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()
