Makematics: Principal Component Analysis

PCA is for: Determining meaningful differences between sets Evaluating covariance between sets of data Reducing data to its most important axes (principal components). Allowing simplified reconstitution of data Principal Component Analysis compares a set against its own mean to normalize all data between -1 and 1. Then it compares this set to another set to…

Makematics: Support Vector Machines

SVM: Using a set of training images (or sounds, or words, etc) for computer recognition and categorization I originally tried this code out as a music recognizer. The plan was to pull out the frequencies (which turned out to be very difficult on anything other than a .wav) and the beat (using FFT) and then…

Makematics: Sequence Alignment

Via wikipedia: In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences. Aligned sequences of nucleotide or amino acid residues are typically represented as rows within a matrix. Gaps…

Makematics final: Diatom Recognizer

An interactive genus identifier to use on the beautiful and unique class of micro-organisms summarized under the name “diatoms”. These tiny creatures fix staggeringly large amounts of CO2 in the ocean, and are a key player in the global carbon cycle. Invisible to the naked eye, some are as small as 3 microns across. Diatoms…