Z = set(Y)
M = [[z,Y.count(z)] for z in Z]
M.sort()
M.reverse()
X = [data[0] for data in M]
Y = [data[1] for data in M]
'''
#Y = [sum(Y[:i]) / float(len(gps_data)) for i in range(1,len(M) + 1,1)]
'''
plt.bar(X, Y, width=0.25)
plt.show()
'''
#pylab.plot(X,Y,'o')
#pylab.show()
labels, centers = sciencecluster.science_cluster(numpy.array(gps_data),
cutoff_distance=0.0000001)
save_gps_data(gps_data, labels)
'''
eps = 0.00001
min_samples = 15
metric = 'euclidean'
core_samples, labels = sklearn.cluster.dbscan(numpy.array(gps_data), metric=metric, eps=eps,min_samples=min_samples)
print labels[:100]
'''
'''
X = [data[0] for data in gps_data]
Y = [data[1] for data in gps_data]
ax1 = pylab.subplot(211)
ax2 = pylab.subplot(212)
pylab.sca(ax1)
for i in range(len(X)):
def science_cluster(gps_data,number,show = False):
labels,centers = sciencecluster.science_cluster(numpy.array(gps_data),num = number,cutoff_distance = 0.000005,experience = 0.000811,show = show) #0.0003 \\0.0001
return centers,labels
Z = set(Y) M = [[z,Y.count(z)] for z in Z] M.sort() M.reverse() X = [data[0] for data in M] Y = [data[1] for data in M] ''' #Y = [sum(Y[:i]) / float(len(gps_data)) for i in range(1,len(M) + 1,1)] ''' plt.bar(X, Y, width=0.25) plt.show() ''' #pylab.plot(X,Y,'o') #pylab.show() labels,centers = sciencecluster.science_cluster(numpy.array(gps_data),cutoff_distance = 0.0000001) save_gps_data(gps_data,labels) ''' eps = 0.00001 min_samples = 15 metric = 'euclidean' core_samples, labels = sklearn.cluster.dbscan(numpy.array(gps_data), metric=metric, eps=eps,min_samples=min_samples) print labels[:100] ''' ''' X = [data[0] for data in gps_data] Y = [data[1] for data in gps_data] ax1 = pylab.subplot(211) ax2 = pylab.subplot(212) pylab.sca(ax1) for i in range(len(X)):
