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kmeans.py
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kmeans.py
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# -*- coding: utf-8 -*-
from __future__ import division
__author__ = "Arunprasath Shankar"
__copyright__ = "Copyright 2012, Arunprasath Shankar"
__license__ = "GPL"
__version__ = "1.0.1"
__email__ = "axs918@case.edu"
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
from ordereddict import OrderedDict
from math import ceil
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d.axes3d as p3
from operator import itemgetter
def kmeans(data, k):
"""
* This def takes a array of integers and the number of clusters to create.:
* It returns a multidimensional array containing the original data organized
* in clusters.
*
* @param array data
* @param int k
*
* @return array
"""
cPositions = assign_initial_positions(data, k)
clusters = OrderedDict()
while True:
changes = kmeans_clustering(data, cPositions, clusters)
if not changes:
return kmeans_get_cluster_values(data, clusters)
cPositions = kmeans_recalculate_cpositions(data, cPositions, clusters)
def kmeans_clustering(data, cPositions, clusters):
"""
"""
nChanges = 0
for dataKey, value in enumerate(data):#.items():
minDistance = None
cluster = None
for k, position in cPositions.items():
dist = distance(value, position)
if None is minDistance or minDistance > dist:
minDistance = dist
cluster = k
if not clusters.has_key(dataKey) or clusters[dataKey] != cluster:
nChanges += 1
clusters[dataKey] = cluster
return nChanges
def kmeans_recalculate_cpositions(data, cPositions, clusters):
kValues = kmeans_get_cluster_values(data, clusters)
for k, position in cPositions.items():
if not kValues.has_key(k):
cPositions[k] = 0
else:
cPositions[k] = kmeans_avg(kValues[k])
#cPositions[k] = empty(kValues[k]) ? 0 : kmeans_avg(kValues[k])
return cPositions
def kmeans_get_cluster_values(data, clusters):
values = OrderedDict()
for dataKey, cluster in clusters.items():
if not values.has_key(cluster):
values[cluster] = []
values[cluster].append(data[dataKey])
return values
def kmeans_avg(values):
n = len(values)
total = sum(values)
if n == 0:
return 0
else:
return total / (n * 1.0)
def distance(v1, v2):
"""
* Calculates the distance (or similarity) between two values. The closer
* the return value is to ZERO, the more similar the two values are.
*
* @param int v1
* @param int v2
*
* @return int
"""
return abs(v1 - v2)
def assign_initial_positions(data, k):
"""
* Creates the initial positions for the given
* number of clusters and data.
* @param array data
* @param int k
*
* @return array
"""
small = min(data)
big = max(data)
num = ceil((abs(big - small) * 1.0) / k)
cPositions = OrderedDict()
while k > 0:
k -= 1
cPositions[k] = small + num * k
return cPositions
def readData(f, tf_idf_list):
f = f.readlines()
for item in f:
item = item.split()
tf_idf = item[1]
tf_idf_list.append(tf_idf)
return tf_idf_list
def printClusterData(f, la, range_estimator):
col = ['r', 'y', 'm', 'c', 'b', 'g', 'r', 'y', 'm', 'c']
ax.hold(True)
l = len(f)
x = range(l)
ax.set_ylim(-50, 350)
ax.set_xlim(-.1, 1.1)
#plt.xticks([.01,.02,.03,.04,.05,.1,.15,.2,.25,.3,.4,.5,1])
'''
fig = Figure(figsize=(9, 7))
canvas = FigureCanvas(fig)
ax = fig.add_subplot(111)
population = {}
for value in set(f):
population[value] = f.count(value)
ax.grid(True, linestyle='-', color='0.75')
ax.scatter(f,x, c=population.keys(), s=population.values(),marker='o', alpha=0.75)
ax.set_title("Scatter Plot for Word Clusters", fontsize=14)
ax.set_xlabel("Tf-Idf (Normalized)", fontsize=12)
ax.set_ylabel("Tf-Idf (Normalized)", fontsize=12)
canvas.print_figure('cluster_plot.png', dpi=700)
'''
#plt.grid(True, linestyle='-', color='0.75')
ax.scatter(f, x, s=30, c=col[i], marker='o', alpha=0.75)
piv = 0
cen = sum(f)/l
range_estimator.append([min(f), cen, max(f)])
print
for dd, ele in enumerate(f):
if (dd+1) < len(f) and ele < cen < f[dd+1]:
piv = dd
print piv
# centroids
ax.scatter(cen,piv, marker='o', s = 300, linewidths=1, c='w', alpha=0.60)
ax.scatter(cen,piv, marker='x', s = 300, linewidths=1, c='k', alpha=0.60 )
#plt.axvline(x=cen, ymin = piv, ymax=piv, linewidth=0.3, color='black')
ax.vlines(x=cen, ymin=-50, ymax=piv, color='k', linestyles='solid')
#plt.show()
fig = "cluster.png"
#plt.savefig(fig)
canvas.print_figure('cluster_plot.pdf', dpi=700)
if __name__ == '__main__':
tf_idf_list = []
f = open('out.txt','r')
x = readData(f, tf_idf_list)
x = [float(e) for e in x ]
la = len(x)
y = kmeans(x, 10)
fig = Figure(figsize=(9, 7))
canvas = FigureCanvas(fig)
ax = fig.add_subplot(111)
range_estimator = []
for i, f in y.iteritems():
print i, f
printClusterData(f, la, range_estimator)
#print range_estimator
ranges = sorted(range_estimator, key=itemgetter(1))
for range in ranges:
print range[0], range[1], range[2]