def myCluster(self, str_clu, str_iter, str_thres):
try:
self.right_tableview.itemChanged.disconnect(
self.item_Changed_Event) # 断开连接，不然会很费时间
except Exception:
pass
try:
if str_clu != '':
num_clu = int(str_clu)
else:
num_clu = 2
if str_iter != '':
num_iter = int(str_iter)
else:
num_iter = 500
if str_thres != '':
num_thres = float(str_thres)
else:
num_thres = 0.0001
except Exception:
QtWidgets.QMessageBox.critical(self, "错误", "输入错误")
return
kmeans = K_means.Kmeans(num_clu, num_iter, num_thres)
self.SelectedToDf()
if self.selectedDf is None:
return
if not self.selectedDf.empty:
if self.selectedDf.T.shape[1] < 2:
QtWidgets.QMessageBox.critical(self, "错误", "输入向量小于2维，无法聚类")
return
try:
print(self.selectedDf)
col = self.selectedDf.T.shape[1]
data_X = self.selectedDf.T.iloc[:, :col].values
predict_y = kmeans.predict(data_X)
self.right_tableview.insertColumn(
self.right_tableview.columnCount())
for i in range(len(predict_y)):
Input_item = QtWidgets.QTableWidgetItem(str(predict_y[i]))
Input_item.setTextAlignment(Qt.AlignHCenter
| Qt.AlignVCenter)
self.right_tableview.setItem(
i,
self.right_tableview.columnCount() - 1, Input_item)
temp_x = pd.DataFrame(data=data_X)
temp_y = pd.Series(data=predict_y)
temp_x["Predict"] = temp_y
if self.selectedDf.T.shape[1] == 2:
ax = self.ClusterWindow.plot_widget.add_subplot(111)
for i in range(num_clu):
ax.scatter(x=temp_x[temp_x.Predict == i].iloc[:, 0],
y=temp_x[temp_x.Predict == i].iloc[:, 1])
elif self.selectedDf.T.shape[1] >= 3:
ax = Axes3D(self.ClusterWindow.plot_widget)
for i in range(num_clu):
ax.scatter(temp_x[temp_x.Predict == i].iloc[:, 0],
temp_x[temp_x.Predict == i].iloc[:, 1],
temp_x[temp_x.Predict == i].iloc[:, 2])
except Exception:
QtWidgets.QMessageBox.critical(self, "错误", "聚类失败，请检查输入数据的合法性")
return
try:
self.item_Changed_Event()
self.right_tableview.itemChanged.connect(
self.item_Changed_Event) # 断开连接，不然会很费时间
except Exception:
pass
self.ClusterWindow.plot_canvas.draw()
if clust is None or len(clust) <= 1:
count1 += 1
else:
count2 += 1
return (count1 / len(clusters)) * 100
#===========Main========================
_data = Mini_Parse("stars.txt")
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# We create an object of the Kmeans class and we put the data in it
kmeans = km.Kmeans(_data)
#The input defines the number of centroids and it calculates them
means = kmeans.CalculateMeans(15)
#Based on the means or centroids it creates the clusters
clusters = kmeans.FindClusters()
hullx = []
hully = []
hullz = []
# Call for each cluster the Jarvis March Algorithm
for i in range(len(clusters)):
if (len(clusters[i])) <= 1:
continue
else:
temp = d3ch.JarvisMarch(clusters[i])
for idx in range(nP):
# The random value to be added to the gene.
for i in range(length):
random_value = np.random.uniform(0.0, 1.0)
cluster.__dict__['populasi'][i][idx][3] + random_value
return cluster
def getCentroidPop(i):
cluster.centroids = cluster.populasi[i]
k = 3
fitness = []
cluster = K_means.Kmeans('seeds.txt', k)
#------------------------- POPULATION, CHROMOSOME, CLUSTERING -------------------------#
createPopulation()
for i in range(0, 70):
getCentroidPop(i) # ngambil centroid d masing2 populasi
cluster.clustering()
centroid, SSE, acc = cluster.groupData()
result = centroid, SSE, acc
fitness.append(result)
# per chromosome->centroid punya fitness function
# print("SSE chromosome: %.2f" % SSE)
# print("Acc chromosome: %.2f" % acc)
#--------------------------------- SELECTION ----------------------------------#
