def classifier(self):
testFeature = np.zeros(20)
testFeature[0] = int(self.ui.comboBox_10.currentIndex())
testFeature[1] = int(self.ui.spinBox.text())
testFeature[2] = int(self.ui.comboBox_12.currentIndex())
testFeature[3] = int(self.ui.comboBox_9.currentIndex())
testFeature[4] = int(self.ui.spinBox_2.text())
testFeature[5] = int(self.ui.comboBox_11.currentIndex())
testFeature[6] = int(self.ui.comboBox_5.currentIndex())
testFeature[7] = float(self.ui.doubleSpinBox.text())
testFeature[8] = int(self.ui.comboBox_2.currentIndex())
testFeature[9] = int(self.ui.comboBox_13.currentIndex())
testFeature[10] = int(self.ui.spinBox_3.text())
testFeature[11] = int(self.ui.comboBox_8.currentIndex())
testFeature[12] = int(self.ui.spinBox_4.text())
testFeature[13] = int(self.ui.comboBox_14.currentIndex())
testFeature[14] = int(self.ui.comboBox_7.currentIndex())
testFeature[15] = int(self.ui.spinBox_5.text())
testFeature[16] = int(self.ui.comboBox_4.currentIndex())
testFeature[17] = int(self.ui.spinBox_6.text())
testFeature[18] = int(self.ui.comboBox_3.currentIndex())
testFeature[19] = int(self.ui.comboBox_6.currentIndex())
filePath = self.ui.fileButton.text()
trainFeature, trainLabel = read_GermanData20(filePath)
predictedLabel = decision_Tree(trainFeature, trainLabel, testFeature)
self.ui.textBrowser.setText(str(predictedLabel))
def classifier(self):
testFeature = np.zeros(20)
testFeature[0] = int(self.ui.comboBox_10.currentIndex())
testFeature[1] = int(self.ui.spinBox.text())
testFeature[2] = int(self.ui.comboBox_12.currentIndex())
testFeature[3] = int(self.ui.comboBox_9.currentIndex())
testFeature[4] = int(self.ui.spinBox_2.text())
testFeature[5] = int(self.ui.comboBox_11.currentIndex())
testFeature[6] = int(self.ui.comboBox_5.currentIndex())
testFeature[7] = float(self.ui.doubleSpinBox.text())
testFeature[8] = int(self.ui.comboBox_2.currentIndex())
testFeature[9] = int(self.ui.comboBox_13.currentIndex())
testFeature[10] = int(self.ui.spinBox_3.text())
testFeature[11] = int(self.ui.comboBox_8.currentIndex())
testFeature[12] = int(self.ui.spinBox_4.text())
testFeature[13] = int(self.ui.comboBox_14.currentIndex())
testFeature[14] = int(self.ui.comboBox_7.currentIndex())
testFeature[15] = int(self.ui.spinBox_5.text())
testFeature[16] = int(self.ui.comboBox_4.currentIndex())
testFeature[17] = int(self.ui.spinBox_6.text())
testFeature[18] = int(self.ui.comboBox_3.currentIndex())
testFeature[19] = int(self.ui.comboBox_6.currentIndex())
filePath = self.ui.fileButton.text()
trainFeature, trainLabel = read_GermanData20(filePath)
predictedLabel = decision_Tree(trainFeature, trainLabel, testFeature)
self.ui.textBrowser.setText(str(predictedLabel))
#print(np.array(negFeatureFolders).shape)
for i in range(folderNum):
subTrainFeature = negFeatureFolders[i]
subTrainFeature.extend(posFeature)
subTrainFeature = np.array(subTrainFeature)
subTrainLabel = list(np.zeros(posNum))
subTrainLabel.extend(list(np.ones(posNum)))
subTrainLabel = np.array(subTrainLabel)
print("=====%dst Bagging=====") % (i + 1)
print("Positive: %d, Negative: %d") % (list(subTrainLabel).count(1),
list(subTrainLabel).count(0))
#print(subTrainFeature.shape)
#print(subTrainLabel)
predictedLabel_temp1 = knn(subTrainFeature, subTrainLabel, testFeature,
5)
predictedLabel_temp2 = decision_Tree(subTrainFeature, subTrainLabel,
testFeature)
predictedLabel_temp3 = adboostDT(subTrainFeature, subTrainLabel,
testFeature)
predictedLabel_temp4 = RandomForest_Classifer(subTrainFeature,
subTrainLabel,
testFeature)
predictedLabel_temp5 = svmclassifier(subTrainFeature, subTrainLabel,
testFeature, 1.0, 0.015625)
predictedLabel_temp6 = logistic_regression(subTrainFeature,
subTrainLabel, testFeature)
predictedLabel_voting1.append(predictedLabel_temp1)
predictedLabel_voting2.append(predictedLabel_temp2)
predictedLabel_voting3.append(predictedLabel_temp3)
predictedLabel_voting4.append(predictedLabel_temp4)
predictedLabel_voting5.append(predictedLabel_temp5)
random.shuffle(sequence)
negFeatureFolders.append([negFeature[j] for j in sequence[:posNum]])
#print(np.array(negFeatureFolders).shape)
for i in range(folderNum):
subTrainFeature = negFeatureFolders[i]
subTrainFeature.extend(posFeature)
subTrainFeature = np.array(subTrainFeature)
subTrainLabel = list(np.zeros(posNum))
subTrainLabel.extend(list(np.ones(posNum)))
subTrainLabel = np.array(subTrainLabel)
print("=====%dst Bagging=====") % (i+1)
print("Positive: %d, Negative: %d") % (list(subTrainLabel).count(1), list(subTrainLabel).count(0))
#print(subTrainFeature.shape)
#print(subTrainLabel)
predictedLabel_temp1 = knn(subTrainFeature, subTrainLabel, testFeature, 5)
predictedLabel_temp2 = decision_Tree(subTrainFeature, subTrainLabel, testFeature)
predictedLabel_temp3 = adboostDT(subTrainFeature, subTrainLabel, testFeature)
predictedLabel_temp4 = RandomForest_Classifer(subTrainFeature, subTrainLabel, testFeature)
predictedLabel_temp5 = svmclassifier(subTrainFeature, subTrainLabel, testFeature, 1.0, 0.015625)
predictedLabel_temp6 = logistic_regression(subTrainFeature, subTrainLabel, testFeature)
predictedLabel_voting1.append(predictedLabel_temp1)
predictedLabel_voting2.append(predictedLabel_temp2)
predictedLabel_voting3.append(predictedLabel_temp3)
predictedLabel_voting4.append(predictedLabel_temp4)
predictedLabel_voting5.append(predictedLabel_temp5)
predictedLabel_voting6.append(predictedLabel_temp6)
print("KNN=====%dst predicted labels:") % (i+1)
print(predictedLabel_temp1)
print("DT=====%dst predicted labels:") % (i+1)
print(predictedLabel_temp2)