forked from vkhakham/k-segment
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do_kmeans.py
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/
do_kmeans.py
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#!/usr/bin/env python
__author__ = 'Ahmad'
import sys
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
import argparse
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('-k', '--num_means', default=2, help="k for k-means")
parser.add_argument('-i', '--input', default="2c300k_dataset.txt", help="input txt file")
parser.add_argument('-v', '--verbose', default=3, help="verbose printing. 1: header, 2: cost, 3: centers, 6: points")
args = parser.parse_args()
return args
def load_test(f):
p = np.loadtxt(f)
w = np.ones(p.shape[0])
return p, w
def do_kmeans(k, datafile, n_init=10, plot=True, v=1):
p, w = load_test(datafile)
p = np.array(p, dtype='float64')
alg = KMeans(n_clusters=k, n_init=n_init).fit(p)
means = alg.cluster_centers_
cost = alg.inertia_
if v>0:
print ("K-means using sklearn for ", datafile, ", with k=", k, ".")
if v>1: print ("reference cost: ", cost)
if v>2: print ("centers: ", means)
if v>3: print ("centers str: ", str(means).replace("\n", ";"))
if v>5: print ("points: ", p)
if plot:
plt.plot(p[:,0], p[:,1],'go')
plt.plot(means[:,0], means[:,1],'ro')
plt.show()
return cost, means, p.shape[0]
def main(argv):
args = get_args()
k = int(args.num_means)
datafile = args.input
prnt = int(args.verbose)
do_kmeans(k, datafile, v=prnt)
if __name__ == "__main__":
main(sys.argv)