def kmeans(self, pixelcluster=True):
'''
calculates kmeans centers for the cob from the centers of the kernels it contains
'''
if pixelcluster == True:
pixellist = []
for kernel in self.kernellist:
pixellist.extend(kernel.pixellist)
kmeans = Kernel.KMeans(n_clusters=2).fit(pixellist)
else:
kmeans = Kernel.KMeans(n_clusters=2).fit(self.kernelcenters)
meanc1 = kmeans.cluster_centers_[0].tolist()
meanc2 = kmeans.cluster_centers_[1].tolist()
sizec1 = 0
sizec2 = 0
kernelclusters = []
for kernel in self.kernellist:
for cluster in kernel.clusters:
kernelclusters.append(cluster)
for label, cluster in zip(kmeans.labels_, kernelclusters):
if label == 0:
sizec1 += cluster[0]
elif label == 1:
sizec2 += cluster[0]
self.clusters.append([sizec1, meanc1])
self.clusters.append([sizec2, meanc2])
def kmeans(self):
'''
calculates kmeans centers for the repline from the centers of the cobs it contains
'''
if len(self.coblist) > 1:
allkernelsizes = []
self.allkernelclusters = []
for cob in self.coblist:
for kernel in cob.kernellist:
for cluster in kernel.clusters:
allkernelsizes.append(cluster[0])
self.allkernelclusters.append(cluster[1])
kmeans = Kernel.KMeans(n_clusters=2).fit(self.allkernelclusters)
meanc1 = kmeans.cluster_centers_[0].tolist()
meanc2 = kmeans.cluster_centers_[1].tolist()
sizec1 = 0
sizec2 = 0
for label, size in zip(kmeans.labels_, allkernelsizes):
if label == 0:
sizec1 += size
elif label == 1:
sizec2 += size
self.clusters.append([sizec1, meanc1])
self.clusters.append([sizec2, meanc2])
self.checkdistance()
else:
self.segregating = self.coblist[0].segregating
self.clusters = self.coblist[0].clusters
