def join_cluster(self):
eval_cluster_bool = self.evaluateClusteringCheckBox.isChecked()
table_object = self.clusterInformationTable.selectedItems()
clusters_to_join = []
if table_object:
for i in range(0, len(table_object)):
temp_table_object = table_object[i].text()
if 'noise' in str(temp_table_object):
helper.error_message(self.error_dialog, 'Can not join noise cluster')
con = False
break
else:
if 'red only' in str(temp_table_object):
temp_table_object = self.data.red_only_cluster_idx
clusters_to_join.append(temp_table_object)
clusters_to_join[i] = int(clusters_to_join[i])
con = True
if con:
self.data.join_clusters_together(clusters_to_join, self.clusterOnData.currentIndex(),
evaluate_cluster=eval_cluster_bool)
self.giniCoeff.setText(str(self.data.gini))
self.shannonEntropy.setText(str(self.data.shannon))
view.update_cluster_table(self.clusterInformationTable, self.data.tab_cluster_data)
self.update_plots()
else:
helper.error_message(self.error_dialog, 'No clusters selected')
def split_cluster(self):
eval_cluster_bool = self.evaluateClusteringCheckBox.isChecked()
cluster_to_split = self.clusterInformationTable.selectedItems()
if cluster_to_split:
if len(cluster_to_split) > 1:
helper.error_message(self.error_dialog,
'Warning: This function can only split one cluster at '
'a time and chose the first selected cluster')
cluster_to_split = cluster_to_split[0].text()
if 'noise' in str(cluster_to_split):
helper.error_message(self.error_dialog, 'Can not split noise cluster')
else:
if 'red only' in str(cluster_to_split):
cluster_to_split = self.data.red_only_cluster_idx
cluster_to_split = int(cluster_to_split)
self.data.split_cluster_in_two(cluster_to_split, self.clusterOnData.currentIndex(),
evaluate_cluster=eval_cluster_bool)
self.giniCoeff.setText(str(self.data.gini))
self.shannonEntropy.setText(str(self.data.shannon))
view.update_cluster_table(self.clusterInformationTable, self.data.tab_cluster_data)
self.update_plots()
else:
helper.error_message(self.error_dialog, 'No cluster selected')
def evaluate_clusters(self):
eval_cluster_bool = self.evaluateClusteringCheckBox.isChecked()
if eval_cluster_bool:
data = self.data.get_data_to_cluster_on(self.clusterOnData.currentIndex())
self.data.evaluate_cluster_solution(data)
self.data.make_tabulated_cluster_data()
view.update_cluster_table(self.clusterInformationTable, self.data.tab_cluster_data)
def cluster_data(self):
# auto cluster the data
eval_cluster_bool = self.evaluateClusteringCheckBox.isChecked()
self.data.auto_cluster(self.clusterOnData.currentIndex(), int(self.clusterMinClusterSize.text()),
int(self.clusterMinSamples.text()), evaluate_cluster=eval_cluster_bool)
self.giniCoeff.setText(str(self.data.gini))
self.shannonEntropy.setText(str(self.data.shannon))
view.update_cluster_table(self.clusterInformationTable, self.data.tab_cluster_data)
# self.data.decision_graph(self.clusterOnData.currentIndex())
self.update_plots()