaces, \\ell_p). Here, we choose p=1 and consider the 1-norm between texts. We use k-means clustering to find 7 clusters of texts which, hopefully, correspond to varying difficulties of math texts.","distinct":true,"url":"https://api.github.com/repos/b-g-goodell/book-clustering/commits/a8f1976d6448947922be6765a2eca4ecef74d629"}]}