def trykmeans(examples, numClusters, numTrials, verbose=False):
"""Calls kmeans numTrials times and returns the result with the
lowest dissimilarity"""
best = kmeans(examples, numClusters, verbose)
minDissimilarity = cluster.dissimilarity(best)
trial = 1
while trial < numTrials:
try:
clusters = kmeans(examples, numClusters, verbose)
except ValueError:
continue # If failed, try again
currDissimilarity = cluster.dissimilarity(clusters)
if currDissimilarity < minDissimilarity:
best = clusters
minDissimilarity = currDissimilarity
trial += 1
return best
def contrivedTest2(numTrials, k, verbose = False):
xMean = 3
xSD = 1
yMean = 5
ySD = 1
n = 8
d1Samples = genDistribution(xMean,xSD, yMean, ySD, n, 'A')
plotSamples(d1Samples, 'k^')
d2Samples = genDistribution(xMean+3,xSD,yMean, ySD, n, 'B')
plotSamples(d2Samples, 'ko')
d3Samples = genDistribution(xMean, xSD, yMean+3, ySD, n, 'C')
plotSamples(d3Samples, 'kx')
clusters = trykmeans(d1Samples + d2Samples + d3Samples, k, numTrials, verbose)
pylab.ylim(0,11)
print('Final result has dissimilarity',round(cluster.dissimilarity(clusters), 3))
for c in clusters:
print(" ",c)
