import proclus as prc
import plotter
import arffreader as ar
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats.stats import pearsonr
import adjrand
# Test to see if there is a correlation between the
# proclus objective function and the adjusted rand index
X, sup = ar.readarff("data/highdataproclus.arff")
NRUNS = 15
objs = np.zeros(NRUNS) # objective function results
adjs = np.zeros(NRUNS) # adjusted rand index results
print "Beginning runs..."
for i in xrange(NRUNS):
rseed = np.random.randint(low=0, high=1239831)
print ">>> Run %d/%d using seed %d" % (i + 1, NRUNS, rseed)
M, D, A = prc.proclus(X, k=7, l=2, seed=rseed)
objs[i] = prc.evaluateClusters(X, A, D, M)
adjs[i] = adjrand.computeAdjustedRandIndex(A, sup)
print "Finished runs..."
sidx = np.argsort(objs)
import proclus as prc
import plotter
import arffreader as ar
import numpy as np
import adjrand
X, sup = ar.readarff("data/simple.arff")
Dims = [0,1]
# plotter.plotDataset(X, D = Dims) # plot 0-1 dimensions
R = 1 # toggle run proclus
RS = 0 # toggle use random seed
if R: # run proclus
rseed = 902884
if RS:
rseed = np.random.randint(low = 0, high = 1239831)
print "Using seed %d" % rseed
M, D, A = prc.proclus(X, k = 3, l = 2, seed = rseed)
print "Accuracy: %.4f" % prc.computeBasicAccuracy(A, sup)
print "Adjusted rand index: %.4f" % adjrand.computeAdjustedRandIndex(A, sup)
plotter.plotClustering(X, M, A, D = Dims)
import proclus as prc
import plotter
import arffreader as ar
import numpy as np
import adjrand
import batalgo
X, sup = ar.readarff("data/D20.arff")
Dims = [1,2]
# plotter.plotDataset(X, D = Dims)
R = 1 # toggle run proclus
RS = 0 # toggle use random seed
a = 0.5124
if R: # run proclus
rseed = 1132743
if RS:
rseed = np.random.randint(low = 0, high = 1239831)
print "Using seed %d" % rseed
M, D, A = prc.proclus(X, k = 7, l = 2, seed = rseed)
# print "Accuracy: %.4f" % prc.computeBasicAccuracy(A, sup)
print "Adjusted rand index: %.4f \n" % adjrand.computeAdjustedRandIndex(A, sup)
plotter.plotClustering(X, M, A, D = Dims)
print "Centroid points given by BAT algorithm:"
batalgo.batalgo(rseed, 7)
M, D, A = prc.batproclus(X, k = 7, l = 2, seed = rseed)
# print "Accuracy: %.4f" % prc.computeBasicAccuracy(A, sup)
print "Adjusted rand index: %.4f \n" % (a)
import proclus as prc
import plotter
import arffreader as ar
import numpy as np
import adjrand
X, sup = ar.readarff("data/D05.arff")
Dims = [0, 1]
# plotter.plotDataset(X, D = Dims) # plot 0-1 dimensions
R = 1 # toggle run proclus
RS = 0 # toggle use random seed
if R: # run proclus
rseed = 902884
if RS:
rseed = np.random.randint(low=0, high=1239831)
print "Using seed %d" % rseed
M, D, A = prc.proclus(X, k=3, l=2, seed=rseed)
# print "Accuracy: %.4f" % prc.computeBasicAccuracy(A, sup)
print "Adjusted rand index: %.4f" % adjrand.computeAdjustedRandIndex(
A, sup)
plotter.plotClustering(X, M, A, D=Dims)