def test(): algorithm = Kohonen([10, 10], 2, gaussian_kernel, verbose=False) data = dataset.load("../../datasets/4gaussians1k.data") algorithm.load(data) print "Kohonen finished in %i iterations" % algorithm.learn() print algorithm.classify(data.data()[0]) print algorithm.class_probability(data.data()[0], 1) algorithm.draw_all()
def test(): data = dataset.load("../../datasets/4gaussians1k.data") classifier = Kmeans(4) classifier.load(data) print "Kmeans finished in %i iterations over the dataset" % \ classifier.learn() classifier.draw_all() pylab.show()
def test(): algorithm = kohonen.Kohonen([10, 10], 2, kohonen.gaussian_kernel, verbose=False) data = dataset.load("../../datasets/2gaussians1k.data") algorithm.load(data) algorithm.learn() #algorithm.draw_all() clusters = algorithm.get_clusters() lvq = LVQ(clusters, dimension=len(data[0])) lvq.load(data) lvq.learn() lvq.draw_all()
#!/usr/bin/env python # encoding: utf-8 """ kfold.py Created by Julien Lauron on @DATE@ Copyright(c) All rights reserved. """ import algorithm as lvq from algorithms.kohonen import algorithm as kohonen from base.crossvalidation import * from base import dataset if __name__ == "__main__": data = dataset.load("../../datasets/2gaussians1k.data") algorithm = kohonen.Kohonen([10, 10], 2, kohonen.gaussian_kernel, verbose=False) algorithm.load(data) algorithm.learn() kfold = KFold(10) kfold.load_dataset(data) print "KFold on LVQ" lvqalgo = lvq.LVQ(algorithm.get_clusters()) kfold.estimate(lvqalgo) kfold.print_results()