def main():
data = readfile('data.txt')
plot_data(data) # show data
# metoda shlukove hladiny
logging.info('metoda shlukove hladiny')
cluster()
# metoda retezove mapy
logging.info('metoda retezove mapy')
chmap()
# metoda maximin
logging.info('metoda maximin')
maximin()
# nerovnomerne binarni deleni
logging.info('nerovnomerne binarni deleni')
unebin()
# kmeans
logging.info('kmeans')
kmeans()
# bayesuv klasifikator
logging.info('bayesuv klasifikator')
bayes()
# klasifikator podle minimalni vzdalenosti
logging.info('klasifikator podle minimalni vzdalenosti')
mindist()
# klasifikator podle k-nejblizsiho souseda
logging.info('klasifikator podle k-nejblizsiho souseda')
nearneigh()
# klasifikator s linearnimi diskriminacnimi funkcemi
logging.info('klasifikator s linearnimi diskriminacnimi funkcemi')
lindisc()
def main():
data = readfile('data.txt')
# data = [(-3, 1), (1, 1), (-2, 0), (3, -3), (1, 2), (-2, -1)]
t0 = dt.datetime.now()
lvls = cluster_levels(data, 1.9)
t1 = dt.datetime.now()
print('cas', t1 - t0)
print_clusterlvls(lvls)
def main():
data = readfile('data.txt')
data = kmeans(data, 3)
ross = rossenblatt(data)
plot_kmeans(ross)
const_incr = constant_increment(data, 0.5)
plot_kmeans(const_incr)
mod_const_incr = constant_increment(data, 0.5)
plot_kmeans(mod_const_incr)
def main():
# data = [(-3, 0), (3, 2), (-2, 0), (3, 3), (2, 2), (3, -2), (4, -2), (3, -3)]
data = readfile('data.txt')
logging.info('k-means')
means = kmeans(data, 3)
j_kmeans = sum(criterion(means).values())
plot_kmeans(means)
logging.info('Unequal binary')
dist = unequal_binary(data)
plot_kmeans(dist)
j_binary = sum(criterion(dist).values())
logging.info('J kmeans: {}, J binary: {}'.format(j_kmeans, j_binary))
def main():
data = readfile('data.txt')
processed_data1 = nearest_neighbour(data, 3)
processed_data2 = knearest_neighbour(data, 3)
plot_kmeans(processed_data1)
plot_kmeans(processed_data2)
def main():
data = readfile('data.txt')
baye = bayes(data, 3, step=0.4)
plot_kmeans(baye)
def main():
data = readfile('data.txt')
chmap = chain_map(data, 9)
plot_chainmap(chmap)
def main():
data = readfile('data.txt')
processed_data = minimal_distance(data, 3)
plot_kmeans(processed_data)
def main():
data = readfile('data.txt')
lvls = cluster_levels(data, 1.9)
logger.info('Aglomerativni metodou byly nalezeny: {} tridy'.format(lvls))
def main():
data = readfile('data.txt')
# data = [(2, -3), (3, 3), (2, 2), (-3, 1), (-1, 0), (-3, -2), (1, -2), (3, 2)]
no_of_clusters = maximin(data, 0.3)
print('MAXIMIN found {} clusters'.format(no_of_clusters))