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Based on the EU funded SPIN! project code base of methods to discover spatial clusters of point patterns. Includes GAM/K, Knox, KNN, Larsen, Mantel, Besag and Newell and Fotheringham and Zhan methods. Reimplemented to make use of the GeoTools library
fftknn learns to recognize any sound, vowel or pitch you teach it and associate it with a MIDI signal. Later, (in real time) when it hears similar sounds it will trigger the learned MIDI signals. For example, fftknn can be used to trigger different synths depending on the vowel a singer sings into a mic. It could also be used to trigger different drums or samples based on different similar sounding vocal cues. requires: python numpy pyjack Technically speaking: fftknn continuously applies an fft
OverviewThe project contains implementation of prediction algorithms that when combined together acheive a 0.8988 RMSE score on the probe set. Please notice that In order to run the classes you'll need a 64bit OS with at least 4GB RAM (we used windows XP 64bit) and a 64bit JDK/JRE 1.6 or above (we used JDK 1.6 64bit). In addition, we recommend using eclipse for running/viewing the code (we used eclipse 3.4). For more information regarding installation and usage, see the wiki pages.
The goal of this project is to implement an efficient implementation of the k-NN algorithm on IBM's Cell processor. The k-nearest neighbor algorithm (k-NN) is a simple algorithm for machine learning. The algorithm projects objects to a multidimensional feature space. It then classifies objects based on the closest (according to a distance metric) training examples in the feature space. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most c