def train(histogram):
data, centroidList, distance = Cluster.kMeans(histogram, main.CLUSTERS)
data = Utils.prepareData(histogram)
labels = Utils.closestCentroidList(data, centroidList)
clf = svm.SVC(kernel='rbf')
clf.fit(data, labels)
return data, clf
def clusterDraw(data, centroidList, histogram):
fig = plt.figure()
ax = Axes3D(fig)
labels = Utils.closestCentroidList(data, centroidList)
data, labels = Utils.removeDuplicates(data, labels)
Xs, Ys, Zs, sizes, colors = Utils.getDrawInfo(data, histogram, labels)
scatter = ax.scatter(Xs, Ys, Zs, s=sizes, c=colors)
plt.show()
def drawSlice(data, centroidList):
fig = plt.figure()
ax = fig.add_subplot(111)
labels = Utils.closestCentroidList(data, centroidList)
clusterList = Utils.getClusters(data, labels)
clusterA = clusterList.pop(random.randrange(len(clusterList)))
clusterB = clusterList.pop(random.randrange(len(clusterList)))
colorA = Utils.getAverageColor(clusterA)
colorB = Utils.getAverageColor(clusterB)
Xs = []
Zs = []
for i in range(len(clusterA)):
Xs.append(clusterA[i][0])
Zs.append(clusterA[i][2])
scatter = ax.scatter(Xs, Zs, s=20, c="b")
Xs = []
Zs = []
for i in range(len(clusterB)):
Xs.append(clusterB[i][0])
Zs.append(clusterB[i][2])
scatter = ax.scatter(Xs, Zs, s=20, c="r")
plt.show()
